Next Previous Contents
There exists a variety of
models which employ computers in order to recognize emotions or to represent them.
These models are not "emotional computers" in a narrow sense, because
their "emotional" components are pre-defined elements and not a
subsystem which developed independently.
The models described in
this chapter are, to a large extent, rule-based production systems. Thus
they are also symbol-processing systems. From the sixties until today a
spirited discussion has taken place whether or to which extent the human mind
is a symbol-processing system and to what extent symbol-processing computer
models can be a realistic approximation to its real workings (see e.g.
Franklin, 1995).
A rule-based production
system has as minimum requirements a set of standard components:
1.
1.
a so-called knowledge
base that contains the processing rules of the system;
2.
2.
a so-called global
database that represents the main memory of the system;
3.
3.
a so-called control
structure which analyzes the contents of this global database
and decides which processing rules of the knowledge base are to be
applied.
A more detailed description
of a rule-based production system is supplied by Franklin (1995) with the
example of SOAR: The system operates within a defined problem space ;
the production process of the system is the use of appropriate condition-action
rules which transform the problem space from one state into another.
The models of Dyer,
Pfeiffer, Bates and Reilly, and Elliot presented here can be regarded as
rule-based production systems. Scherer's model forms an exception in as much as
it is an implementation which does not work rule-based. Its underlying approach
is, however, an appraisal theory and could easily be implemented as a
production system.
5.1.
The models of Dyer
Dyer has developed three
models in all: BORIS, OpEd and DAYDREAMER. BORIS and OpEd are systems which can
infer emotions from texts; DAYDREAMER is a computer model which can generate
emotions.
Dyer regards emotions as an
emergent phenomenon:
|
"Neither BORIS,
OpEd, nor DAYDREAMER were designed to address specifically the problem of emotion.
Rather, emotion comprehension and emotional reactions in these models arise
through the interaction of general cognitive processes of retrieval, planning
and reasoning over memory episodes, goals, and beliefs." |
|
(Dyer, 1987, p. 324) |
These "general
cognitive processes" are realized by Dyer in the form of demons,
specialized program subroutines which are activated under certain conditions
and can accomplish specific tasks independently from one another. After the
completion of their work these demons
"die" or spawn new subroutines.
|
"In BORIS,
"disappointed" caused several demons to be spawned. One demon used
syntactic knowledge to work out which character x was feeling the
disappointment. Another demon looked to see if x had suffered a recent goal
failure and if this was unexpected." |
|
(Dyer, 1987, p. 332) |
5.1.1. BORIS
BORIS is based on a
so-called affect lexicon which possesses six components: A person who feels
the emotion; the polarity of the emotion (positive - negative); one or more
goal attainment situations; the thing or the person toward which the emotion is
directed; the strength of the emotion as well as the respective expectation.
With these components,
emotions present themselves as follows in BORIS:
Emotion: relief
Person: x
Polarity: positive
Directed at: -/-
Goal attainment: goal
attained
Expectation: Expectation
not fulfilled
In this case the person x
did not expect to achieve her goal. This expectation did not fulfill itself.
Now the person x experiences a positive excitation state felt by her as relief
(after Dyer, 1987, p. 325).
In similar form emotions as
happy, sad, grateful, angry-at , hopefu , fearful,
disappointed, guilty etc. are represented in BORIS.
This underlines the goal
Dyer pursues with BORIS: All emotions can be represented in BORIS in form of a
negative or positive excitation condition, connected with information about the
goals and expectations of a person.
Dyer points out that with
the help of the variables specified by him one can also represent emotions for
which there is no appropriate word in a given language.
With the help of this model
BORIS can draw conclusions about the respective goal attainment situation of a
person, understand and generate text that contains descriptions of emotions as
well as understad and compare the meanings of emotional terms. The system is
also able to represent multiple emotional states.
From the excuted goal/plan
analysis of a person and the result BORIS can also develop expectations how
this person will continue to behave in order to achieve her goals. Also the
strength of an excitation state can be used by BORIS for such predictions.
OpEd represents an extension
of BORIS. While BORIS can, due to its internal encyclopedia, only understands
emotions in narrative texts, OpEd is able to infer emotions and beliefs also
from texts which are not narrative:
|
"OpEd is...designed
to read and answer questions about editorial text. OpEd explicitly tracks the
beliefs of the editorial writer and builds representations of the beliefs of
the writer and of those beliefs the writer ascribes to his opponents." |
|
(Dyer, 1987, p. 329) |
Beliefs are implemented with OpEd on the
basis of four dimensions: Believer is someone who possesses a certain
belief; content is an evaluation of goals and plans; attack are
the beliefs which oppose the one currently expressed; support are the
beliefs which support the current belief.
According to Dyer, beliefs
were an substantial element which was missing in BORIS. For example, the
statement "happy(x)" is represented in BORIS as the attainment of a
goal by x. This, Dyer notes, is not sufficient:
|
"What should have
been represented is that happy(x) implies that x believes that x has
achieved (or will achieve, or has a chance of achieving) a goal of x." |
|
(Dyer, 1987, p. 330) |
Therefore new demons
are added in OpEd to the ones known from BORIS: belief-building,
affect-related demons.
Dyer has shown that OpEd is
able not only to deduce from newspaper texts the beliefs of the author but also
to draw conclusions about the beliefs of those against which the author takes
position.
5.1.3.
DAYDREAMER
While BORIS and OpEd are
meant to understand emotions, DAYDREAMER (Mueller and Dyer, 1985) is an attempt
to develop a system that "feels" them. This feeling expresses itself
not in a subjective state of the system, but in that its respective "
emotional" condition affects its internal behaviour during the processing
of information.
Mueller and Dyer define
four substantial functions of daydreams: They increase the effectiveness of
future behaviour by the anticipation of possible reactions to expected events;
they support learning from successes and errors by thinking through alternative
courses of action to the end; they support creativity, because the imaginary
following through of courses of action can lead to new solutions, and they
support the adjustment of emotions by reducing their felt intensity.
In order to achieve these
goals, DAYDREAMER is equipped with the following main components:
1. a scenario generator which
consists of a planner and so-called relaxation rules;
2. a dynamic episodic memory, whose
contents are used by the scenario
generator;
3. an accumulation of personal goals
and control goals which steer the scenario generator;
4. an emotion component, in
which daydreams are generated or initiated by emotional states which are
elicited by reaching or not reaching a goal;
5. a knowledge (domain knowledge)
about interpersonal relations and everyday life activities.
DAYDREAMER has two kinds of
functions, daydreaming mode and performance mode. In daydreaming
mode the system stays continually in daydreams until it is interrupted; in
performance mode the system shows what it has learned from the
daydreams.
Mueller and Dyer postulate
a set of goals possessed by a system and which they call control goals. These
are released partially by emotions and release again daydreams. The function of
the control goals consists of providing at short notice for a modification of
emotional states and securing the reaching of personal goals on a long-term
basis.
The system has thus a
feedback mechanism in which emotions release daydreams and daydreams modify
these emotions and release new emotions which again initiate new
daydreams.
Mueller and Dyer name four
control goals which arise with daydreams:
1. Rationalization: The goal of rationalizing
away a failure and of reducing in this way a negative emotional state.
2. Revenge: The goal of preventing for another
the reaching of a goal and thus to reduce the own annoyance.
3. Reversal of success or failure: The goal of imagining a scenario
with an opposite result in order to turn around the polarity of an emotional
state.
4. Preparation:
The goal of developing hypothetical episodes in order to play through the
consequences of a possible action.
Mueller and Dyer describe
the functioning of DAYDREAMER by an example in which DAYDREAMER represents an
active young man with social goals who met an actress who rejected his
invitation to a drink.
DAYDREAMER generates
thereupon the following two daydreams:
|
"Daydream 1:
I am disappointed that she didn't accept my offer...I imagine that she accepted
my offer and we soon become a pair. I help her when she has to rehearse her
lines...When she has to do a film in France, I drop my work and travel there
with her...I begin to miss my work. I become unhappy and feel unfulfilled.
She loses interest in me, because I have nothing to offer her. It's good I
didn't get involved with her, because it would've led to disaster. I feel
less disappointed that she didn't accept my offer. |
|
(......) |
|
Daydream 2: I'm angry that she didn't accept
my offer to go have a drink. I imagine I pursue an acting career and become a
star even more famous than she is. She remembers meeting me a long time ago
in a movie theater and calls me up...I go out with her, but now she has to
compete with many other women for my attention. I eventually dump her." |
|
(Dyer, 1987, p. 337) |
The first daydream is an
example of reversal: he pretends that
the rendezvous took place and develops a fantasy over the consequences. The reality monitor announces that an important
goal, i.e. the own career, is neglected.
The result is a rationalization which reduces the negative emotional
condition.
Daydream 2 is released by
the emotional condition of annoyance and embodies revenge to reduce the the
negative effect of the current emotional condition.
As soon as a control goal
is activated, the scenario generator generates a set of events which
are connected with the control goal.
These daydreams differ in as much from classical plans as they are not
exclusively directed at a goal, but can change in a loose, associative
manner. The system contains, in
addition, a relaxation mechanism which makes possible daydreams which are out
of touch with reality.
Mueller and Dyer cite four
examples of such relaxations in their model:
|
|
Behavior of others: DAYDREAMER can assume that the
film star accepts his offer. |
|
|
Self attributes: DAYDREAMER can assume to be a
professional athleteor a well-known film star. |
|
|
Physical constraints: One can assume to be invisible
or to fly. |
|
|
Social constraints: One can assume to provoke a
scene in a distinguished restaurant. |
The strength of the
relaxations is not always the same; it
varies after the respective active control goals.
