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Anticipation and Emotion: a low-level approach to Believability

This paper discusses a low-level approach to creating believable behavior in agents by integrating anticipation and emotion. It also explores the integration of uncertainty as a meta-anticipation strategy. The architecture is evaluated and potential future work is discussed.

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Anticipation and Emotion: a low-level approach to Believability

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  1. Anticipation and Emotion:a low-level approach to Believability Carlos Martinho, João Gonçalves, Ana Paiva Instituto Superior Técnico MindRACES, First Review Meeting, Lund, 11/01/2006

  2. Outline • IST MindRACES Scenario • Aibo in the domotic household • Low-level Anticipatory Affective Architecture • Test Environment: Aini • Integrates Anticipation and Attention • Integrates Anticipation and Emotion • Integrates Uncertainty as Meta-Anticipation • Preliminary Evaluation of the Architecture • Future Work in MindRACES MindRACES, First Review Meeting, Lund, 11/01/2006

  3. IST Scenario • Scenario 1 - Task 3: “Looking for an object”: • Takes place in a household environment where Aibo, the synthetic dog, “lives”. Several distractors, will be added to difficult the task and provide with opportunities for Aibo to “play in character” and be evaluated in terms of believability. MindRACES, First Review Meeting, Lund, 11/01/2006

  4. IST Scenario Context • Our Competences: • Provide with an anticipatory affective component, a low-level approach that provides with believable behaviour while an agent is searching for an object. • Simulation only. Need components for real-world integration. • Missing Links: • Learn to find an object in an occluded environment (IDSIA fovea + UW-COGSI learning strategies). • High level anticipatory affective cognitive reasoning component (ISTC-CNR BDI extension with expectation) for dealing with unplausibility, curiosity, cautiousness... • Using reasoning by analogy to complement the search process (NBU). MindRACES, First Review Meeting, Lund, 11/01/2006

  5. Virtual AIBO Toolbox Main purpose is to assess if the physical restrictions of Sony AIBO are adequate for the expression of believable behaviour. MindRACES, First Review Meeting, Lund, 11/01/2006

  6. AIBO Domotic Environment AIBO sleeps near the children while they play in the living room when it senses the arrival of their father from work. Aibo will run to the front door and starts barking, anticipating the arrival of the owner. MindRACES, First Review Meeting, Lund, 11/01/2006

  7. AIBO Domotic Environment AIBO will monitorize the domotic system and will give clues on what relevant events are occuring inside the household. MindRACES, First Review Meeting, Lund, 11/01/2006

  8. Low-Level Architecture (D5.1) Sensor Effector Emotivector (lower-cognition anticipatory affect) SENSATIONS Agent Processing (BDI extension) (higher-cognition anticipatory affect) EMOTIONS MindRACES, First Review Meeting, Lund, 11/01/2006

  9. AINI Environment Testbed Aini anticipatorybelievability MindRACES, First Review Meeting, Lund, 11/01/2006

  10. The Word Puzzle Game MindRACES, First Review Meeting, Lund, 11/01/2006

  11. Anticipation ? 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  12. Anticipation Expected Value 0.5 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  13. Anticipation and Attention Posner 1980 Müller and Rabbit 1989 Expected Value 0.5 0.4  0.3 Sensed Value 0.2 0.2 time SURPRISE = automatic reaction to a mismatch (Castelfranchi 2005) MindRACES, First Review Meeting, Lund, 11/01/2006

  14. Attention in Action [demo] MindRACES, First Review Meeting, Lund, 11/01/2006

  15. Anticipation and Emotion ? 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  16. Anticipation and Emotion Some signals may have a search value MindRACES, First Review Meeting, Lund, 11/01/2006

  17. Anticipation and Emotion search 1.0 current distance ? 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  18. Anticipation and Emotion search 1.0 expected distance current distance 0.5 expected reward 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  19. Anticipation and Emotion search 1.0 sensed distance sensed punishment 0.4 0.3 0.2 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  20. Anticipation and Emotion • Expectated Qualia vs Sensed • Surprise (S) • Positive Increase (S+) • Positive Reduction ($+) • Negative Increase (S-) • Negative Reduction ($-) MindRACES, First Review Meeting, Lund, 11/01/2006

  21. Anticipation and Emotion • Harlow and Stagner (1933) • Emotion versus sensation • Young (1961) • Emotion as process in hedonistic continuum • Hammond (1970) • Existence / absence of stimuli • Millenson (1967) • Intensity versus name • Example of Sensation: $+ • Harlow and Stagner - discontentment • Hammond - Distress • Millenson - negative unconditioned stimulus MindRACES, First Review Meeting, Lund, 11/01/2006

  22. Emotions in Action MindRACES, First Review Meeting, Lund, 11/01/2006

  23. Uncertainty and Salience Management • Emotivector Salience Management Strategies: • Winner takes-all: idle and restrictive • Resource ordering: wasted in low relevance • Treshold limit: which value to use? • Meta-Anticipation MindRACES, First Review Meeting, Lund, 11/01/2006

  24. Uncertainty and Salience Management Model( M ) Error Prediction = Uncertainty Model( S ) System S Environment E Meta-anticipatory System MindRACES, First Review Meeting, Lund, 11/01/2006

  25. Uncertainty and Salience Management predicted error 0.4 0.3 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  26. Uncertainty and Salience Management • Introduces uncertainty as error-prediction (resilient to white noise - Schimidhuber) • Extension to 9 sensations (using neutral-based sensations) non-relevant signal 0.4 0.3 relevant signal 0.2 time MindRACES, First Review Meeting, Lund, 11/01/2006

  27. Prediction • Comparative Evaluation of different algorithms: • Polynomial Extrapolation (cubic curves) • Error-Based learning • Kalman filtering • Statistical Limitation • PID based prediction MindRACES, First Review Meeting, Lund, 11/01/2006

  28. Prediction Winner • Kalman-filtering simplification + 2-phase recirculation algorithm+ statistical limitation MindRACES, First Review Meeting, Lund, 11/01/2006

  29. Evaluation: Word Puzzle Scenario MindRACES, First Review Meeting, Lund, 11/01/2006

  30. Preliminary Evaluation • Believability-oriented evaluation • Pre-evaluation with 10 representants of 5 user-groups: • from 5 to 79 years-old of both sexes • different familiarities with computer systems • Divided the experiment in 2 fases: • Training: navigation, attention, emotion • Word Puzzle: 2 control, 1 emotivector-based, 1 common sense algorithm used in games • All 10 subjects succeeded at the task but (surprisingly) only with emotivector-based approach! MindRACES, First Review Meeting, Lund, 11/01/2006

  31. Future Work • Evaluation of AINI scenario foir believability with users • Assert relevance for the community • Implementation of architecture in AIBO • Evaluation of domotic scenario for believability with users • Integration of high-level Anticipatory Affect (ISTC-CNR) • Integration of real-world search functionality: IDSIA (fovea), UW-COGSI (search) and NBU (analogy) • Evaluation of the integration (real time constraints) MindRACES, First Review Meeting, Lund, 11/01/2006

  32. Questions? MindRACES, First Review Meeting, Lund, 11/01/2006

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