Adaptive Behavior, 2 (2)
Adaptive Behavior
Volume 2, Number 2
Fall 1993
Table of Contents
DeLiang Wang
A Neural Model of Synaptic Plasticity Underlying Short-term and Long-term Habituation
Adaptive Behavior, 2 (2), 111-129.
Csaba Szepesvári and András Lörincz
Behavior of an Adaptive Self-organizing Autonomous Agent Working with Cues and Competing Concepts
Adaptive Behavior, 2 (2), 131-160.
Bruce J. MacLennan and Gordon M. Burghardt
Synthetic Ethology and the Evolution of Cooperative Communication
Adaptive Behavior, 2 (2), 161-188.
C. Ronald Kube and Hong Zhang
Collective Robotics: From Social Insects to Robots
Adaptive Behavior, 2 (2), 189-218.
A Neural Model of Synaptic Plasticity Underlying Short-term and Long-term Habituation
By DeLiang Wang
Abstract
It has been demonstrated that short-term habituation may be caused by a decrease in release of presynaptic neurotransmitters and long-term habituation seems to be caused by morphological changes of presynaptic terminals. A parsimonious model of short-term and long- term synaptic plasticity at the electrophysiological level is presented. This model consists of two interacting differential equations, one describing alterations of the synaptic weight and the other describing changes to the speed of recovery (forgetting). The latter exhibits an inverse S-shaped curve whose high value corresponds to fast recovery (short-term habituation) and low value corresponds to slow recovery (long-term habituation). The model has been tested on short-term and a set of long-term habituation data of prey-catching behavior in toads, spanning minutes to hours to several weeks.Key Words
synaptic plasticity; short-term habituation; long-term habituation; modeling; toad
Behavior of an Adaptive Self-organizing Autonomous Agent Working with Cues and Competing Concepts
By Csaba Szepesvári and András Lörincz
Abstract
A brain model-based alternative to reinforcement learning is presented that integrates artificial neural networks and knowledge-based systems into one unit or agent for goal-oriented problem solving. The agent may possess inherited and learned artificial neural networks and knowledge-based subsystems. The agent has and develops ANN cues to the environment for dimensionality reduction (data compression) to ease the problem of combinatorial explosion. Here, a dynamical concept model is put forward that builds cue models of the phenomena in the world, designs dynamical action sets (concepts), and makes them compete in a spreading- activation neural stage to reach decision. The agent works under closed-loop control. Here we examine a simple robotlike object in a two-dimensional conditionally probabilistic space.Key Words
adaptivity; artificial neural networks; knowledge-based system; self-organization; activation spreading; autonomous system
Synthetic Ethology and the Evolution of Cooperative Communication
By Bruce J. MacLennan and Gordon M. Burghardt
Abstract
Synthetic ethology is proposed as a means of conducting controlled experiments investigating the mechanisms and evolution of communication. After a discussion of the goals and methods of synthetic ethology, two series of experiments are described based on at least 5000 breeding cycles. The first demonstrates the evolution of cooperative communication in a population of simple machines. The average fitness of the population and the organization of its use of signals are compared under three conditions: communication suppressed, communication permitted, and communication permitted in the presence of learning. Where communication is permitted, the fitness increases approximately 26 times faster than when communication is suppressed; with communication and learning, the rate of fitness increase is nearly 100-fold. The second series of experiments illustrates the evolution of a syntactically simple language, in which a pair of signals is required for effective communication.Key Words
artificial life; communication; cooperation; entropy; ethology; evolution; genetic algorithm; intentionality; language; learning; synthetic ethology
Collective Robotics: From Social Insects to Robots
By C. Ronald Kube and Hong Zhang
Abstract
Achieving tasks with multiple robots will require a control system that is both simple and scalable as the number of robots increases. Collective behavior as demonstrated by social insects is a form of decentralized control that may prove useful in controlling multiple robots. Nature's several examples of collective behavior have motivated our approach to controlling a multiple robot system using a group behavior. Our mechanisms, used to invoke the group behavior, allow the system of robots to perform tasks without centralized control or explicit communication. We have constructed a system of five mobile robots capable of achieving simple collective tasks to verify the results obtained in simulation. The results suggest that decentralized control without explicit communication can be used to perform cooperative tasks requiring a collective behavior.Key Words
multi-robot; multi-agent; cooperative behavior; collective behavior; autonomous robots
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