Positive emotions arise
through the memory of a goal reaching, negative emotions through the memory of
a failure. If another is responsible
for the non reaching of a goal of DAYDREAMER, the emotion Anger is
released. Imaginary successes imagined in the daydream call up positive
emotions awake; imaginary failures
negative emotions.
During its daydreams
DAYDREAMER stores in its memory complete daydreams, future plans and planning
strategies. These are indexed in the
episodic memory and can be called up later.
Thus the system is able to learn from its daydreams for future
situations.
The ability of a computer
to develop daydreams is substantial for the development of its intelligence,
Mueller and Dyer maintain. They imagine
computers which, in the time in which they are not used, can daydream in order
to increase their efficiency in this way.
The model of Mueller and
Dyer has not been developed further after its original conception
5.2.
The model of Pfeifer
With FEELER
("Framework for Evaluation of Events and Linkages into Emotional
Responses") , Pfeifer (1982, 1988) presented a model of an emotional
computer system which is based explicitly on psychological emotion theories.
Pfeifer's model is a
rule-based system with working memory (WM), rule memory (long term memory- LTM)
and control structure; the contents of
the long term storage (the knowledge base) he additionally differentiates into declarative
and procedural knowledge.
In order to be able to
represent emotions, Pfeifer extends this structure of a rule-based system by
further subsystems. Thus FEELER has not
only a cognitive, but additionally a physiological working memory.
To develop emotions, FEELER
needs a schema in order to analyze the cognitive conditions which lead to an
emotion. For this purpose Pfeifer makes
use of the taxonomy developed by Weiner (1982). From this he develops exemplarily a rule for the emergence of an
emotion:
|
"IF current_state is
negative for self |
|
and emotional_target is
VARperson |
|
and locus_of_causality is
VARperson |
|
and locus_of_control is
VARperson |
|
THEN ANGER at
VARperson" |
|
(Pfeifer, 1988, p. 292) |
So that this rule can
become effective, all its conditions must be represented in the WM. This is done via inference processes which
place their results in the WM. Such inference
processes are, according to Pfeifer, typically released by interrupts.
Appropriate interrupts are
generated by FEELER if expectations are hurt regarding the reaching of subgoals
and/or if for an event no expectations exist.
In a second rule Pfeifer
defines an action tendency following rule 1:
|
IF angry |
|
and emotional_target is
VARperson |
|
and int_pers_rel self -
VARperson is negative |
|
THEN generate goal to
harm VARperson |
|
(Pfeifer, 1988, p. 297) |
This rule shows at the same
time, according to Pfeifer, the heuristic value of an emotion: the emotion reduces the circle of the
possible candidates and actions for inference processes.
Pfeifer grants that such a model is
not able to cover all emotional states.
He discusses a number of problems, for example the interaction of
different subsystems and their influence on the development, duration and
fading away of emotions. In a
further step Pfeifer supplemented its model with the taxonomy of Roseman
(1979), in order to be able to represent emotions in FEELER in connection with
the reaching of goals.
5.3.
The model of Bates and Reilly
In his essay "The Role
of Emotion in Believable Agents" (Bates, 1994) Joseph Bates quotes the
Disney artist Chuck Jones with the statement, Disney would, with his cartoon
characters, always strive for believability. Bates continues:
|
"Emotion is one of
the primary means to achieve this believability, this illusion of life,
because it helps us know that characters really care about what happens in
the world, that they truly have desires." |
|
(Bates, 1994, p. 6) |
Together with a group of
colleagues at Carnegie-Mellon University Bates created the Oz
Project . Their goal is to build
synthetic creatures which appear to their human public as genuinly lifelike as
possible. Briefly, it concerns an interactive
drama system or "artistically effective simulated worlds" (Bates et.
al., 1992, p.1).
The fundamental approach
consists in the creation of broad and shallow agents . While computer models of AI and of emotions
concentrate on specific aspects and try to cover these as detailed as possible,
Bates takes the opposite approach:
|
"...part of our
effort is aimed at producing agents with a broad set of capabilities,
including goal-directed reactive behavior, emotional state and behavior,
social knowledge and behavior, and some natural language abilities. For our
purpose, each of these capacities can be as limited as is necessary to allow
us to build broad, integrated agents..." |
|
(Bates et. al., 1992a, p.1) |
The broad approach is, so Bates,
necessary in order to create believable artificial characters. Only an agent that is able to react
convincingly to a variety of situations in an environment to which a human user
belongs, is also really accepted by the latter as a believable character.
Since Oz is
intentionally constructed as an artificial worl which is to be regarded by the
user like a film or a play, it is sufficient to construct the various abilities
of the system "flat" in order to satisfy expectations of the user. Because, as in the cinema, he does not
expect a correct picture of reality, but an artificial world with in this
context convincing participants.
An Oz -world
consists of four substantial elements:
A simulated environment, a number of agents who populate this artificial
world, an interface through which humans can participate at the happenings in
this world, and a planner that is concerned with the long-term structure of the
experiences of a user.
The agent architecture of
Bates is called Tok and consists of a set of components: There are modules for goals and behaviour,
for sensory perception, language analysis and language production. And there is a module called Em for
emotions and social relations.
Fig. 4: Structure of the TOK architecture
(Reilly, 1996, p. 14)
Em contains an emotion system which is
based in the model of Ortony, Clore and Collins (1988). However, the OCC model is not implemented in
its entire complexity in Em .
This concerns in particular the intensity variables postulated by
Ortony, Clore and Collins and their complex interactions. Em uses a simpler subset of these
variables which is judged as sufficient for the the intended purpose.
Reilly (1996) explains that
with the use of such subsets the OCC model is in effect not redudce but
extended. He clarifies this with two
examples:
With Ortony, Clore and
Collins pity is generated as follows:
Agent A feels pity for agent B, if agent A likes agent B and agent A
appraisesan event as unpleasantly for agent B regarding his goals. "So, if Alice hears that Bill got a
demotion, Alice must be able to match this event with a model of Bill's goals,
including goals about demotions."
(Reilly, 1996, p. 53) This would mean that Alice would have to possess a
relatively comprehensive knowledge of Bill's goals and appraisal mechanisms -
according to Reilly a difficult venture in a dynamic world, in which goals can
change fast. I
He suggests the following
mechanism instead: Agent A feels pity for Agent B, if Agent A likes Agent B and
Agent A believes that Agent B is unhappy. According to Reilly, this description
has other advantages than just being simpler:
|
"In this case, I have
broken the OCC model into two components: recognizing sadness in others and
having a sympathetic emotional response..... Recognizing sadness in others is
done, according to the OCC model, only through reasoning and modeling of the
goals of other agents, so this inference can be built into the model of how
the emotion is generated. Em keeps the recognition of sadness apart from the
emotional response, which allows for multiple ways of coming to know about
the emotions of others. One way is to do reasoning and modeling, but another
way, for example, is to see that an agent is crying. |
|
The Em model is more
complete than the OCC model in cases such as agent A seeing that agent B is
sad but not knowing why. In the OCC case, when agent A does not know why
agent B is unhappy, the criteria for pity is not met. Because the default Em
emotions generators require only that agent A believe that agent B is
unhappy, which can be perceived in this case, Em generates pity." |
|
(Reilly, 1996, p. 53f.) |
As the second example,
Reilly (1996) states the emergence of distress . In the OCC model distress develops if an
event is appraised as unpleasant regarding the goals of an agent. That means that external events must be
evaluated. With Em, distress
is caused by the fact that goals are either not achieved or the probability
rises that they are not reached, which is connected with the motivation and
action system. Reilly explains:
|
"This shifts the emphasis
towards the goal processing of the agent and away from the cognitive
appraisal of external events. This is useful for two reasons. First, the
motivation system is already doing much of the processing (e.g., determining
goal successes and failures), so doing it in the emotion system as well is
redundant. Second, much of this processing is easier to do in the motivation
system since that's where the relevant information is. For instance, deciding
how likely a goal is to fail might depend on how far the behavior to achieve
that goal has progressed or how many alternate ways to achieve the goal are
available - this information is already in the motivation system." |
|
(Reilly, 1996, p. 54f.) |
In this way emotion
structures are to develop which can be used more completely and more simply
than the purely cognitive models. Which
emotions can be generated by Em on which basis shows the
following table:
|
Emotion Type |
Cause in Default Em System |
|
Distress |
Goal fails or becomes more likely to
fail and it is important to the agent that the goal not fail. |
|
Joy |
Goal succeeds or becomes more
likely to succeed and it is important to the agent that the goal succeed. |
|
Fear |
Agent believes a goal is likely to
fail and it is important to the agent that the goal not fail. |
|
Hope |
Agent believes a goal is likely to
succeed and it is important to the agent that the goal succeed. |
|
Satisfaction |
A goal succeeds that the agent
hoped would succeed. |
|
Fears-Confirmed |
A goal failed that the agent
feared would fail. |
|
Disappointment |
A goal failed that the agent hoped
would succeed. |
|
Relief |
A goal succeeds that the agent
feared would fail. |
|
Happy-For |
A liked other agent is happy. |
|
Pity |
A liked other agent is sad. |
|
Gloating |
A disliked other agent is sad. |
|
Resentment |
A disliked other agent is happy. |
|
Like |
Agent is near or thinking about a
liked object or agent. |
|
Dislike |
Agent is near or thinking about a disliked
object or agent. |
|
Other attitude-based emotions |
Agent is near or thinking about an
object or agent that the agent has an attitude towards (e.g., awe). |
|
Pride |
Agent performs an action that
meets a standard of behavior. |
|
Shame |
Agent performs an action that
breaks a standard of behavior. |
|
Admiration |
Another agent performs an action
that meets a standard of behavior. |
|
Reproach |
Another agent performs an action
that breaks a standard of behavior. |
|
Anger |
Another agent is responsible for a
goal failing or becoming more likely to fail and it is important that the
goal not fail. |
|
Remorse |
An agent is responsible for one of
his own goals failing or becoming more likely to fail and it is important to the
agent that the goal not fail. |
|
Gratitude |
Another agent is responsible for a
goal succeeding or becoming more likely to succeed and it is important that
the goal succeed. |
|
Gratification |
An agent is responsible for one of
his own goals succeeding or becoming more likely to succeed and it is
important to the agent that the goal succeed. |
|
Frustration |
A plan or behavior of the agent
fails. |
|
Startle |
A loud noise is heard. |
Table 3: Emotion types and their generation
in Em (after Reilly, 1996, p. 58 f.)
