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A computational unification of cognitive behavior and emotion Robert P. Marinier III, John E. Laird, Richard L. Lewis Cognitive Systems Research vol. 10,

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Presentation on theme: "A computational unification of cognitive behavior and emotion Robert P. Marinier III, John E. Laird, Richard L. Lewis Cognitive Systems Research vol. 10,"— Presentation transcript:

1 A computational unification of cognitive behavior and emotion Robert P. Marinier III, John E. Laird, Richard L. Lewis Cognitive Systems Research vol. 10, no. 1, pp. 48-69, 2008 2008311760 최봉환

2 PEACTIDM 2 /24

3 Soar : cognitive architecture Cognitive architecture –Task-independent structure and subsystems Soar –For Cognitive modeling –For Real-world application of knowledge-rich intelligent systems –Long-term Memories Procedural, semantic, episodic Associative learning mechanisms (working Memory) 3 /24

4 PEACTIDM in Soar Motor handled by simulation of the environment Decode send selected action to output system Comprehend implemented as a set of comprehend operators Attend implemented as an Attend operatorby PEACTIDM ( only allow a stimuli at a time ) Encoding matching rules in procedural memorygenerate domain-independent augmentations Perceive reception of raw sensory inputs Tasking Create the goal in Short-term Memory 4 /24

5 appraisal theories What can emotion provide? –PEACTIDM and cognitive architectures Describe : processes, constraints and timescale Do not describe : the specific knowledge structures –Much of the information required by PEACTIDM Structure of Encode generate, what information does Attend, information by Comprehend generate, information of Intend use to generate a response  Emotion = the PEACTIDM operations Appraisal theories –Emotions result from the evaluation of the relations ship between goals and situations [Roseman & Smith, 2001] Ref) Parkinson (2009), Marsella and Gratch (2009), and Reisenzein (2009). –Fit naturally into our immediate choice response task Complex cognition = with complex emotion [Smith & Lazarus, 1990] –Discrepancy from Expectation  전구를 끄려고 버튼을 눌렀지만 안 꺼진 경우 Mismatch between the actual state and the expected state Conflicts with the Outcome Probability Feel Surprise Emotion modeling : Introduce 5 /24

6 Scherer’s appraisal theory ( 2001) Features –16 appraisal dimensions 4 groups : relevance, implication, coping potential, normative significance –A continuous space of emotion Provides a mapping from appraisal values to emotion labels Labels  modal emotions –Appraisal are not generated simultaneously –Process model (abstract level) Emotion modeling : in detail 6 /24

7 Integration : Theory How PEACTIDM + Scherer's appraisal theory 7 /24

8 Integration : Implementation(1) Appraisal values Computing the active appraisal frame –Pre-attentive appraisal frames[Gratch and Marsella, 2004] Before Attend : one frame for each stimulus the agent perceives –Attend = select a stimulus –Active frame : selected stimulus associated appraisal frame 8 /24

9 Integration : Implementation(2) Sequences and time courses of appraisals –The appraisals are generated sequentially [Scherer, 2001] –The model implies  avoid error and low efficiency Partially ordered sequences of appraisals Varying time courses for the generation of those appraisals Determining the current emotion –Appraisal Detector [Smith & Kirby, 2001] processes the active frame to determine the current emotion –Supports one active appraisal frame at a time(=only one emotion) –Categorical theories of emotion : fixed number of possible feelings A unique appraisal frame  a unique experience segmenting the space of appraisal frames  Categorical, linguistic labels –Actual representation active appraisal frame: Suddenness = 1.0, Goal Relevance= 1.0, Outcome Probability = 1.0, Conduciveness = 1.0. 9 /24

10 Integration : Implementation(3) Calculating intensity –Summarizes the importance of the emotion –Intensity function [Marinier and Laird, 2007] Limited ranges : single value, should map to [0, 1] No dominant appraisal : multiple values, should dominate the intensity function, generally multiplication is used as combine method [Gratch and Marsella, 2004] Realization principle : expected stimuli should be less intense thant unexpected stimuli [Neal Reilly, 2006]  OP : Outcome Probability, DE : Discrepancy from Expectation, S : Suddennesss UP : Unpredictability, IP : Intrinsic Pleasantness, GR : Goal Relevance, Cond : Conduciveness, Ctrl : Control, P : Power, num_dims : # of dimension 10 /24

