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...

A complete understanding of communication, language, intentionality and related mental phenomena will require a theory integrating mechanistic explanations with ethological phenomena. For the foreseeable future, the complexities of natural life in its natural environment will preclude such an understanding. An approach more conducive to carefully controlled experiments and to the discovery of deep laws of great generality is to study synthetic life forms in a synthetic world to which they have become coupled through evolution. This is the approach of synthetic ethology. Some simple experiments in synthetic ethology are described, in which we have observed the evolution of communication in a population of simple machines. We show that even in these simple worlds communication manifests some of the richness and complexity found in natural communication. Finally some future directions for research in synthetic ethology are discussed, as well as some issues relevant to both synthetic ethol...

This paper describes the problem of task sharing between two autonomous six--legged robots. The robots are equipped with two-way communication, object and goal sensing, and a repertoire of basic behaviors. The performance on the selected task, pushing an elongated box toward a goal region, is difficult for a single robot and improves significantly when performed cooperatively. The cooperative solution, however, requires careful coordination between the robots. We present an approach that takes advantage of a simple communication protocol to compensate for the robots' noisy and uncertain sensing and actuation, and their partial knowledge about the world. We demonstrate our approach on a series of experiments with two physical robots. 1 Introduction The work in this paper is concerned with the problem of mobile robot task sharing. In particular, we are focusing on manipulation tasks with sufficiently challenging dynamics to require the careful cooperation of two (or more) robots. In m...

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...

Coordinated motion in a group of simulated critters can evolve under selection pressure from an appropriate fitness criteria. Evolution is modeled with the Genetic Programming paradigm. The simulated environment consists of a group of critters, some static obstacles, and a predator. In order to survive, the critters must avoid collisions (with obstacles as well as with each other) and must avoid predation. They must steer a safe path through the dynamic environment using only information received through their visual sensors. The arrangement of visual sensors, as well as the mapping from sensor data to motor action is determined by the evolved controller program. The motor model assumes an innate constant forward velocity and limited steering.

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...

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.

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.