Reilly points out expressly
that these emotion types do not pretend to be correct in the psychological
sense but only represent a starting point in order to create believable
emotional agents.
The emotion types of Em
are arranged in the following hierarchy:
|
Total |
Positive |
Joy |
|
|
Hope |
|||
|
Happy-For |
|||
|
Gloating |
|||
|
Love |
|||
|
Satisfaction |
|||
|
Relief |
|||
|
Pride |
|||
|
Admiration |
|||
|
Gratitude |
|||
|
Gratification |
|||
|
|
|
|
|
|
Negative |
Distress |
|
|
|
Fear |
Startle |
||
|
Pity |
|
||
|
Resentment |
|||
|
Hate |
|||
|
Disappointment |
|||
|
Fears-Confirmed |
|||
|
Shame |
|||
|
Reproach |
|||
|
Anger |
Frustration |
||
|
Remorse |
|
Table 4: Hierarchy of emotion types in Em
(after Reilly, 1996, p. 76)
One notices that in this
hierarchy the emotion types modelled after the OCC model are arranged one level
below the level of "positive - negative". This mood level lends to Em the
possibility of specifying the general mood situation of an agent well before a
deep-going analysis, which simplifies the production of emotional effects
substantially.
For the determination of
the general mood situation (good-mood vs. bad-mood), Em
first sums up the intensities of the positive emotions and then those of the
negative emotions. Formalized, this
looks as follows:
IF Ip
> In
THEN set good-mood =
Ip
AND set bad-mood = 0
ELSE set good-mood = 0
AND set bad-mood = - In
(after Picard, 1997, p.
202)
The TOK system has been
realized with different characters. One
of the most well-known is Lyotard, a virtual cat. Bates et al. (1992b) describe a typical
interaction with Lyotard:
|
"As the trace
begins, Lyotard is engaged in exploration behavior in an attempt to satisfy a
goal to amuse himself... This behavior leads Lyotard to look around the room,
jump on a potted plant, nibble the plant, etc. After suffcient exploration,
Lyotard's goal is satisfied. This success is passed on to Em which makes
Lyotard mildly happy. The happy emotion leads to the "content"
feature being set. Hap then notices this feature being active and decides to
pursue a behavior to find a comfortable place to sit, again to satisfy the
high-level amusement goal. This behavior consists of going to a bedroom,
jumping onto a chair, sitting down, and licking himself for a while. |
|
At this point, a human
user whom Lyotard dislikes walks into the room. The dislike attitude, part of
the human-cat social relationship in Em, gives rise to an emotion of mild
hate toward the user. Further, Em notices that some of Lyotard's goals, such
as not-being-hurt, are threatened by the disliked user's proximity. This
prospect of a goal failure generates fear in Lyotard. The fear and hate
combine to generate a strong "aggressive" feature and diminish the
previous "content" feature. |
|
In this case, Hap also
has access to the fear emotion itself to determine why Lyotard is feeling
aggressive. All this combines in Hap to give rise to an avoid-harm goal and
its subsidiary escape/run-away behavior that leads Lyotard to jump off the
chair and run out of the room." |
|
(Bates et al., 1992b, p. 7) |
Reilly (1996) examined the
believability of a virtual character equipped with Em. Test subjects were confronted with two
virtual worlds in which two virtual characters acted. The difference between the two worlds consisted of the fact that
in one case both characters were equipped with Em, while in the second
case only one character contained it.
Afterwards it was explored
with a questionnaire which differences were noticed by the test subjects
between the Em-character ("Melvin") and the Non-Em-character
("Chuckie").
The test subjects
classified Melvin as more emotional than Chuckie. Also its believabilitywas more highly evaluated than the
Chuckies. At the same time the test
subjects indicated that Melvins personality was more outlined than Chuckie's
and that with Melvin they had less frequently the feeling that they had to
do with fictitious characters than with Chuckie.
The significance of the
results varies clearly, however, so that Reilly grants that Em is
only "moderately successful" (Reilly, 1996, p. 129).
5.4.
The model of Elliott
A further model which
is based on the theory of Ortony, Clore and Collins is the Affective
Reasoner of Clark Elliott.
Elliott's interest is primarily the role of emotions in social
interactions, be it between humans, between humans and computers, or between
virtual participants in a virtual computer world.
Elliott summarizes the core
elements of the Affective Reasoner in such a way:
|
"One way to explore
emotion reasoning is by simulating a world and populating it with agents
capable of participating in emotional episodes. This is the approach we have
taken. For this to be useful we must have (1) a simulated world which is rich
enough to test the many subtle variations a treatment of emotion reasoning
requires, (2) agents capable of (a) a wide range of affective states, (b) an
interesting array of interpretations of situations leading to those states
and (c) a reasonable set of reactions to those states, (3) a way to capture a
theory of emotions, and (4) a way for agents to interact and to reason about
the affective states of one another. The Affective Reasoner supports these
requirements." |
|
(Elliott, 1992, p. 2) |
The advantages of such a model
are, according to Elliott, numerous: On
the one hand it makes possible to examine psychological theories about the
emergence of emotions and the actions resulting from it for its internal
plausibility. Secodly, affective
modules are an important component of distributed agent systems, if these are
to act without friction losses in real time.
Thirdly, a computer model which can understand and express emotions is a
substantial step for the building of better man-machine interfaces.
As example of a simulated
world, Elliott (1992) chose Taxiworld, a scenario with four taxi
drivers in Chicago. (Taxiworld
is not limited to four drivers; the
simulation was implemented with up to 40 drivers.) There are different stops , different passengers, policemen, and
different travel goals. Thus can be
created a number of situations which lead to the development
of emotions.
The taxi drivers must be
able to interpret these situations in such a way that emotions can
develop. For this, they need the
ability to be able to reflect over the emotions of other taxi drivers. Finally, the drivers should be able to act
based on their emotions.
Elliott illustrates the
difference between the Affective Reasoner and classical analysis
models of AI by the following example (Elliott, 1992): "Toms car did not start, and Tom
therefore missed an appointment. He
insulted his car. Harry observed this
incident."
A classical AI system would
draw the following conclusions from this story: Tom should let his car be repaired. Harry has learned that Tom's car is defective. Tom could not come to his appointment in
time without his car. Harry suggests
that in the future Tom should set out earlier to his appointments.
The Affective Reasoner,
however, would come to completely different conclusions: Tom holds his car responsible for his missed
appointment. Tom is angry. Harry cannot understand, why Tom is angry with
his car, since one cannot hold a car responsible. Harry advises Tom to calm down.
Harry has pity with his friend Tom, because he is so excited.
In order to react in this
way, the Affective Reasoner needs a relatively large number of
components. Although it is specialized
in emotions, Elliott calls it nevertheless a "shallow model"
(Elliott, 1994). In the following
section the substantial components of the Affective Reasoner will be
presented, as described by Elliott (1992).
5.4.1.
The construction of the agents
The agents of the Affective
Reasoner have a rudimentary personality.
This personality consists of two components: the interpretive personality component represents the
individual disposition of an agent to interpret situations in its world. The manifestative personality component
is its individual way of showing its emotions.
Each agent has one or more
goals. With this are meant situations
whose occurrence the agent judges as desirable. In order to be able to act emotional, the agents need an object
domain within which situations occur which can lead to emotions and within
which the agents can execute actions elicited by emotions.
Each agent needs several
data bases for its functioning, to which he must have access at any time:
1. A data base with 24
emotion types which essentially correspond to the emotion types of Ortony,
Clore and Collins (1988) and were extended by Elliott by the two types love
and hate. Special emotion
eliciting conditions (ECC) are assigned to each of these emotion types.
2. A data base with goals, standards
and preferences. These GSPs constitute the concern structure of
an agent and define at the same time its interpretive personality component.
3. A data base with assumed
GSPs for other agents of its world.
Elliott calls it COO data base (Concerns-of-Others). Since these are data acquired by the
agent, they are mostly imperfect and can contain also wrong assumptions.
4. A data base with
reaction patterns which, depending upon type of emotion, are divided into up to
twenty different groups.
5.4.2.
Generating emotions
The patterns stored in the
GSP and COO data bases are compared by the agent with the EECs in its
world, and with correspondences a group of connections develops. Some these connections represent two or more
values for a class which Elliott calls emotion eliciting condition
relation (EEC relation).
