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1. Representing and Parameterizing Agent Behaviors

This special lecture explores the importance of action representation in creating interactive worlds. It discusses control vs. autonomy, AI-level representation, network simulation, and the PAR architecture. It also covers object representation, personality and emotions, interfaces, and future research.

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1. Representing and Parameterizing Agent Behaviors

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  1. 1. Representing and ParameterizingAgent Behaviors Jan Allbeck and Norm Badler 연세대학교 컴퓨터과학과로봇 공학 특강 2004 2학기 10410898 유 지 오

  2. sub-title Agenda • Introduction • Control vs. Autonomy • AI-Level Representation • Network Simulation • Parameterized Action Representation • PAR Architecture • Action Representation • Object Representation • PAR for Agent Modeling • Personality and Emotions • EMOTE for Displaying Affect • Interfaces to Representations • Conclusions and Future Research

  3. Introduction • The world is complex  difficult to represent… • In order to create an interactive world that meets natural expectations  substantial amount of computer S/W Engineering is required • Graphical depictions, motion models or generators, collision detection and avoidance, communication or synchronization channels, planning and navigation, cognitive modeling, psychosocial and physiological modeling … • An action representation is IMPORTANT!! • In this chapter… • Outline some thing to consider when adopting an action representation • Present a representation, Parameterized Action Representation (PAR)

  4. Control vs. Autonomy • Control • Key-frame animation • Detailed control over the movement of the characters • A time consuming process, required a large storage, specific to a character • Cannot be altered to context  Difficult to… • Interact with objects and other agents • Create transitions between motions • Alter the expression of the motion to new context • Autonomy • Decrease the data, enable context-sensitive actions • Use Inverse kinematics • Motion capture • Example) Jack, DI-Guy (Human Simulation) … Low-level motion representations

  5. AI-Level Representation • High-level representations • Can vary in their purpose and their semantics • Communicative or conversational Agents • Mechanisms to synchronize facial expressions with speech • Extract semantic information from text • Perform autonomously in a virtual world • Concentrate on an agent’s interactions and autonomy • Planning for characters in virtual environments • Require representations of the state of the environment (dynamic)  Object must also be represented • Cognitive and social modeling • Emotional states, goals, motivations, and more…

  6. Network Simulations • Design dimensions for distributed or networked simulations • Bandwidth, synchronization, agent autonomy, agent control, latency, visualization, interfaces… • Trade off • Ex) Minimize bandwidth vs. maximize control • Packets describing agent actions must be formulated, sent, received, and interpreted • Increasing the autonomy  decreasing in necessary bandwidth • Frame-by-frame joint angle vs. string “enter the building” • “enter the building + carefully + through the blue door” • Modification the detailed joint or motion capture data is IMPOSSSIBLE!! • If the actions are suitably parameterized  POSSIBLE!!

  7. PAR Parameterized Action Representation • PAR allows an agent to act, plan, and reason • A knowledge base and intermediary between natural language and animation • Specify (parameterize) the agent • Any relevant objects, information about paths, locations, manners, and purposes

  8. PAR PAR Architecture • Actionary  stores uninstantiated PARs (UPARs) • Agent Process  create instantiated PARs (IPARs) • Consider emotion, personality factors, current state of the world • Motion Generators  simply replay stored joint angle data or alter this data for context or affect

  9. PAR Action Representation • Include fields for low-level animation concepts • Kinematics, dynamics, … • Participants • Object or other agents involved in the action or can be affected by it • Applicability conditions • True  can perform the action • Preparatory specifications • A list of <CONDITION, action> statements • Termination conditions • A list of conditions which when satisfied indicate the completion of the action

  10. PAR Object Representation • Stored Actionary • Virtual world created  retrieve object from the actionary  instantiated  placed  updated throughout the simulation • Associated with a graphical model in a scene graph • Many of the fields can be filled in as the simulation begins • Ex) bounding volume • Help orient actions that involve objects

  11. Funge et al[19],hierarchy of computer graphics modeling PAR for Agent Modeling • PAR and PARSYS enable each level • Geometric PAR representsand PARSYS automatically recognizes • Kinematics and dynamics (physical) explicitly represented in PAR • Behavioral component World model + agent processes+ motion generators in PARSYS • Cognitive modeling PARSYS contains mechanismsfor planning and also filtering and prioritizing the actions • Individualizing the agent • Use conditions (Actionary)

  12. PAR for Agent Modeling Personality and Emotions • Personality  OCEAN • “Big Five” • Openness • Conscientiousness • Extroversion • Agreeableness • Neuroticism • Emotion  OCC • Emotion are generated through the agent’s construal of and reaction to the consequence of events, actions of agents, aspects of objects

  13. PAR for Agent Modeling EMOTE for Displaying Affect • EMOTE system • Based on movement observation science • Laban Movement Analysis (LMA)  Effort and Shape

  14. PAR for Agent Modeling EMOTE Example • Hitting a balloon • Differing EMOTE setting

  15. PAR for Agent Modeling EMOTE and OCEAN linkage • Future work in EMOTE system and the motion quality recognizer • Train the system to correlate captured motions with actor affect, behavior, mood, and intent

  16. Interfaces to Representations • Basic scripting languages • Create outline to perform … • Specified action • Specified time • Drag-and-drop creation applications • For virtual environments • Natural language

  17. Conclusions and Future Research • An action representation • Autonomy and control • Minimize data storage • Provide semantic for planning • Level of detail • Nearby action: Inverse kinematics • Further distance: replaying motion capture data • Cognitive representation for conveying action information between agents • Flexible representation • Different types of information • Trade-off • Parameterization specificity vs. program complexity • Future work • PAR to XML representation • EMOTE parameterization  models of personality and emotion • Natural language interface

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