Download Innate and Learned Emotion Network

Innate and Learned Emotion Network
Rony NOVIANTO a and Mary-Anne WILLIAMS b
a
[email protected]
b
[email protected]
Centre for Quantum Computation and Intelligent Systems
University of Technology Sydney, Australia
Abstract. Autonomous agents sometimes can only rely on the subjective information in terms of emotions to make decision due to the inavailability of the nonsubjective knowledge. However, current emotion models lack of integrating innate
emotion and learned emotion and tend to focus on a specific aspect. This paper
describes the underlying new computational emotion model in ASMO which integrates both innate and learned emotions as well as reasoning based on probabilistic
causal network. ASMO’s emotion model is compared with other models and related works and shows its practical capabilities to utilize subjective knowledge in
decision making.
Keywords. Innate and learned emotion, Emotion causal network, Attentive and
Self-modifying (ASMO) emotion
1. Introduction
Autonomous agents sometimes do not have the required non-subjective knowledge to
reason about the environment and need to rely on the subjective knowledge available
to make decision, which are typically presented in terms of emotions. For example, a
duration or a cost of a path is unknown but a user’s opinion or feeling about the path is
known. This subjective knowledge or emotion is also needed when reasoning does not
find the optimal solution in time because of the complexity of the systems. In addition,
it can be used to represent agents’ preferences when more than one acceptable solution
is found, which create their individual characteristics. Inspired by human emotion, we
model this subjective knowledge or emotion on the existing ASMO cognitive architecture
[8,9].
The discussion of whether human emotion is innate or learned leads to two major
aspects of emotion, namely biological and cognitive aspects, which are often viewed
as being opposed to each other. The biological aspect leads to a basic emotion theory
whereas the cognitive aspect leads to a cognitive appraisal theory [13]. In this paper, we
explore both of these aspects.
The theory of biological and cognitive aspects of emotion and how they are designed in ASMO are described in Section 2. In Section 3, we describe how behavior
and physiological responses are influenced by emotion and reasoning. The evaluation of
ASMO’s emotion model in a robot bear is then discussed in Section 4. We conclude with
a comparison with other models and related works in Section 5 and highlight its practical
impact.
2. Emotion Theory and Design
Emotions can be represented using a n-dimensional space [12] where each coordinate in
a space refers to a specific instance of emotion. In the current ASMO architecture, three
emotional dimensions namely positive valence, negative valence and arousal are used to
indicate how pleasant, unpleasant, and exciting a situation or an event is respectively.
The values of these dimensions are bounded from 0 to 1 (equivalent to a range from 0 to
100 percent) and thus a dimension could not have a negative value.
We view emotion as the probability of causing pleasantness, unpleasantness, and
excitement to occur. An event which has the probability of 0.7 to cause pleasantness to
occur, may imply that it has the probability of 0.3 to cause pleasantness to not occur, but
does not imply that it has the probability of 0.3 to cause unpleasantness to occur and vice
versa. In another words, a situation is not necessarily unpleasant when pleasantness does
not occur. This suggests that both positive and negative valence dimensions should be
independent to each other, i.e. bivariate instead of bipolar dimensions.
Some studies have found that people can feel both happy and sad at the same time
[5] although it is not clear in those studies whether people do so because they interpret
the same situations from different perspectives, hence different appraisals. Other emotion
models have also suggested different kind of bivariate dimensions such as [7,18], however, they are less clear and practical. For example, the difference between pleasantness
and positive affect dimensions in work by Watson and Tellegen [18] is ambiguous.
ASMO’s emotion is modeled using a directed acyclic causal network [11] where
nodes are divided into four categories, namely label, dimension, biological, and cognitive nodes (see Figure 1 for an example of an emotion network). This causal network
restricts the parent nodes to be the cause of their children nodes, i.e. causality relationship. For example, ‘rain’ node can be the parent of ‘wet’ node, but not vice versa. The
label nodes represent types of emotion, such as happy and sad. The dimension nodes are
connected to the label nodes and correspond to the emotional dimensions, i.e. positive
valence, negative valence, and arousal dimension. The biological nodes are connected to
the dimension nodes and/or other biological nodes. The cognitive nodes are connected to
the dimension nodes, biological nodes and/or other cognitive nodes.
