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5. Art and Design An Artificial Life Approach for the Animation of Cognitive Characters

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Behavioral control : finite state machine optimized by genetic algorithm ... Vision and audition neural networks (3-layer NN) to recognize pyramids and cubes ... – PowerPoint PPT presentation

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Title: 5. Art and Design An Artificial Life Approach for the Animation of Cognitive Characters


1
?5?. Art and DesignAn Artificial Life Approach
for the Animation of Cognitive Characters
  • F.R. Miranda, J.E. Kogler Jr, E.D.M. Hernandez
    and M.L. Netto, Computers Graphics, vol. 25,
    pp. 955964, 2001
  • ????
  • ??? ???? ??? ??? ????? ??? ????? ?? ?? ???? ???
    ??? ? ??? ??

2
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  • Cognitive character animation
  • Perception neural networks
  • Behavioral control finite state machine
    optimized by genetic algorithm
  • WOXBOT/ARENA research project
  • Build virtual worlds where small robots perform
    tasks with their own motivation and reasoning
  • Open distributed object architecture for
  • Evolutionary computation, artificial life,
    pattern recognition, artificial intelligence,
    cognitive neuroscience, distributed objects
    architecture

3
Introduction
  • Goal of artificial life Computational model of
    complex behavior
  • Simulation of macroscopic behavioral aspects of
    living beings using microscopically simple
    components
  • Virtual characters whose behavior emerges from
    hierarchical and functionally specialized complex
    structures
  • Artificial life worlds
  • Virtual places where animated characters interact
    with the environment and with other virtual
    beings of the same or distinct categories
  • Behavioral animation characters have some degree
    of autonomy to decide their actions
  • Cognitive animation
  • AI evolutionary computation graphics
  • Perform tasks not explicitly specified
  • Adaptive algorithms evolutionary programming
    AI

4
Introduction (2)
  • ARENA Fig. 1
  • Artificial environment for animated virtual
    characters
  • Goal obtain virtual creatures capable of
    performing specified tasks in their environment
    by exploring certain strategies and adapting them
  • WOXBOT Fig. 2 (Wide Open eXtensible roBOT)
  • Vision system simulated camera and neural
    network
  • Motor system finite state machine optimized by
    GA
  • Motion forward, backward, turn left, turn right
  • Purposes
  • Behavior modeling lab of learning algorithms
  • Research on societies of virtual characters
  • Study of collective dynamics of populations

5
Artificial Life
  • A-life in computer science
  • Cellular automata theory ? computer graphics
    animation
  • Universal life concept ? evolution and natural
    selection concepts
  • GA with mutation and combination to change one
    generation to another
  • Keeping greater energy, living more time,
    performing tasks faster,
  • Use of knowledge ? intelligent behavior
  • Present in environment and in conception of
    creatures
  • Used by creatures when performing actions

6
Intelligent Agents
  • Definition
  • Computational entities of autonomous behavior in
    agent design, environment, goals and motivations
  • Life features able to sense world, analyze the
    information, able to express the decisions
    through actions
  • Sensing and perception
  • Vision and audition ? neural networks (3-layer
    NN) to recognize pyramids and cubes
  • Sensing ? rendering better images
  • Perception ? adjusting NN size and training
  • Behavior reasoning and acting by learning and
    evolution
  • Learning and evolution through generations
  • Natural selection

7
Project Overview
  • Requirements
  • Mathematical and computational models
  • Efficient environment and platform
  • Constant evolution and improvement
  • ARENA implementation
  • Distributed communicating objects on
    microcomputer cluster
  • Parallelism through multithreading
  • Floor and walls, objects (obstacles, barriers,
    traps, shelters, energy or food sources, )
  • WOXBOT task is to keep itself alive as long as
    it can
  • Major ingredients
  • Sensing and perception, knowledge use and
    evolution

8
Current Implementation Model
  • Sensing and perception (pattern recognition)
  • Nutrients (yellow pyramids) and hurting entities
    (red cubes)
  • JAVA3D ? 3 color channels of RGB Fig. 3
  • 2 specialized neural networks for 4 outputs
    Table 1
  • ANN-I targeting nutrients (identification of
    yellow pyramids)
  • ANN-II targeting hurting entities (for red
    cubes)
  • 1 hidden layer with 8 nodes
  • Use of samples of similar sizes no scale
    invariant
  • Action and behavior learning and reasoning
  • Finite state machine
  • Input combination of ANN-I and ANN-II (Table 2)
  • Output 00 (turn left), 01(go straight), 10 (turn
    right), 11 (go back)
  • Evolving FSM from initial random structure with
    life duration
  • Chromosome entry for 16 possible inputs (next
    state, action code)
  • Fig. 4 5

9
Implementation Issues
  • Real-time application?
  • Maya, Softimage ? good API ? bad user interaction
  • Choice of API
  • Portability, share ability, open architecture,
    easy of use
  • JAVA PL, JAVA3D for graphics
  • Scene-graph oriented approach ? shortening
    development time
  • Environment ARENA
  • Green rectangular floor-plan, four blue walls,
    red cubic boxes, yellow pyramids
  • Looking for pyramids while avoiding cubes
  • Character WOXBOT
  • FSM optimized by GA
  • States, inputs and actions

10
Simulation Results and Further Work
  • GA parameters
  • 16 individuals, 30 generations
  • Half random generation, half crossover and
    mutation (0.06)
  • Fig. 7 8
  • Fig. 9 JAVA3D to develop ARENA/WOXBOT project
  • Limitations
  • No memory of the previous actions ? no learning
  • Low number of states ? only 4
  • Size variant
  • Distribution of objects
  • Textures
  • Multiple WOXBOTs
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