Transcript Document
1
1. Representing and Parameterizing Agent Behaviors Jan Allbeck and Norm Badler
연세대학교 컴퓨터과학과 로봇 공학 특강
2004 2
학기
10410898
유 지 오
2
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
sub-title
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)
Control vs. Autonomy
4 • •
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
bandwidth decreasing in necessary
–
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
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
PAR
PAR Architecture
PAR 8 • • •
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
–
A list of conditions which when satisfied indicate the completion of the action
Object Representation
PAR 10 • • • • •
Stored Actionary Virtual world created
instantiated
placed
retrieve object from the actionary
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
PAR for Agent Modeling
•
PAR and PARSYS enable each level
–
Geometric
PAR represents and PARSYS automatically recognizes
–
Kinematics and dynamics (physical)
explicitly represented in PAR
– –
Behavioral component
World model + agent processes + motion generators in PARSYS Cognitive modeling
Funge et al[19], hierarchy of computer graphics modeling PARSYS contains mechanisms for planning and also filtering and prioritizing the actions
Individualizing the agent
Use conditions (Actionary)
12
Personality and Emotions
•
Personality
OCEAN
–
“Big Five”
Openness
Conscientiousness
Extroversion
Agreeableness
Neuroticism
PAR for Agent Modeling •
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
EMOTE Example
•
Hitting a balloon
–
Differing EMOTE setting
PAR for Agent Modeling
PAR for Agent Modeling
EMOTE and OCEAN linkage
15 •
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
Natural language interface models of personality and emotion