Figure 1. Emotion network with different node categories
2.1. Biological Appraisal
Human emotion cannot always be justified. People sometimes report that they like something because of the way it is regardless its relevance and effects to them. They have no
reason or do not think that they need a reason to like it. They seem to have biologically
innate or ‘built-in’ knowledge to evaluate situations without reasoning, similar to traits
that are imprinted in the DNA. Studies have shown that infants have innate preferences to
sweet taste [4]. Infants have also shown an emotionally innate response to high-pitched
human voices [19]. Major research traditions in the biological perspective of emotion
can be seen in [13, p.309]. We refer to biological appraisal as this evaluation of an event
or a situation based on innate or ‘built-in’ knowledge.
In ASMO, innate knowledge is built as biological nodes where their conditional
probability values can be determined at design time. By convention, these nodes are fixed
across the agent’s lifespan, which are useful when the designers want to embed permanent characteristics or personality to the agent, e.g. being harmful to human is unpleasant.
The strength level of the characteristics or personality depends on the conditional probability values. The agent can have a maximum strength of emotional judgement about an
event by ensuring that the conditional probability values are at maximum, which is 1.
2.2. Cognitive Appraisal
According to cognitive appraisal theory, emotions are elicited based on the individual’s
subjective appraisal of a situation or an event in terms of its relevance and effects to personal well-being [6]. Some of the most frequently used theories in computational modeling, such as the OCC theory [10] and Scherer’s theory[17] describe criteria to evaluate
a situation in order to generate an emotion. A theory can have the same criteria and yet
generates different emotions because of the different ways to structure the criteria. Major
research traditions in the cognitive perspective of emotion can be seen in [13, p.311].
Cognitive appraisal is the evaluation of an event or a situation based on the knowledge
which is learned because of its relevance and effects to personal well-being.
In ASMO, the evaluation criteria are structured as cognitive nodes in the causal
network, which can be learned from the environment, such as through association or
conditioning. As the number of nodes increases, the network becomes computationally
intractable to do exact inferece, so approximate inference method such as Monte Carlo
algorithm [16] is used to approximately evaluate the situations.
Cognitive appraisal can be seen as a specific type of reasoning. It always evaluates
situations in terms of the probability of the emotional dimensions to occur whereas reasoning evaluates situations in terms of the probability of the goals to occur. Reasoning
does not necessary elicit emotions as shown in Figure 1 where the energy node is disconnected from the dimensional nodes. However, reasoning can just be as same as cognitive
appraisal when the goals are the emotional dimensions nodes, e.g. the agent wants to
get pleasantness. Thus, ASMO’s emotion network integrates the different perspectives
of emotion and reasoning on the same network.
3. Behavior and Physiological Responses
Biological appraisal, cognitive appraisal and reasoning influence behavior and physiological responses. Some people would do things based on their goals despite their emo-
tions, while some others would do the same things based on their emotions despite their
goals. Consider a play–sleep situation where a person encounters a dilemma between
playing a red ball game, playing drums, or going to sleep (see Figure 1 for the emotion network). Assume that the person is a male for the simplicity of the paper and the
emotion towards the red sensation is innate. He likes to play his favorite red ball game
without any reason, but he also wants to play drums, because he thinks that he will get
praised when he plays the drums and not the ball game. Meanwhile, he needs to go to
sleep to have enough energy for working the next day.
In this case, biological appraisal favors playing ball behavior because of the innate
preference in the red sensation. Cognitive appraisal favors the playing drums behavior
because he has learned that praise is benefit for his well-being. Reasoning favors going
to sleep behavior because his goals can be achieved. Depending on which evaluation is
higher, he will choose to play ball or drums despite his goal to sleep or choose to go to
sleep despite his emotion about playing ball and drums.