EEC relations are composed from elements of the
emotion eliciting situation and their interpretation by the agent. Taken ogether, the condition for the call of
an emotion can develop inthis way:
|
self |
other |
desire-
self |
desire-
other |
pleas-
ingness |
status |
evalua-
tion |
respon-
sible agent |
appeal-
ingness |
|
(*) |
(*) |
(d/u) |
(d/t) |
(p/d) |
(u/c/d) |
(p/b) |
(*) |
(a/u) |
|
Key
to attribute values |
|
|
abbreviation |
meaning |
|
* |
some
agent's name |
|
d/u |
desirable
or undesirable (event) |
|
p/d |
pleased
or displeased about another's fortunes (event) |
|
p/b |
praiseworthy
or blameworthy (act) |
|
a/u |
appealing
or unappealing (object) |
|
u/c/d |
unconfirmed,
confirmed or disconfirmed |
Table 5: EEC relations of the Affective
Reasoner (after Elliott, 1992, p. 37)
Once one ore more EEC
relations are formed, these are used in order to generate emotions. In this phase arises a number of problems
which are discussed in detail by Elliott because they were not sufficiently
considered in the theory of Ortony, Clore and Collins.
As example Elliott cites a
compound emotion. The Affective
Reasoner constructs the EEC relations for the two underlying
emotions and summarizes them afterwards in a new EEC relation. The constituent emotions are thus replaced
by the compound emotion. Elliott does
not regard this as an optimal solution:
|
"Does anger really
subsume distress? Do compound emotions always subsume their constituent
emotions? That is, in feeling anger does a person also feel distress and
reproach? This is a diffcult question. Unfortunately, since we are
implementing a platform that generates discrete instances of emotions, we
cannot finesse this issue. Either they do or they do not. There can be no
middle ground until the eliciting condition theory is extended, and the EEC
relations extended." |
|
(Elliott, 1992, p. 42) |
This proceeding may
function with qualitatively similar emotions (Elliott cites as examples distress
and anger), but a problem emerges with several emotions arising
at the same time, in particular if they contradict each other.
With several instances of
the same emotion the solution is still quite simple. If an agent has, for example, two goals while playing cards
("to win "and "earn money"), its winning releases twice the
emotion happy. The Affective
Reasoner then simply generates two instances of the same emotion.
The situation is more
problematic with contradicting emotions.
Elliott grants that the OCC model exhibits gaps in this respect and
explains: "Except for the
superficial treatment of conflicting expressions of emotions, the development
and implementation of a theory of the expression of multiple emotions is beyond
the scope of this work." (Elliott,
1992, p. 44f.) The Affective
Reasoner therefore shifts the "solution" of this problem to its
action production module (see below).
5.4.3.
Generating actions
As soon as an emotional
stae for an agent has been generated, an action resulting from it is
initiated. The Affective Reasoner
uses for this an emotion manifestation lexicon which has three
dimensions: The 24 emotion types, the about
twenty reaction types (emotion manifestation categories) as well as an
intensity hierarchy of the possible reactions (which were not implemented in
the first model of the Affective Reasoner).
The reaction types of the Affective
Reasoner are based on a list by Gilboa and Ortony (unpublished). These are hierarchically organized; furthermore, each hierarchic level is
arranged along a continuum from spontaneous to planned reactions. As example Elliott cites the action
categories for "gloating":
|
Sponta- neous |
Non goal-directed |
Expressive |
Somatic |
flush, tremble, quiet pleasure |
|
Gloating |
Behavioral (towards inanimate) |
slap |
||
|
Behavioral (towards animate) |
smile, grin, laugh |
|||
|
Communicative (non verbal) |
superior smile, throw arms up in
air |
|||
|
Communicative (verbal) |
crow, inform-victim |
|||
|
Information Processing |
Evaluative self- directed attributions
of... |
superiority, intelligence,
prowess, invincibility |
||
|
Evaluative agent- directed attributions
of.... |
silliness, vulnerability,
inferiority |
|||
|
Obsessive Atten- tional focus on... |
other agent's blocked goal |
|||
|
Goal directed |
Affect-oriented Emotion regulation and
modulation |
Repression |
deny positive valence |
|
|
Reciprocal |
"rub-it-in" |
|||
|
Suppression |
show compassion |
|||
|
Distraction |
focus on other events |
|||
|
Reappraisal of self as.... |
winner |
|||
|
Reappraisal of situation as... |
modifiable, insignificant |
|||
|
Other-directed emotion modulation |
induce embarrassment, induce fear,
induce sympathy for future, induce others to experience joy at victim's
expense |
|||
|
Plan-oriented |
Situated plan-initiation |
call attention to the event |
||
|
Planned |
Full plan-initiation |
plan for recurrence of event |
Table 6: Reaction types of the Affective
Reasoner for "gloating" (after Elliott, 1992, p. 97)
For each agent, individual
categories can be activated or deactivated before the start of the
simulation. This specific pattern of
active and inactive categories constitutes the individual manifestative
personality of an agent. Elliott
calls the activated categories the potential temperament traits of an
agent.
In order to avoid conflicts
between contradicting emotions and concomitantly contradicting actions, the
action module contains so-called action exclusion sets. They are formed by classifying
the possible reactions into equivalence classes. A member of one of these classes can never emerge together with a
member of another class in the resulting action set.
5.4.4.
Interpreting the emotions of other agents
An agent receives its
knowledge over emotions of other agents not only through pre-programmed
characteristics, but also by observing other agents within the simulation and
drawing conclusions from these observations.
These flow then into its COO data base.
In order to integrate this learning process into the Affective
Reasoner, Elliott uses a program named Protos (Bareiss,
1989).
An agent observes the
emotional reaction of another agent.
Protos permits the agent then to draw conclusions about the emotion the
other agent feels and thus to demonstrate empathy.
First of all the observed
emotional reaction is compared with a data base of emotional reactions, in
order to define the underlying emotion.
Then the observed event is filtered through the COO data base for the
observed agent in order to determine whether this reaction is already
registered. If this is the case, it can
be assumed that the data base contains a correct representation of the
emotion-eliciting situation. On this
basis the observing agent can then develop an explanation for the behaviour of
the observed agent.
If the representation in
the COO data base should not agree with the observed behaviour, it is removed
from the data base and the data base is scanned again. If no correct representation should be
found, the agent can fall back to default values which are then integrated into
the COO data base.
Since COOs are nothing else
than assumed GSPs for another agent, the Affective Reasoner
is, with the help of so-called satellite COOs, able to
represent beliefs of an agent about the assumptions of another agent.
5.4.5.
The development of the model
The model described so far
in its essence was presented in this form by Elliott in his thesis in
1992. In the following years he
developed the Affective Reasoner in a number of areas.
Thus a component which
determines the intensity of emotions was missing to the original model. In a further work, Elliott (Elliott and
Siegle, 1993) developed a group of emotion intensity variables
based on the work of Ortony, Clore and Collins and Frijda.
The intensity variables are
classified by Elliott into three categories.
To each variable limit values are assigned within which they can
move (partially bipolar). Most
intensities can take on a value between 0 and 10. Weaker modifiers can take on values between 0 and
3; modifier which only reduce an intensity only values between 0 and
1. Variables whose effects on the
intensity computations are determinde by the valence of an emotion (a variable
which increases the intensity of a negatively valenced emotion, but reduces the
intensity of a positively valenced emotion for example), can take
on values between 1 and 3 and received additionally a bias value
which specifies the direction. In the
following the intensity variables and their value scopes are specified:
1. simulation-event variables are variables whose values change
independently of the interpretation mechanisms of the agents (goal
realization/blockage: -10 to +10, blameworthiness-praiseworthiness:
-10 to +10, appealingness: 0 to10, repulsiveness: -10 to 0, certainty:
0 to 1, sense-of-reality: 0 to 1, temporal proximity: 0 to 1, surprisingness:
1 to 3, effort: 0 to 3, deservingness: 1 to 3);
2. stable disposition variables have to do with the interpretation
of a situation by an agent, are relatively constant and constitute the
personality of an agent (importance to agent of achieving goal: 0 to 10,
importance to agent of not having goal blocked: 0 to 10, importance
to agent of having standard upheld: 0 to 10, importance to agent of not
having standard violated: 0 to 10, influence of preference on agent:
0 to 10, friendship-animosity: 0 to 3, emotional interrelatedness of
agents: 0 to 3);
3. mood-relevant variables are volatile, change for an agent
the interpretation of a situation, can be the result of previous affective
experiences and return to their default values after a certain time ( arousal:
0.1 to 3, physical well-being: 0.1 to 3, valence bias: 1 to 3, depression-ecstasy:
1 to 3, anxiety-invincibility: 1 to 3, importance of all Goals,
Standards, and Preferences: 0.3 to 3, liability-creditableness: 1 to
3).
Elliott
(Elliott and Siegle, 1993) reports that an analysis of emotional episodes with
the help of this variables led to the result that within the context
of the model all emotions can be represented and recognized.
In a
further step Elliott (Elliott and Carlino, 1994) extended the Affective
Reasoner by a speech recognition module.
The system was presented with sentences with emotion words, intensity
modifiers and pronomial references at third parties ("I am a bit sad because he....") presented. In the first run 188 out of 198 emotion
words were recognized. In a second
experiment the sentence "Hello Sam, I want to talk to you" was
presented to the system with seven different emotional different
intonations (anger, hatred, sadness, love, joy, fear, neutral). After some training, the Affective
Reasoner delivered a hundred percent correct identification of the
underlying emotion category.