11 Integration : Implementation(4) Modeling the task The revised task 11 /24

12 Example : Eaters (Pacman) domain (1) Eaters Domain : an arbitrary # of cycle is required New topic –How previous emotions affect new emotions –The role of Tasking when the ongoing task may be viewed as different subtasks 12 /24

13 Example : PEACTIDM (1) Perception & Encoding –Perception Per direction by Symbolic data –Encoding 4 Cardinal direction : north/south/west/east Each direction has passable, distance to goal The distance to goal – estimated on Manhattan distance 13 /24

14 Example : PEACTIDM (2) Attending –The selection of which stimulus : weighted random choice Weight : the values of the appraisals 14 /24

15 Example : PEACTIDM (3) Comprehension –Additional appraisal values to the active frame Conduciveness : if direction is passable and on the path to the goal then high Control and Power : if direction is passable then high –Specific stimuli determine "natural"  Causal Agent "chance"  Causal Motive "back out" : should not proceed, solve with heuristic method ( dynamic difference reduction ; Newell, Shaw, and Simon, 1960) –Comprehension operators Complete : when can act as stimuli Ignore : control return to attend Tasking (in generelly Managing goals) –Abstracted goal : ex) "go to work" cannot be acted upon directly must be broken down into more concrete compoonents –Concrete goal : ex) "take a step" can be acted upon directly 15 /24

16 Example : PEACTIDM (4) Intending –Intend function : implemented as a Soar operator –If the agent is currently one step away from the goal, then it creates a goal achievement prediction. –Along with the prediction, the agent also generates an Outcome Probability appraisal. Decode and motor –Soar’s standard method of communicating 16 /24

17 Example : Emotion (1) Over a long period of time in this task  how do emotions affect each other over time? Emotion –Many theories : Hudlicka, 2004, Gratch & Marsella, 2004, Damasio, 1994; Damasio, 2003,... Feelings = perception of our emotions Emotion : short-lived Mood : tend to longer –Modeling Feeling : intensity of appraisal frame Emotion : feeling + feeling intensity Mood : "moves" toward the emotion each time step 17 /24

18 Example : Emotion (2) 18 /24

19 Example : The Influence of Emotion, mood and feeling upon behavior Feeling –Additional knowledge to the state representation Current = emotion, Past = mood –Guide control  influence behavior [Forgas, 1999], [Gross & John, 2003] –Integration with action tendencies [Frijda et al., 1989]  included to demonstrate the possibility of feelings influencing behavior and focusing on one aspect of coping coping by giving up on goals Giving up : a kind of Tasking –Emotional feedback  can detect is not making progress toward the goal –Subtask can give up if agents current feeling of Conduciveness is negative Mood : motivation to go 19 /24

20 Evaluation 20 /24

21 Evaluation Result 21 /24

22 Related Work EMA [Gratch and Marsella, 2004] –Emotion and Adaptation –A computational model of a simple appraisal theory implemented in Soar 7 MAMID [Hudlicka, 2004] – Building emotions into a cognitive architecture OCC/Em [Ortony et al, 1988] –OCC model –OCC only briefly touches on mood, but leaves it unspecified Kismet [Breazeal, 2003] – social robot 22 /24

23 Summary (1) Appraisals are a functionally required part of cognitive processing; they cannot be replaced by some other emotion generation theory. (2) Appraisals provide a task-independent language for control knowledge, although their values can be determined by task-dependent knowledge. Emotion and mood, by virtue of being derived from appraisals, abstract summaries of the current and past states, respectively. Feeling, then, augments the current state representation with knowledge that combines the emotion and mood representations and can influence control. (3) The integration of appraisal and PEACTIDM implies a partial ordering of appraisal generation. (4) This partial ordering specifies a time course of appraisal generation, which leads to time courses for emotion, mood and feeling. (5) Emotion intensity is largely determined by expectations and consequences for the agent; thus, even seemingly mundane tasks can be emotional under the right circumstances. (6) In general, appraisals may require an arbitrary amount of inference to be generated 23 /24

24 용어 CSP - Constraint Satisfaction ProblemEBG - Explanation-Based Generalisation EBL - Explanation-Based Learning GOMS - Goals, Operators, Methods, and Selection rules HISoar - Highly Interactive Soar ILP - Inductive Logic Programming NNPSCM - New New Problem Space Computational Model NTD - NASA Test Director PEACTIDM - Perceive, Encode, Attend, Comprehend, Task, Intend, Decode, Move SCA - Symbolic Concept Acquisition 24 /24

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