Adaptive Behavior, 5 (1)

In this article, we present a learning mechanism that allows a multiagent system to cooperate to achieve a gathering task efficiently in unknown and changing environments. The multiagent system is a team of autonomous behavior-based agents with limited communication capabilities. Cooperation is based on the acquisition of signaling behaviors and on the specialization of the agents into differents types. Every agent has the same collection of built-in reactive behaviors. Some of the built-in behaviors are fixed, whereas others can be modified through reinforcement learning. The reinforcement signal is delayed until a trial is completed and assesses the collective performance of the team. Each agent uses this common signal to learn what individual behaviors are more suitable for the team. Simulation results, and the corresponding statistical analysis, show that the multiagent system always achieves near-optimal performances.

Key Words

reinforcement learning; multiagent systems; cooperation; specialization

Pages 29-74

Hierarchical Map Building and Self-Positioning with MonaLysa

By Jean-Yves Donnart, Jean-Arcady Meyer

Abstract

This article describes how an animat endowed with the MonaLysa control architecture can build a cognitive map that merges into a hierarchical framework not only topological links between landmarks but also higher-level structures, control information, and metric distances and orientations. The article also describes how the animat can use such a map to locate itself, even if it is endowed with noisy dead-reckoning capacities. MonaLysa's mapping and self-positioning capacities are illustrated by results obtained in three different environments and four noise-level conditions. These capacities appear to be gracefully degraded when the environment grows more challenging and when the noise level increases. In the discussion, the current approach is compared to others with similar objectives, and directions for future work are outlined.

Key Words

hierarchical map; topological information; metric information; landmarks; self-positioning; dead-reckoning; robustness to noise

Pages 75-98

Learning to Adapt to Changing Environments in Evolving Neural Networks

By Stefano Nolfi, Domenico Parisi

Abstract

To study learning as an adaptive process, one must take into consideration the role of evolution, which is the primary adaptive process. In addition, learning should be studied in (artificial) organisms that live in an independent physical environment in such a way that the input from the environment can be at least partially controlled by the organisms' behavior. To explore these issues, we used a genetic algorithm to simulate the evolution of a population of neural networks, each controlling the behavior of a small mobile robot that must explore efficiently an environment surrounded by walls. Because the environment changes from one generation to the next, each network must learn during its life to adapt to the particular environment into which it happens to be born. We found that evolved networks incorporate a genetically inherited predisposition to learn that can be described as (1) the presence of initial conditions that tend to canalize learning in the right directions; (2) the tendency to behave in a way that enhances the perceived differences between different environments and determines input stimuli that facilitate the learning of adaptive changes; and (3) the ability to reach desirable stable states.

Key Words

learning; evolution; environmental changes; adaptation

Pages 99-105

Comment on "Phase Transitions in Instigated Collective Decision Making"

By Eric Bonabeau

Abstract

The aim of this comment is to highlight the clear correspondence between some existing experimental data on cooperative foraging in ants, where it is known that mass recruitment is less flexible than combinations of group and mass recruitment, and the idea of "instigated" decision making (Numaoka, 1995). A simple model accouting for the experimental observations is introduced, and its phase diagram is presented briefly.

Key Words

social insects; self-organization; collective intelligence

back to TOC, back to top

Adaptive Behavior, 5 (1)

Adaptive Behavior

Volume 5, Number 1

Summer 1996

Table of Contents

Janet R. P. Halperin

Guest Editorial

Antonio Murciano and José del R. Millán

Learning Signaling Behaviors and Specialization in Cooperative Agents