In a further step the Affective
Reasoner received a module with which it can represent emotion types
as face expressions of a cartoon face (Elliott, Yang and Nerheim-Wolfe,
1993). The representational abilities cover the 24 emotion types in three
intensity stages each, which can be represented by one of seven schematic
faces. The faces were fed into a
morphing module which is able to produce rudimentary lip movements and change
fluently from one facial expression to the next . In addition, the Affective Reasoner was equipped with a
speech output module and the ability to select and play different music from an
extensive data base depending upon emotion .
The ability of the system
to represent emotions correctly was examined by Elliott (1997a) in an
experiment in which 141 test subjects participated. The test subjects were shown videos on which either an actor or
the faces of the Affective Reasoner spoke a sentence which, depending
upon intonation and face expression, could possess different meanings. The actor was trained thoroughly to express
even subtle differences between emotions;
only the emotion category and the text were given to the Affective
Reasoner. The task of the test
subjects consisted of assigning the spoken sentence the correct emotional
meaning from a list of alternatives. An
example:
|
"For example, in one
set, twelve presentations of the ambiguous sentence, "I picked up
Catapia in Timbuktu," were shown to subjects. These had to be matched
against twelve scenario descriptions such as, (a) Jack is proud of the
Catapia he got in Timbuktu because it is quite a collector's prize; (b) Jack
is gloating because his horse, Catapia, just won the Kentucky Derby and his
archrival Archie could have bought Catapia himself last year in Timbuktu; and
(c) Jack hopes that the Catapia stock he picked up in Timbuktu is going to be
worth a fortune when the news about the oil elds hits; [etc., (d) -
(l)]." |
|
(Elliott, 1997a, p. 3) |
Additionally,
the test subjects indicated on a scale from 1 to 5 how safe they were of their
judgements. The computer outputs were divided
into three groups: Face expression,
face expression and language and face expression, language and underlying
music.
Altogether the
test subjects could identify the underlying scenarios significantly better
correctly with the computer faces than with the actor (70 percent compared with
53 percent). There were hardly no
differences between the three representational forms of the computer
(face: 69 per cent; face and language: 71 percent; face,
language and music: 70 percent).
At present Elliott
works on the merging of the Affective Reasoner as module into two
existing interactive computer instruction systems (STEVE and Design-A-Plant) in
order to give to the virtual tutors the ability to understand and
expressemotions and thus to make the training procedure more effective (Elliott
et al, 1997).
5.5. The model of Scherer
Scherer
implemented his theoretical approach in form of an expert system named GENESE
(Geneva Expert System on Emotions) (Scherer, 1993). The motive was to get further insights for emotion-psychological
model building and to determine in particular how many evaluation criteria are
at least necessary in order to identify an emotion clearly:
|
"As shown earlier,
the question of how many and which appraisal criteria are minimally needed to
explain emotion differentiation is one of the central issues in research on
emotion-antecedent appraisal. It is argued here that one can work towards
settling the issue by constructing, and continuously refining, an expert
system that attempts to diagnose the nature of an emotional experience based
exclusively on information about the results of the stimulus or event
evaluation processes that have elicited the emotion." |
|
(Scherer, 1993, p. 331) |
The system
consists of a knowledge base in which is held which kinds of appraisals are
connected with which emotions. The
different appraisal dimensions are linked by weights with 14 different
emotions. These weights represent
thereby the probability, with which a certain appraisal is linked with a
certain emotion.
The user of the
program must answer 15 questions regarding a certain emotional experience, for
example: "Did the situation which
caused your emotion happen very suddenly or abruptly?". The user can answer each question on a
quantitative scale from 0 ("not true") to 5
("extraordinary").
If all
questions are answered, the system compares the answer pattern of the user with
the answer patterns which are theoretically linked with a certain emotion. Subsequently, it presents a list of all 14
emotions to the user, arranged in the order "most likely" to
"most improbably". If the
computer determined the emotion correctly, it receives a confirmation from the
user; otherwise, the user types in
"not correct". The system
presents to him then a further ranking of emotions. If this should be equally wrong, the user enters the correct
emotion and the program designs a specific appraisal-emotion data base with
this answer particularly for this user.
Through an
empirical examination of the forecast strength of his system, Scherer
determined that it worked correctly in 77,9 % of all cases. Certain emotions (e.g. despair/mourning)
were more frequently predicted correctly than others (e.g. fear/worries).
Schere'rs GENESE is in as
much unusual as it does not represent a classical rule-based system, but works
with weights in a multidimensional space.
There are exactly15 dimensions which correspond with the 16 appraisal
dimensions of Scherer's emotion model.
Each of the 14 emotions occupies a specific point in this space. The program makes its forecasts by converting
the answers of the users likewise into one point in this vector space and
measuring afterwards the distances to the points for the 14 emotions. The emotion lying next to the input is then
presented first.
Exactly this approach
motivated Chwelos & Oatley (1994) to a criticism of the system. First of all they point out that such a
spacewith 15 dimensions can contain altogether 4.7 x 1011 points. That can lead to the fact that the point
calculated after the inputs of the user can lie far away from each of the 14
emotions. Nevertheless, the system selects the nearest emotion. Chwelos & Oatley argue that in such a
case the answer should be rather "no emotion" and propose that the
system is extended by a limit value within which a given point of input must
lie around an emotion in order to elicit a concrete answer.
Secondly, they criticize
that the model proceeds from the assumption that each emotion corresponds with
exactly only one point in this space.
They raise the question why this is the case, since different combinations
of appraisal dimensions can elicit the same emotion.
Thirdly, Chwelos &
Oatley debate the heuristic adjustments of the appraisal dimensions implemented
in GENESE, which can not be found in Scherer's theoretical model. They speculate that it could be an artifact
of the vector space approach and note that it possesses no theoretical
motivation.
Finally, Chwelos &
Oatley doubt that Scherer's system actually delivers informations about how
many appraisal dimensions are at least necessary in order to differentiate an
emotion clearly.
5.6.
The model of Frijda and Swagerman
There exist two
implementations of Frijda's concern realisation theory: ACRES (Frijda and Swagerman, 1987) and WILL
(Moffat and Frijda, 1995).
ACRES
(Artificial Concern REaliation System) is a computer program which stores facts
about emotions and works with them.
Frijda and Swagerman wanted to answer the question: "Can computers
do the same sort of things as humans can by way of their emotions; and can they
be made to do so in a functionally similar way?" (Frijda and Swagerman, 1987, p. 236)
Starting point
for ACRES is the acceptance of a system which has various concerns and limited
resources. Furthermore, the system has
to move in an environment which is changing fast and never completely
predictable.
Based on these
conditions, Frijda and Swagerman define seven requirements for such a
system:
1. The existence of
concerns demands a mechanism which can identify objects with concern
relevance - objects which can promote or inhibit a concern.
2. Because opportunities
and dangers are distributed over space and time, the system must also be able
to act; otherwise it cannot be regarded
as independent. Furthermore, the action
control system must be able to understand the signals of the concern relevance
mechanism.
3. The system must possess
the ability to monitor its own activities regarding the pursuit of
opportunities and the avoidance of dangers and to recognize whether an action
can lead to success or not.
4. The system must have a
repertoire of appropriate action alternatives and be able to generate action
successions or plans.
5. The system needs a number of
pre-programmed actions for emergencies, so that it can react fast if necessary.
6. Since the environment of the system
consists partially of other agents like it, actions with a social character
must be present in the action repertoire.
7. Multiple concerns in an uncertain
environment make it necessary to rearrange and/or temporarily postpone
goals. The system must have a mechnaism
which makes such changes of priorities possible.
All these
specifications are fulfilled by the human emotion system, according to Frijda
and Swagerman:
- Objects are felt as attractive
or repulsive.
- Attaining or non--attaining of
such objects releases signals of joy and pain.
- Different emotions are answers
to different situations with different relevance and include action
selection procedures.
- Different emotions release
different impulses and with it different action plans.
- Some of these emotion-action
pairs are pre-programmed.
- Social actions are a
particularly conspicuous group of the emotional actions.
- Emotional action programs
dominate non-emotional action programs - and thus lead to the interruption
of current activities and to the re-organization of the goals of the
system.
In order to
implement such a system, Frijda and Swagerman selected as an action environment
which makes sense for a computer program, the interaction with the user of this
program. The concerns of the system in
this context are:
|
|
avoid being killed concern; |
|
|
preserve reasonable waiting times
concern; |
|
|
correct input concern; |
|
|
variety in input concern; |
|
|
safety concern. |
All knowledge
of ACRES is organized in the form of concepts.
These concepts consist of attribute-value pairs. Concerns are represented by a concept which
contains, on the one hand, the topic and, on the other hand, a tariff
sub-conzept which represents the desired situation.
ACRES has three
major tasks: To receive and accept
input (the system rejects inputs with typing errors, for example); to learn about emotions through the
informations about emotions it receives from the user as well as to gain, store
and use knowledge about its own emotions and the emotions of others. Therefore,
the system has three corresponding task components: Input, Vicarious knowledge and Learning.
Each task
component has two functions: an operation
function and a concern realisation function. The functions test whether concepts
exist,which are applicable to the received information; they use their knowledge to infer and
generate related goals; they infer,
which actions are relevant for the reaching of these goals and elicit
appropriate actions.
The essential
informations with which ACRES works result from the inputs of the user, from
informations already collected by ACRES as well as informations inferred by
ACRES from the existing information store.
