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...

Our previous work introduced a methodology for synthesizing and analyzing basic behaviors which served as a substrate for generating a large repertoire of higher--level group interactions (Matari'c 1992, Matari'c 1993). In this paper we describe how, given the substrate, agents can learn to behave socially, i.e. to maximize average individual by maximizing collective benefit. While this is a well--defined problem for rational agents, it is difficult to learn in situated domains. We describe three sources of reinforcement and show their necessity for learning non--greedy social rules. The learning strategy is demonstrated on a group of physical mobile robots learning to yield and share information in a foraging task. 1 Introduction Our previous work focused on analyzing and synthesizing complex group behaviors from simple social interactions between individuals (Matari'c 1992, Matari'c 1993). We introduced a methodology which involved designing a collection of basic behaviors which se...

This paper appeared in Trends in Cognitive Science, Vol. 2, No. 3, March 1998, 82-87.)

This paper discusses the challenges of learning to behave socially in a group of greedy agents. We build on our previous work, which introduced a methodology for synthesizing basic behaviors that serve as a substrate for generating a large repertoire of higher-level group interactions. In this paper we describe how, given the substrate, greedy agents can learn social rules that benefit the group as a whole. While this is a well-defined problem for rational agents, it is less so in dynamic, noisy situated domains. We describe three sources of reinforcement and show their necessity for learning non-greedy social rules. We then demonstrate the learning approach on a group of four mobile robots learning to yield and share information in a foraging task.

This paper presents an approach to describing group behavior using simple local interactions among individuals. We propose that for a given domain a set of basic interactions can be defined which describes a large variety of group behaviors. The methodology we present allows for simplified qualitative analysis of group behavior through the use of shared goals, kin recognition, and minimal communication. We also demonstrate how these basic interactions can be simply combined into more complex compound group behaviors. To validate our approach we implemented an array of basic group behaviors in the domain of spatial interactions among homogeneous agents. We describe some of the experimental results from two distinct domains: a software environment, and a collection of 20 mobile robots. We also describe a compound behavior involving a combination of the basic interactions. Finally, we compare the performance of homogeneous groups to those of dominance hierarchies on the same set of basic ...

This paper raises the question of the connection between embodiment and higher-level cognition which has been eloquently addressed before, but has not yet received much focus in the AI community. The paper then proceeds to break the question down into subparts, and address how each can be approached and studied. Finally, the paper briefly overviews two directions of our work: group behavior and imitative behavior, and describes their relation to the issue of embodiment and cognition. Motivation The role of physical embodiment in cognition has long been the subject of debate. In Artificial Intelligence (AI) it is largely accepted that embodiment has strong implications on the control strategies for generating purposive and intelligent behavior in the world. However, some theories outside AI have proposed that embodiment not only constrains but may also facilitate certain types of higher-level cognition. Evidence from neuroscience allows for postulating shared mechanisms for low-level c...