1. Representing and Parameterizing Agent Behaviors

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1. Representing and ParameterizingAgent Behaviors

• Jan Allbeck and Norm Badler
• ????? ???????? ?? ??
• 2004 2??

10410898 ? ? ?
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Agenda
sub-title

• 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

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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)

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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
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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 agents 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

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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!!

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Parameterized Action Representation
PAR

• 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

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PAR Architecture
PAR

• 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

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Action Representation
PAR

• 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 ltCONDITION, actiongt statements
• Termination conditions
• A list of conditions which when satisfied
  indicate the completion of the action

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Object Representation
PAR

• 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

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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)

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Personality and Emotions
PAR for Agent Modeling

• Personality ? OCEAN
• Big Five
• Openness
• Conscientiousness
• Extroversion
• Agreeableness
• Neuroticism
• Emotion ? OCC
• Emotion are generated through the agents
  construal of and reaction to the consequence of
  events, actions of agents, aspects of objects

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EMOTE for Displaying Affect
PAR for Agent Modeling

• EMOTE system
• Based on movement observation science
• Laban Movement Analysis (LMA) ? Effort and Shape

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EMOTE Example
PAR for Agent Modeling

• Hitting a balloon
• Differing EMOTE setting

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EMOTE and OCEAN linkage
PAR for Agent Modeling

• Future work in EMOTE system and the motion
  quality recognizer
• Train the system to correlate captured motions
  with actor affect, behavior, mood, and intent

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Interfaces to Representations

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

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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