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제5주. Art and Design
An 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. 955~964, 2001
학습목표
컴퓨터 그래픽스 분야의 캐릭터 자동생성을 위하여 인공생명
의 여러 가지 방법론이 어떻게 적용될 수 있는지 이해
개요
 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
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
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
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
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
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
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
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
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