The collected
informations represent the "memory" of ACRES. To this belongs, for example, how often a
certain user made typing errors during the input; how long ACRES had to wait for new input etc.. Due to its experiences with the users ACRES
builds a so-called status index for each user: positive experiences lead to a rise in status, negative to the
lowering of status.
Concern
relevance tests run in ACRES in such a way that the information about a current
situation is compared with the pre-programmed concerns of the system. Apart from the informations which are
collected by ACRES in the course of time, there are some specific inputs which
are directly emotionally relevant for ACRES, for example the instruction "kill"
Information
about action alternatives is likewise represented in ACRES in the form of
concepts. Each action concept consists
of the sub-concepts start state, end state, and fail state. The sub-concept start state
describes the initial conditions of an action, end state describes the
condition that the action can reach, and fail state the conditions under
which this action cannot be implemented.
With the action
selection, firstly the goal is compared with the end state sub-concepts
of all action concepts; then the
current state is compared with the start state sub-concepts of the
action concepts selected before, and one of it is selected. If no suitable action concept exists, a
planning process is initiated which selects the action concept with the most
obvious start state.
Events lead
ACRES to the setting up of goals. The
event of the discovery of concern relevance leads to the goal of doing
something in this regard. The following
action selection process selects an action alternative with the procedure
described above. This process
corresponds to what Frijda calls context appraisal in his emotion model
.
|
|
Time, processing capacity, and
storage space are used to prepare and execute the concern realisation
goal. Task-oriented processing is postponed.. |
|
|
The precedence remains if the user
does not change the situation due to the requests of ACRES. |
|
|
ACRES can refuse to accept new
input as long as its concern has not been realized. |
|
|
ACRES executes the concern realisation
actions, some of which can affect the following processing. |
Control
precedence depends with ACRES on two factors:
the relative meaning of the mobilized concerns and the gravity of the
situation. The relative meaning of the
concerns is a previously set value; "kill"
has the highest meaning of all . The
gravity of the situation is a variable which changes by the interaction of
ACRES with the users. In order to become effective, the control precedence must
pass a certain threshold value.
The net result
all these processes are a number of "emotional" phenomena. ACRES has, for example, a vocabulary of
curses, offenses or exclamations which can express such a state. The system can refuse to co-operate with an
user further; can try to influence him
or address simply again and again the same request to the user. What is special with ACRES is not the fact
that the program does not continue working with incorrect input - every other
software does this also:
|
"It is the dynamic nature
of the reactions, however, that is different: They sometimes occur when an
input mistake is made or some other input feature is shown, and sometimes
they do not. Moreover, some of the reactions themselves are dynamic, notably
the changes in operator status." |
|
(Frijda und Swagerman, 1987, p. 254) |
Apart from
the perception of events and their implications, ACRES is also able to notice
its own perception. The model designs a
representation of the current state and of the aspects relevant for its
concerns. According to Frijda and
Swagerman, ACRES thereby designs an emotional experience for itself. They stress expressly: "It is not a play on words when we say
that ACRES builds up an emotional experience." (Frijda und
Swagerman, 1987, p. 254). They
continue:
|
"We do not wish to
go into the deep problems of whether ACRES' representations can be said to
correspond to "experiences", to "feels", as they are
called in philosophical discussion. Probably, ACRES cannot be said to
"feel", just as a colour-naming machine cannot be said to
"see" red or blue, although we still have to be given satisfactory
criteria for this denial of feeling or seeing. The main point, in the present
context, is that ACRES shows many, and perhaps, in essence, the major signs that
lead one to ascribe "emotions" to an animate agent." |
|
(Frijda und Swagerman, 1987, p. 255) |
The authors grant that
their model still lacks certain features.
ACRES is by far unable to show all phenomena which arise during the ascribing
of emotions. They state, however, that,
regarded from a theoretical point of view, these shortcomings can be considered
as trivial, because this is not a question of principle, but only of the
implementation. They state that the
computer cannot work parallelly and thus cannot supply interruptions of a
current activity in real time.
Furthermore, the computer would not move around in a real environment
and possess no energy management of its own.
All these, they postulate, are purely technical questions and not
problems of the theory.
5.7.
The model of Moffat and Frijda
In a
further work (Moffat, Frijda and Phaf, 1993), Frijda nevertheless grants that
ACRES exhibits some shortcomings in principle.
Because of that, he developed, together with Moffat (Frijda and Moffat,
1993 and 1994), a new basic model which has been rudimentarily implemented in
the system WILL (Moffat and Frijda, 1995;
Moffat, 1997).
5.7.1. Criticism of ACRES
Starting point for the
criticism of ACRES is a logged meeting with a user which is typical for the
system:
|
Hello, Dave. I am ACRES. What
would you like to know? |
|
|
[1] show_concept X |
Query the database about X. |
|
[2] debug_on |
Turn debugging on. |
|
[3] debug_off |
Turn it off again. |
|
[4] debug_off |
I'm trying to `bore´ ACRES. |
|
[5] debug_off |
|
|
[6] debug_off |
I ignore his requests to type
faster, and to be more interesting, so he regards me as an unfriendly threat. |
|
[7] debug_off |
|
|
[8] debug_off |
|
|
[9] debug_off |
|
|
[10] name_last_emotion I experienced : Self-hatred ... 89 % Anger ......... 88 % Disgust ....... 79 % YOU ENDANGER MY SAFETY! |
Ask the model to analyse its |
|
[11] show_concept |
|
|
[12] X |
|
|
[13] kill |
First "kill" command to
end session |
|
[14] debug_off |
Ignore request, but don't
"kill" either. (Unusual aggression
for someone making an appeal.) |
|
[15] live |
|
Table 7: Session protocol of ACRES (Moffat
et.al., 1993)
On the basis of
these minutes, Moffat et al. formulate the following criticism of ACRES:
1. ACRES shows enormous
emotion reversals, and this within few seconds. One of the reasons for it, so the authors, could lie in the fact
that ACRES has no physiology like humans whose emotional states are lent a
certain stability and duration by neurotransmitters, for example. Much more important, however, is for the
authors that ACRES possesses no memory.
Even a short time memory, thus the ability to remember the immediately
preceding state, could affect the behaviour of the system in a similar
direction as a physiology.
2. ACRES supplies in one
and the same output contradicting emotional reactions. If a user enters the same instruction again
and again, but fast, ACRES shows a positive emotional reaction regarding the
speed of the input, regarding the lack of variability of the input however a
negative emotional reaction. This is a
behaviour untypical for humans.
3. The emotional and
non-emotional reactions exhibited together by ACRES concern not the same topic,
but different topics. This is also
rarely observed with humans. ACRES can
answer the question of a user and directly afterwards give an emotional
reaction on another topic. As a reason
for this behaviour the authors state that ACRES cannot differentiate
theoretically between emotional and more generally motivated behaviour and
regards these as qualitatively equivalently.
The reason for this would lie in an arbitrarily determined threshold
value with which the system differentiates between emotionally relevant and
emotionally irrelevant concerns.
4. The reactions of ACRES
are easily predictable. Thus, if the
input is too slow, it always answers with the phrase "
You keep me waiting too long!”. This corresponds more to a reflex than to a
genuine emotional reaction.
5.7.2.
Demands upon an emotional system
Due to this
analysis, the authors then suggest a number of further components which an
emotional system should possess and which, at the same time, also affect a
theory of emotions.
With the
term awareness of the present
they describe the ability of a system to observe its own actions over a
certain period of time. This motivational
visibility of the present means that a system does not simply forget a
motivated action which failed, but that the emotion disappears only then if the
goal condition originally aimed at is reached.
As the second necessary
element they name the motivational visibility of the planner. In ACRES, like in almost all other AI
systems, the planner is implemented as a separate module. The other modules receive no insight into
its semifinished plans and therefore cannot affect them. The different concerns of a system must,
however, possess the possibility of having insight into these plans, since
these develop under specific criteria which might be, taken for themselves,
completely logical but perhaps hurt another request.
The third
element is called by the authors motivational visibility of the future. This means the possibility to make not only
the own planned actions visible for the entire system, but also the actions of
other agents and events from the environment.
This is important for anticipations of the future and thus for emotions
like, for example, surprise.
Furthermore,
the system needs a world model. In ACRES, only the planning module contains
such a world model. The overall system
does not have the possibility of observing the effects of its actions and of
recognizing whether they failed or were
successful. Coupled with a memory, the
world model lends the ability to the system to try out and evaluate different
actions. The system receives thereby a
larger and, above all, more flexible action repertoire. At the same time, a sense of time is
necessary with which the system can assess within which period of time it must
react and which time is taken up by an action.
Finally, the
authors consider it essential to differentiate clearly between motives and
emotions, something ACRES does not do.
They postulate that an emotion arises only then if a motive cannot be
satisfied or only with a large load upon the resources of a system. A system will first try to satisfy a concern
with the associated, almost automatic action.
If that does not work or the system can predict that it will not
function or the confidence of the system into the functioning is low or the
system assumes that it does not possess sufficient control, only then arises an
emotion. Its function is to mobilize
the entire system in order to cope with the problem.
5.7.3.
Implementation in WILL
Based on these considerations, Frijda and Moffat have
developed a computer model called WILL which is supposed to correct the
shortcomings of ACRES. WILL is a parallelly working system with the following
architecture:
Fig. 5: Architecture of WILL (Frijda and
Moffat, 1994)
The system
consists of a perception module, the Perceiver; an action execution module, the Executor; a forecast module, the Predictor; a planning module, the Planner as
well as an emotion module, the Emotor.
