Innate and Learned Emotion Network

Transcription

1 Innate and Learned Emotion Network Rony NOVIANTO a and Mary-Anne WILLIAMS b a rony@ronynovianto.com b mary-anne@themagiclab.org 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 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

3 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 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-

4 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 true R red sensation B p(r) D p(p) S p(e) f false P praise t 0.8 t 0.7 t 0.8 B play ball E energy f 0.5 f 0.5 f 0.2 D play drum PV positive valence S to sleep Table 1. Initial values of the conditional probility tables R P p(pv) t t 0.9 t f 0.9 f t 0.9 f f 0.5 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

5 W: p(p=t D=t) Trial W X Y Z X: p(pv=t B=t) Y: p(pv=t D=t) Z: p(e=t S=t) 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-

6 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] C. Conati. Probabilistic assessment of user s emotions in educational games. Applied Artificial Intelligence, 16(7-8): , [2] C. Conati and H. Maclaren. Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction, 19(3): , [3] C. Domshlak, E. Hüllermeier, S. Kaci, and H. Prade. Preferences in ai: an overview. Artif. Intell., 175(7-8): , May [4] A. Drewnowski. Taste preferences and food intake. Annual Review of Nutrition, 17(1): , [5] J. T. Larsen and A. P. McGraw. Further evidence for mixed emotions. Personality and Social Psychology Review, Jan [6] R. S. Lazarus. Emotion and adaptation. Oxford University Press, [7] 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): , The biopsychology of emotion: Current theoretical and empirical perspectives. [8] 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, [9] R. Novianto and M.-A. Williams. The role of attention in robot self-awareness. In Robot and Human Interactive Communication, RO-MAN The 18th IEEE International Symposium on, pages , Sep [10] A. Ortony, G. L. Clore, and A. Collins. The cognitive structure of emotions. Cambridge University Press, [11] J. Pearl. Causality: models, reasoning, and inference. Cambridge University Press, [12] 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: , [13] J. Reeve. Understanding motivation and emotion. John Wiley & Sons, [14] 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, Emotion- Oriented Systems, Cognitive Technologies, pages Springer Berlin Heidelberg, [15] 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, [16] S. J. Russell and P. Norvig. Artificial intelligence: a modern approach. Prentice Hall series in artificial intelligence. Prentice Hall, [17] 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 Oxford University Press, New York, NY, US, [18] D. Watson and A. Tellegen. Toward a consensual structure of mood. Psychological Bulletin, 98(2): , Sep [19] P. H. Wolff. The natural history of crying and other vocalizations in early infancy. In B. M. Foss, editor, Determinants of infant behavior, pages Methuen, London, 1969.