Behavior and physiological responses are also effected by arousal. The higher the
arousal is, the faster the responses. When people are highly aroused, they will act or
move faster and their heart rates are faster than when they are calm or in a normal state.
4. Discussion
The emotion network mechanism is evaluated using a robot bear called Smokey based
on the play–sleep dilemma described above. Smokey is presented with a red ball and
drums while ‘he’ has a goal to sleep. His evaluation is calculated based on the conditional
probability tables where initial values are shown in Table 1. Other probabilities which
are not shown in the table are given a fair probability of 0.5 and p(A) refers to p(A=true)
which is the probability of A is true.
Symbol assignments
t
f
B
true
false
play ball
R
P
E
red sensation
praise
energy
D
S
play drum
to sleep
PV
positive valence
B
p(R)
D
p(P)
S
p(E)
t
f
0.8
0.5
t
f
0.7
0.5
t
f
0.8
0.2
R
P
p(PV)
t
t
f
f
t
f
t
f
0.9
0.9
0.9
0.5
Table 1. Initial values of the conditional probility tables
The audience (i.e. his friends) can send him a request during the interaction to influence his decision. Each request is interpreted as a 0.05 increased in the prior probability.
Table 2 shows the audience’s influences on the ‘play drums’ behavior, where p(A | B) is
the probability of A given B. Initially, Smokey chooses to play his favorite red ball as X
is the highest value in the first trial. However, he changes his decision to play drums on
the third trial as his belief of getting praise increases because of the requests from the
audience, i.e. the W values increased. The ‘to sleep’ behavior remains in a lower value
because he has a strong embedded emotional judgement in this experiment. Arousal is
used to determine the speeds of the arms movements and the heart shape LEDs beat rate.
Emotion can not only compete with the reasoning, but also reinforce it and vice
versa. For example, if Smokey does not have to work the next day and has a goal to
Trial
W
X
Y
Z
W:
p(P=t | D=t)
X:
p(PV=t | B=t)
1
0.7
0.868
0.858
0.8
Y:
p(PV=t | D=t)
2
0.8
0.872
0.872
0.8
Z:
p(E=t | S=t)
3
0.9
0.876
0.886
0.8
Table 2. Play–sleep evaluation and audience’s influences on play drums behavior
have fun with his friends instead, then both playing ball and playing drums behaviors are
valid and reinforced by the reasoning. Smokey will choose the behavior with the highest
judgement value, which indicates his preference over the other behaviors. In this case,
one behavior may be prefered over the others when there is more than one behavior to
achieve the goals. In ASMO’s emotion model, developers can provide some preferences
to agents which become their personal characteristics.
5. Comparison and Related Works
Conati and Maclaren have provided a brief summary of emotion models that integrate
causes and effects [2]. In addition, Rumbell et al. have also described a recent comparison
of emotion models in autonomous agents [15]. To our understanding, we are not aware of
any emotion models that integrate innate emotion (biological appraisal), learned emotion
(cognitive appraisal) and reasoning which can influence the agent’s behaviors.
A similar probabilistic approach using dynamic decision network has been used to
recognize students’ emotions based on the OCC cognitive appraisal theory [1,2]. Like
ASMO’s emotion model, it contains nodes to represent the situations, such as student’s
goal, interaction pattern, appraisal, and agent’s action nodes. However, this model does
not account the biological aspect of emotion and how emotions and reasoning influence
the agent’s behavior.
Considering the non-cognitive aspect of emotion, Rosis et al. [14] describe a BDI
emotion model that distinguishes cognitive evaluations with intuitive appraisals. They
proposed that cognitive evaluation is a rational judgement that is supported by reasons
whereas intuitive appraisal is a non-rational judgement based on associative learning and
memory instead of justifiable reasons. In ASMO, both of these judgements are considered as cognitive appraisals, because they are based on the knowledge which is learned
due to its relevence and effects to personal well-being whereas biological appraisal is
based on the innate knowledge.