In addition it contains a memory and a module for the examination of
concern relevance.
A basic
principle of the system is it that all modules communicate not directly with
one another, but only through the memory.
Thus all elements of the system have at any time access to all processes
and subprocesses of other elements.
Each module reads out its information from memory, works on it and
writes it again into memory. All the
modules work in parallel, i.e., they are all equal in principle.
Everything that
is written into memory is tested for concern relevance when it passes the concerns
layer. By this mechanism, the
system receives a regulation instance, because different concerns have
different meaning for the system. The
concern relevance module thus possesses a control function by differently
evaluating the passing information.
This evaluation
looks such that the concern relevance module attributes a charge value
to each element which is written into memory.
Elements with higher charge are more relevant for the concerns of the
system than elements with low charge.
Each of the
modules receives its information from memory.
The element with the highest charge is always given to the modules to be
worked on by them. The authors call
this element focus item. In
order to prevent that the order of rank of the elements remains the same in
memory, the elements must be able to win and lose charge. With WILL this happens this by the fact that
the element in memory with the highest charge loses charge if it is not worked
on in a working cycle by a module. Thus
if the Planner received a focus element but could develop no plan in
connection with it, the element is written back into memory with a lower
charge. The authors call this procedure
autoboredom.
The task of the
Emotor consists, in the context of a further appraisal process (Moffat
calls this secondary appraisal;
it corresponds with the context appraisal from Frijda’s theory),
in the production of action tendencies for elements with high concern relevance
which belong to the emotion and to deposit these in memory as action
intentions. With the next cycle, the Executor
will take up this action intention if it was not changed in the meantime or
lost the rank of focus element.
Moffat
presented a first realization of WILL (Moffat, 1997). The system has the task to play the game "Prisoner's
Dilemma" with a user. In its basic
form, Prisoner's dilemma consists of the fact that two players decide
independently from one another whether
they want to cooperate with their opposite (cooperate, c) or not
(defect, d). After they made their choice, this is
communicated to both players. Depending
upon the result (cc, cd, dc, dd) the players get paid out a certain
amount of money. The result matrix for
WILL looks as follows (numbers mean amounts of dollars):
|
|
User |
||
|
c |
d |
||
|
Will |
c |
3 3 |
5 0 |
|
d |
0 5 |
1 1 |
|
Table 8: Result matrix for Prisoner's
Dilemma (after Moffat, 1997)
Extensive
experimentation (see e.g. Axelrod, 1990) has shown that under most
circumstances a strategy of mutual co-operation for both sides is most
successful. However, there can be situations
in which it is better for a player not to cooperate.
In Moffats
model, there are two kinds of
events: move events and payoff
events. The decision of the user is
represented formally with move(user, c). A prognosis which move will be made by the user in the next round
is expressed by move(user, { c,d }).
With these definitions, the world of the game can be expressed in
structured form. Thus the assumption of
WILL that it will not cooperate, but that the user either will cooperate or not
is expressed as follows with the associated rewards:
move(will,d) & move(user, {c,d}) ==> payoff (will, {1,5}) &
payoff (user, {0,1}
The concern of
WILL in this game is to win as much money as possible. This is expressed formally as $_concern = [0 -> 2 ->5] and means that the most undesirable
result is 0 dollars, the most desirable 5 dollars and the so called set-point
2 dollars. The set-point defines the average result. The valence of the
possible result is defined as follows for WILL:
win $0 --> valence = -2 x IMP
win $2 --> valence = 0 x IMP
win $3 --> valence = +1 x IMP
win $5 --> valence = +3 x IMP
IMP is a factor for the importance of the concern.
A further concern of WILL is moral behaviour . The system knows that co-operation is more
moral than non--co-operation:
morality_concern = [0 -> 0.8 -> 1].
The game move c has the moral value 1, the move
D the value 0. The set-point
is 0.8.
WILL has two cognitive modules, the Predictor
and the Planner. Implemented in
memory is a world model which
expresses, for example, the assumption that the user will not cooperate
constantly as follows:
move(user,UM) --> move(user,d).
According to Moffat, with the elements mentioned
already substantial parts of an emotion are modelled, i.e. affect, relevance
assessment and control priority. For
context appraisal and action tendency, the Emotor is responsible. The appraisals programmed into WILL are
derived from Frijda’s theory. Some
examples:
|
|
Valence – Can be + or – . States how
un/comfortable the emotion is. |
|
|
Un/Expectedness - Was the perceived
event expected? |
|
|
Control – Does the agent have control of
the situation? |
|
|
Agency – Who is responsible for the
event? |
|
|
Morality – Was the event (action) moral? |
|
|
Probability – The probability that the event
will really happen. |
|
|
Urgency – How urgent is the situation? |
Action
tendencies are likewise firmly programmed into WILL. Some examples :
|
|
hurt(O) / help(O) – Wants to harm or help other agent
O. |
|
|
try_harder(G) / give_up(G) – Try harder or give up goal G. |
|
|
approach(O) / avoid(O) – Wants to approach or avoid O. |
|
|
fight(O) / flee(O) – Wants to fight O or flee. |
|
|
exuberance / apathy &
inhibition –
General activation level. |
|
|
|
From the appraisals and
action tendencies, the Emotor produces emotions which Moffat calls true. He gives three examples:
Happiness Appraisals: valence = positive
agency = world
Action tendency:
happy_exuberance
Anger Appraisals: valence = negative
morality = negative
agency = User
Action tendency: hurt(User)
--> play D,
attend(User)
Pride Appraisals: valence = positive
morality = positive
agency = Self
Action tendency: exuberance
--> verbal,
attend(Self)
On the basis of
a session protocol, Moffat then describes the internal functioning of
WILL:
1. Planner: Notice that I
play c or d in round 1.
a. Decide that I play c
in round 1.
WILL hat noticed that soon
it will play a first round of Prisoner's Dilemma. The Planner points out
the two alternatives; the decision falls on c because this is the
morally more correct alternative.
2. Predictor: Notice that I
play c in round 1.
a. Predict that I win $0
or $3 and User wins $3 or $5 in round 1.
The Predictor picks
up the information written back into memory and predicts the possible results
of the first round.
3. Predictor: Notice that I
win $0 or $3 in round 1.
a. Predict that I play c
or d and User plays c or d in round 2.
4. Predictor: Notice that I
play c or d in round 2.
a. Predict that I and User
win $0 or $1 or $3 or $5 in round 1.
The Predictor again
reads out the information and makes further predictions.
5. Planner: Notice that I
play c or d in round 2.
a. Decide that I play c
in round 2.
The Planner reads
out the information and plans for round 2.
6. Executor: Tell the
Umpire that I play c in round 1.
The Executor
implements the action for the first round suggested by the Planner and
announces it to the umpire, a software module independent of the system. The perceptible topic change illustrates how
by charging or uncharging of elements in memory the attention of the system
shifts: For several working cycles the
move for round 1 was charged so low that the other modules did not occupy
themselves with it.
7. UMPIRE: Round 1. What do
you play ? . . . c.
8. UMPIRE: Round 1. You
play c and Will plays c.
9. Perceiver: Hear from
Umpire that User just played c and I just played c.
10. Emotor: Notice that I
just played c in round 1.
a. Appraisals
b. intensity = 0.4 Action tendencies
c. valence = +0.4 exuberance = 0.4
d. agency = myself
e. morality = 0.4 emotion is pride
f. [0.4] express pride
The umpire announces the
moves of the first round. The Perceiver writes them into memory. The Emotor
perceives them and develops due to ist moral move a positive emotion whose
value, however, lies underneath the arbitrarily determindes threshold for
verbalization.
11. UMPIRE: Round 1. You
win $3 and Will wins $3.
12. Perceiver: Hear from
Umpire that User just won $3 and I just won $3.
13. Emotor: Notice that I
did win $3 in round 1.
a. Appraisals
b. intensity = 4.0 Action tendencies
c. valence = +4.0 jump_for_joy = 4.0
d. agency = world
e. morality = 0.0 emotion is joy
f. [4.0] express joy
say: "La la la!"
The umpire announces the
rewards of the first round. The Perceiver writes them into memory where
they are perceived by the Emotor which then produces an emotion with a
high enough value to lead to a verbalization.
14. Emotor: Notice that I
did win $3 in round 1.
a. Appraisals
b. intensity = 2.7 Action tendencies
c. valence = +2.7 jump_for_joy = 2.7
d. agency = world
e. morality = 0.0 emotion is joy
f. [2.7] express joy
Since no other module works
with the information of the Emotor arbeitet, a porcess of uncharging
takes place (autoboredom). The value of the emotion drops.
15. Emotor: Notice that I
shall win $0 or $1 or $3 or $5 in round 2.
a. Appraisals
b. intensity = 3.0 Action tendencies
c. valence = +3.0 jump_for_joy = 6.0
d. agency = world
e. morality = 0.0 emotion is joy
f. [6.0] express joy
say: "Yabba-dabba-doo!"
The Emotor reads out
the reward expectations for round 2 and develops an appropiate expectation with
high value .
. . .
16. UMPIRE: Round 2. You
play d and Will plays c.
. . .
