컴퓨터 그래픽스 분야의 캐릭터 자동생성을 위하여 인공생명의 여러 가지 방법론이 어떻게 적용될 수 있는지 이해 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 인공생명의 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