The ASMO’s emotion model to represent and reason preferences shares similarities
with works in preference logic [3]. Instead of using logic, ASMO uses bayesian probability to model the preferences. The desirability in preference logic is similar to the prior
probability in bayesian causal network of ASMO’s emotion model, which is the belief
of a condition will occur in terms of probability.
6. Conclusion
Subjective preferences or knowledge which are typically presented in terms of emotions can be used to complement decision making and to create personal characteristics.
ASMO integrates a reasoning together with innate and learned emotions, which are rep-
resented as biological appraisal and cognitive appraisal into a probabilistic casual network. It allows developers to build autonomous agents that can respond to the environment in a practical manner.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
C. Conati. Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence, 16(7-8):555–575, 2002.
C. Conati and H. Maclaren. Empirically building and evaluating a probabilistic model of user affect.
User Modeling and User-Adapted Interaction, 19(3):267–303, 2009.
C. Domshlak, E. Hüllermeier, S. Kaci, and H. Prade. Preferences in ai: an overview. Artif. Intell.,
175(7-8):1037–1052, May 2011.
A. Drewnowski. Taste preferences and food intake. Annual Review of Nutrition, 17(1):237–253, 1997.
J. T. Larsen and A. P. McGraw. Further evidence for mixed emotions. Personality and Social Psychology
Review, Jan 2011.
R. S. Lazarus. Emotion and adaptation. Oxford University Press, 1994.
C. J. Norris, J. Gollan, G. G. Berntson, and J. T. Cacioppo. The current status of research on the structure
of evaluative space. Biological Psychology, 84(3):422–436, 2010. The biopsychology of emotion:
Current theoretical and empirical perspectives.
R. Novianto, B. Johnston, and M.-A. Williams. Attention in the asmo cognitive architecture. In Proceedings of the First Annual Meeting of the BICA Society, 2010.
R. Novianto and M.-A. Williams. The role of attention in robot self-awareness. In Robot and Human
Interactive Communication, 2009. RO-MAN 2009. The 18th IEEE International Symposium on, pages
1047–1053, Sep 2009.
A. Ortony, G. L. Clore, and A. Collins. The cognitive structure of emotions. Cambridge University
Press, 1990.
J. Pearl. Causality: models, reasoning, and inference. Cambridge University Press, 2009.
J. Posner, J. A. Russell, and B. S. Peterson. The circumplex model of affect: An integrative approach to
affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17:715–734, 2005.
J. Reeve. Understanding motivation and emotion. John Wiley & Sons, 2009.
F. Rosis, C. Castelfranchi, P. Goldie, and V. Carofiglio. Cognitive evaluations and intuitive appraisals:
can emotion models handle them both? In R. Cowie, C. Pelachaud, and P. Petta, editors, EmotionOriented Systems, Cognitive Technologies, pages 459–481. Springer Berlin Heidelberg, 2011.
T. Rumbell, J. Barnden, S. Denham, and T. Wennekers. Emotions in autonomous agents: comparative
analysis of mechanisms and functions. Autonomous Agents and Multi-Agent Systems, 2011.
S. J. Russell and P. Norvig. Artificial intelligence: a modern approach. Prentice Hall series in artificial
intelligence. Prentice Hall, 2010.
K. R. Scherer. Appraisal considered as a process of multilevel sequential checking. In K. R. Scherer,
A. Schorr, and T. Johnstone, editors, Appraisal processes in emotion: theory, methods, research, affective
science, pages 92–120. Oxford University Press, New York, NY, US, 2001.
D. Watson and A. Tellegen. Toward a consensual structure of mood. Psychological Bulletin, 98(2):219–
235, Sep 1985.
P. H. Wolff. The natural history of crying and other vocalizations in early infancy. In B. M. Foss, editor,
Determinants of infant behavior, pages 81–109. Methuen, London, 1969.