17. Emotor: Notice that
User just played d in round 2.
a. Appraisals
b. intensity = 1.8 Action tendencies
c. valence = -1.8 sentiment = -2.7
d. agency = user so urge = 4.5 (|int-sent|)
e. morality = -1.8 hurt(user) = 4.5
f. [4.5] express anger emotion is angry revenge
say: "I will get you back for that!" & soon play d to hurt
user
(Several
intermediate steps are omitted.) The umpire announces the moves of round 2.
Move d of the user means that WILL gets nothing. This annoys WILL
because it does not only hurt its moral yardsticks but also impairs its concern
to make money. The value of the emotion produced by the Emotor is
accordingly high. This elicits the action tendency to likewise play d in
the next round in order to pay back the user.
In a following
discussion Moffat asks whether WILL possesses a personality as defined by the
five big traits. He states the
fact that Will is neither open nor agreeable: For this it has too few interests and has no
social consciousness. It is, however,
neurotic because WWILL is a worrier;
in addition one could say that he, at least partly, is conscientious -
he is equipped with a concern for fairness and honesty. Also, the characteristic of extrovertedness
can be partly ascribes to it. Moffat
comes to the conclusion that machines can possess quite human-like
personalities:
|
"In this case, the answer
is a much more qualified "yes"... The programmable personality
parameters in Will include the charge manipulation parameters in the
attentional mechanism, the appraisal categories, action tendencies, and
concerns, all of which can be set at different relative strengths. In this
programmability, human-specificity can be built in as required, but with
different settings other personalities would also be possible, the like of
which have never yet existed. What they may be is beyond science-fiction to imagine,
but it is unlikely that they will all be dull, unemotional computers like HAL
in the film 2001." |
|
(Moffat, 1997) |
5.8. Other models
There exists a
number of other models which are concerned, under different aspects, with the
simulation of emotions in computers.
Some of them will be described briefly in this section; a more detailed discussion would surpass the
scope of this work.
5.8.1. The model of Colby
One of the
first computer models, which was concerned expressly with emotions, was PARRY
by Kenneth Colby (Colby, 1981). PARRY
simulates a person with a paranoid personality who believes to be pursued by
the Mafia. The user interacts with the
program in form of a dialogue; the system reacts with verbal outputs to text
inputs through a keyboard.
The program has
the task to scan the inputs of the user actively for an interpretation which
can be seen as ill will. As soon as
this is discovered, one of three emotional reactions is elicited: fear, anger, or mistrust,
dependent on the kind of the ascribed ill will. An assumed physical threat elicits fear; an assumed psychological threat elicits
anger; both kinds of assumed threats
cause mistrust. PARRY reacts to the
attacks constructed by it either with a counter attack or with retreat.
In order to
design a model of a paranoid patient, Colby and his coworkers invested, for
that time, a lot of work into the project.
PARRY has a vocabulary of 4500 words and 700 colloquial idioms as well
as the grammatical competence to use these.
PARRY compares the text inputs of the users with his stored word and
idiom list and reacts in the emotional mode as soon as it discovers a
correspondence.
A number of
variables refer to the three emotional conditions fear, anger and mistrust and
are constantly updated in the course of an interaction. Thus PARRY can “work itself up" into
certain emotional conditions; even
Colby, its creator and a psychiatrist by training, was surprised by some
behaviours of PARRY.
Colby submitted
PARRY to several tests. In one he let
several psychiatrists lead telephone interviews with paranoid patients and with
PARRY, without informing them about the fact that one "patient" is a
machine. After their interviews Colby
informed the participants about this and asked them to identify the
machine. The result: Apart from one or the other accidental hit,
no psychiatrist could indicate whether he had conversed with a human or with
PARRY.
In a further
experiment an improved system was presented to a number of psychiatrists
again. This time the test participants
were informed from the beginning about the fact that one of their interviewees
would be a computer and they were requested to identify him. Again the results did not deviate
substantially from the first experiment.
PARRY possesses
the ability, to express beliefs, fears,
and anxieties; these are, however, pre-defined
and hardwired from the outset. Only the
intensity can change in the course of an interaction and thus modify the
conversational behaviour of PARRY.
5.8.2. The model of Reeves
THUNDER stands for THematic UNDerstanding from Ethical Reasoning and was developed by
John Reeves (Reeves, 1991). THUNDER is
a system that can understand stories and has its emphasis with the evaluation of
these stories and with ethical considerations.
In order to represent different criteria in a conflict
situation, THUNDER uses so-called Belief Conflict Patterns. Thus
the system is in a position to work out moral patterns from submitted
stories. These patterns are then used
by so-called evaluators in order to make moral judgements about the
characters in a story. According to
Reeves, without such moral patterns many texts (and also situations) could not
be understood.
As example
Reeves cites the story of hunters that tie dynamite to a rabbit “just for
fun”. The rabbit hides itself with the
dynamite under the car of the hunter which is destroyed by the following
explosion. In order to understand the
irony of such a story, the system, according to Reeves, has to know first of
all that the action of the hunters is morally despicable and the following,
coincidental destruction of their car represents a morally satisfying
reconciliation for it.
The emphasis of
THUNDER lies on the analysis of motives of other individuals, who are either in
a certain situation or observe it.
5.8.3.
The model of Rollenhagen and Dalkvist
Rollenhagen and
Dalkvist developed SIES, the System for Interpretation of Emotional Situations (Rollenhagen
and Dalkvist, 1989). The task of SIES
is it to draw conclusions about situations which elicited an emotion.
SIES unites a
cognitive with a situationalen approach.
The basic assumption is that the eliciting conditions of an emotion are
to be found in situations of the real world.
The core of SIES is a reasoning system which accomplishes a structural
content analysis of submitted texts.
These texts consist of reports in which one reports retrospectively on
emotion-eliciting situations.
The system is
equipped with a set of rules which are able to differentiate and classify
emotions but really do nothing else than to structure the information contained
in a story.
5.8.4.
The model of O'Rorke
The AMAL system
introduced by O'Rorke and Ortony (O'Rorke and Ortony, unpublished manuscript),
later called by Ortony also "AbMal", is based on the theoretical
approach of Ortony, Clore and Collins (1988).
The goal of AMAL is to identify emotion-eliciting situations which are
described in diaries of students. In
order to solve this task, AMAL uses a so-called situation calculus. With the help of abductive logic, AMAL can
filter plausible explanations for their occurrence from emotional
episodes.
5.8.5.
The model of Araujo
Aluizio Araujo
of the University of Sao Paulo in Brazil has developed a model which tries to
unite findings from psychology and neurophysiology with one another (Araujo,
1994).
The interest of
Araujo lies in the simulation of mood-dependent recall, learning, and the
influence of fearfulness and task difficulty on memory. His model consists of two interacting neural
nets, the "emotional net" and the "cognitive net". The intention is to simulate thereby the
roles of the limbic and the cortical structures in the human brain. For Araujo it is essential to model not only
cognitive processes but also physiological emotional reactions on a low level
which affect the cognitive processing on a higher level.
The emotional
net evaluates affective meanings of incoming stimuli and produces the emotional state of the system. Its processing mechanisms are relatively
simple and accordingly fast. The
cognitive net implements cognitive tasks, for example free recall of words or
the association between pairs of words.
The processing processes are more detailed than with the emotional net
but require more time.
In Araujos
model, an "emotional processor" comoutes valence and excitation for
each stimulus and changes through this parameters of the cognitive net. In particular the output of the emotional
net can affect the learning rate and the accuracy of the cognitive net.
5.9.
Conclusion and evaluation
The models presented in this chapter differ clearly in
their theoretical assumptions and in their details. There is a common theme, nevertheless: The goal of all models is either to understand emotions or to
exhibit (pseudo)emotional behaviour.
What an emotion is, is exactly defined
from the outset. The differences
between the models lie mainly in the
variety of the defined emotions as well as in the wealth of details of
the model.
The models of Elliott and Reilly are based on the
emotion theory of Ortony, Clore and Collins.
Their goal is to increase the efficiency of a computer system with
certain tasks through consideration of emotions, for example with speech comprehension
or with the development of computer-assisted training systems or other
interactive systems. The introduction
of emotional elements in these models is made according to given tasks with the
aim to absolve them more effectively.
Both Elliott and Reilly achieved with their models a large part of what
they aimed at. It becomes clear, however, that the operationally formulated
theory of Ortony, Clore and Collins cannot be converted simply into a computer
model, but must be extended by additional components whose value in the context
of the theory is doubtful. Particularly
Reilly’s criticism of the "overcognitivation" of the theory led him
to introduce a "shortcut" which does not represent simply an
extension of the OCC model, but stands outside of it.
The models BORIS and OpED of Dyer, just like AMAL,
THUNDER and SIES, also serve only to identify emotions from texts, but carry
out this task less efficiently than Elliotts Affective Reasoner.
In contrast, the models of Frijda and his coworkers
pursue the goal of examining Frijda’s emotion theory with the help of a
computer model. The deficits arisen
with ACRES brought Frijda to a partial revision of his theory which shall be
examined anew with WILL. The models do
not have another task than the implementation and examination of the
theory. The same applies also to
Scherer’s GENESE, even if the depth of detail of this model is substantially
less than with WILL.
DAYDREAMER by Mueller and Dyer and WILL by Frijda and
Moffat are situated in a region already bordering on models in which emotions
are regarded as control functions of an intelligent system. Also, Pfeifer’s FEELER developed from the
demand to simulate control processes;
something the model is not able to, however, due to its very specific
emotion definitions.
The model of Colby finally is of more historical
interest, since he was interested less in the modelling of emotions but more in
the simulation of a specific phenomenon which included an emotional component.
Next Previous Contents