This thesis addresses situated, embodied agents interacting in complex domains. It focuses on two problems: 1) synthesis and analysis of intelligent group behavior, and 2) learning in complex group environments. Basic behaviors, control laws that cluster constraints to achieve particular goals and have the appropriate compositional properties, are proposed as effective primitives for control and learning. The thesis describes the process of selecting such basic behaviors, formally specifying them, algorithmically implementing them, and empirically evaluating them. All of the proposed ideas are validated with a group of up to 20 mobile robots using a basic behavior set consisting of: safe--wandering, following, aggregation, dispersion, and homing. The set of basic behaviors acts as a substrate for achieving more complex high--level goals and tasks. Two behavior combination operators are introduced, and verified by combining subsets of the above basic behavior set to implement collective flocking, foraging, and docking. A methodology is introduced for automatically constructing higher--level behaviors

This paper proposes the concept of basis behaviors as ubiquitous general building blocks for synthesizing artificial group behavior in multi--agent systems, and for analyzing group behavior in nature. We demonstrate the concept through examples implemented both in simulation and on a group of physical mobile robots. The basis behavior set we propose, consisting of avoidance, safe--wandering, following, aggregation, dispersion, and homing, is constructed from behaviors commonly observed in a variety of species in nature. The proposed behaviors are manifested spatially, but have an effect on more abstract modes of interaction, including the exchange of information and cooperation. We demonstrate how basis behaviors can be combined into higher--level group behaviors commonly observed across species. The combination mechanisms we propose are useful for synthesizing a variety of new group behaviors, as well as for analyzing naturally occurring ones. Key words: group behavior, robotics, eth...

The problem of synthesizing and analyzing collective autonomous agents has only recently begun to be practically studied by the robotics community. This paper overviews the most prominent directions of research, defines key terms, and summarizes the main issues. Finally, it briefly describes our approach to controlling group behavior and its relation to the field as a whole.

In this paper, we experiment, from the point of view of their efficiency, different implementations of the "explorer robots application". Three types of "Tom Thumb robots", whose behavior is based on the foraging behaviors of ants are proposed and their results are critically examined. We then introduce chain-making robots (the "dockers"), governed by local perceptions and interactions. This helps us to show that only a few changes in the robots' behavior may greatly improve the efficiency of the population. Introduction In the research conducted in the field of swarm intelligence, the "explorer robots application" appears to be one of the most common examples found to illustrate the capacity of a population of poorly intelligent creatures to handle with a global goal. In this case study, the goal is to make a team of robots find and collect samples in an unpredictable environment and take them back to a home base. These robots usually operate independently and behave in a quite simpl...

This work demonstrates the application of the distributed behavior-based approach [1] to generating a multi-robot controller for a group of mobile robots performing a clean-up and collection task. The paper studies a territorial approach to the task in which the robots are assigned individual territories that can be dynamically resized if one of the robots missfunctions, permitting the completion of the task. The described controller is implemented on a group of four IS Robotics R2e mobile robots. Using a collection of experimental robot data, we empirically derive and demonstrate most effective foraging in our domain, and show the decline of performance of the space division strategy with increased group size.

This work demonstrates the application of the behavior--based approach to generating ethologically--inspired adaptive foraging using a division of labor into exclusive spatial territories. First, we use fixed group sizes to evaluate and compare the performance of the two types of adaptive solutions. Second, using a collection of experimental robot data, we empirically derive and demonstrate the critical mass for most effective foraging in our domain, and show the decline of performance of the space division strategy with increased group size. 1 Introduction This paper describes work that applies the behavior-- based approach for autonomous agent control to generating ethologically--inspired adaptive foraging. In contrast to foraging methods explored by various researchers to date (see section 3 for an overview), we employ a territorial principle that implements a division of labor into exclusive spatial areas. First, we use fixed group sizes of two, three, and four robots to evaluate...

Because we think that plans or strategies are useful for coordinating multiple agents, and because we hypothesize that most of the plans we use are build partly by us and partly by our immediate environment (which includes other agents), this paper is devoted to the conditions in which strategies can be viewed as the result of interactions between simple agents, each of them having only local information about the state of the world. Our approach is based on the study of some examples of reactive agents applications. Their features arebriefly described and we underline, in each of them, what we call the emergent strategies obtained from the local interactions between the agents. Three examples are studied this way: the eco-problem-solving implementations of Pengi and the N-Puzzle, and the sociogenesis process occuring in the artificial ant colonies that compose the MANTA project. We then consider a typical strategical game (chess), and see how to decompose it through a distributed reac...

Abstract not found

This work demonstrates the application of the behavior--based approach to generating ethologically--inspired adaptive foraging using a division of labor into exclusive spatial territories. First, we use fixed group sizes to evaluate and compare the performance of the two types of adaptive solutions. Second, using a collection of experimental robot data, we empirically derive and demonstrate the critical mass for most effective foraging in our domain, and show the decline of performance of the space division strategy with increased group size.

In this paper we present a new optimization algorithm based on a model of the foraging behavior of a population of primitive ants (Pachycondyla apicalis). These ants are characterized by a relatively simple but efficient strategy for prey search in which individuals hunt alone and try to cover a given area around their nest. The ant colony search behavior consists of a set of parallel local searches on hunting sites with a sensitivity to successful sites. Also, their nest is periodically moved. Accordingly, the proposed algorithm performs parallel random searches in the neighborhood of points called hunting sites. Hunting sites are created in the neighborhood of a point called nest. At constant intervals of time the nest is moved, which corresponds to a restart operator which re-initializes the parallel searches. We have applied this algorithm, called API, to numerical optimization problems with encouraging results. 2000 Elsevier Science B.V.