From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive BehaviorJean-Arcady Meyer, H. L. Roitblat, Stewart W. Wilson MIT Press, 1993 - 523 sivua More than sixty contributions in From Animals to Animats 2 byresearchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fieldsinvestigate behaviors and the underlying mechanisms that allow animals and, potentially, robots toadapt and survive in uncertain environments. Jean-Arcady Meyer is Director of Research, CNRS, Paris.Herbert L. Roitblat is Professor of Psychology at the University of Hawaii at Manoa. Stewart W.Wilson is a scientist at The Rowland Institute for Science, Cambridge,Massachusetts. Topics covered: The Animat Approach to Adaptive Behavior,Perception and Motor Control, Action Selection and Behavioral Sequences, Cognitive Maps and InternalWorld Models, Learning, Evolution, Collective Behavior. |
Sisältö
The Use of Hierarchies for Action Selection | 138 |
Two Methods for Hierarchy Learning in Reinforcement Environments | 148 |
Coordinating Biological Needs with | 156 |
Behavior Networks and Force Fields for Simulating Spinal Reflex Behaviors of the Frog | 172 |
The Ariadnes Clew Algorithm | 182 |
Dynamic Selection of Action Sequences | 189 |
Planning Simple Trajectories Using Neural Subgoal Generators | 196 |
A Note on RateSensitive Habituation | 203 |
Evolutionary Learning of Predatory Behaviors Based on Structured Classifiers | 356 |
Issues in Evolutionary Robotics | 364 |
Evolving Visually Guided Robots | 374 |
An Evolved VisionBased Behavioral Model of Coordinated Group Motion | 384 |
Evolution of Herding Behavior in Artificial Animals | 393 |
An Evolutionary Approach to Cognition | 400 |
Emergence of NestBased Foraging Strategies in Ecosystems of Neural Networks | 410 |
Evolving Artificial Insect Brains for Artificial Compound Eye Robotics | 425 |
A Directional Spreading Activation Network for Mobile Robot Navigation | 218 |
Memorizing and Representing Route Scenes | 225 |
Building LongRange Cognitive Maps Using Local Landmarks | 233 |
An Adaptive Neural Network | 243 |
Modeling Nervous System Function with a Hierarchical Network | 254 |
An OptimizationBased Categorization of Reinforcement Learning Environments | 262 |
Reinforcement Learning with Hidden States | 271 |
Efficient Learning and Planning within the Dyna Framework | 281 |
Increasing Behavioural Repertoire in a Mobile Robot | 291 |
Learning Biped Robot Obstacle Crossing | 298 |
Learning to Control an Autonomous Robot by Distributed Genetic Algorithms | 305 |
Temporary Memory for Examples Can Speed Learning in a Simple Adaptive System 31 3 | 313 |
Implementing Inner Drive Through Competence Reflection | 321 |
Dynamic Flight Control with Adaptive Coarse Coding | 327 |
Learning via Task Decomposition | 337 |
Neural Networks with Motivational Units | 346 |
From Local Interactions to Collective Intelligence | 432 |
Some Experiments with Foraging Robots | 451 |
Collective Choice of Strategic Type | 469 |
Dimensions of Communication and Social Organization in MultiAgent Robotic Systems | 486 |
Action Selection and Learning in MultiAgent Environments | 502 |
Comparing Robot and Animal Behavior | 517 |
1 | |
FALLIBILITY AND CORRIGIBILITY | |
Comparative Statics | |
Truth as a Value in Inquiry | |
Four Types of Revision | |
Free Speech | |
61 | 14 |
62 | 21 |
Yleiset termit ja lausekkeet
ACP network action activation Adaptive Behavior agent animal Animats appetitive approach architecture Artificial Artificial Intelligence autonomous barrier basilar membrane biological cells classical conditioning cognitive complex computational connections coordination creatures cues defined depth described direction directional mate distance dolphin dynamics echolocation encode environment Equation evolution evolutionary evolved example experiments exploration force function Genetic Algorithms goal hierarchy implemented inhibitory interaction internal landmarks Machine Learning Maes mate preferences mechanisms method mobile robot modules motor centers move movement navigation neural network neurons node objects obstacle optimal organisms output parameters path pattern performance phenotype position predator pretectum prey problem Q-learning real robot receptive field reinforcement centers reinforcement learning representation represents route schema sensors sequence sexual selection shows signal simple simulation spatial step stereopsis stimuli strategy structure subgoal surface target task threshold tion units vector velocity visual weight
Viitteet tähän teokseen
Mind as Motion: Explorations in the Dynamics of Cognition Robert F. Port,Timothy Van Gelder Rajoitettu esikatselu - 1995 |
Comparative Approaches to Cognitive Science H. L. Roitblat,Jean-Arcady Meyer Rajoitettu esikatselu - 1995 |