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

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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 ...

Modeling Modeling is not merely a process of behavioral mimicry. Highly functional patterns of behavior, which constitute the proven skills and established customs of a culture, may be adopted in essentially the same form as they are exemplified. There is little leeway for 25 improvisation on how to drive automobiles or to perform arithmetic operations. However, in many activities, subskills must be improvised to suit varying circumstances. Modeling influences can convey rules for generative and innovative behavior as well. This higher-level learning is achieved through abstract modeling. Rule-governed behavior differs in specific content and other details but it contains the same underlying rule. For example, the modeled statements, "The dog is being petted," and "the window was opened" refer to different things but the linguistic rule-- the passive form--is the same. In abstract modeling, observers extract the rule embodied in the specific behavior exhibited by others. Once they lear...

and Creative Modeling Modeling is not simply a process of response mimicry as commonly believed. Modeled judgments and actions may differ in specific content but embody the same rule. For example, a model may deal with moral dilemmas that differ widely in the nature of the activity but apply the same moral standard to them. Modeled activities thus convey rules for generative and innovative behavior. This higher level learning is achieved through abstract modeling. Once observers extract the rules underlying the modeled activities they can generate new behaviors that go beyond what they have seen or heard. Creativeness rarely springs entirely from individual inventiveness. A lot of modeling goes on in creativity. By refining preexisting innovations, synthesizing them into new ways and adding novel elements to them something new is created. When exposed to models of differing styles of thinking and behaving, observers vary in what they adopt from the different sources and thereby create ...

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Cultural Evolutionary Theory 2 Cultural evolutionary theory, like other evolutionary theories, links individual-level and population or society-level phenomena. It provides numerous bridges between social psychology and other disciplines and sub-disciplines. The theory uses mathematical models to understand the population-level consequences of the individual-level processes of individual and social learning. The theory has been used to explain group-level behavior such as cooperation, altruism, and the cross-cultural variation associated with social institutions. The empirical study of social psychological assumptions of such models and experimental tests of cultural-evolutionary hypotheses are in their infancy. Biographic Sketch Brian Paciotti is a 2002 PhD graduate from an interdisciplinary social sciences program at UC Davis where he currently teaches social psychology and conducts research on the influence of religious institutions on altruistic behavior. Peter Richerson and Robert Boyd are both UC Davis PhDs (1969 and 1973 respectively), where Richerson remains in the

graduates be able to “function on multidisciplinary teams ” and “communicate effectively”, the need for effective team skills goes far deeper. One solution is the use of a computationally intelligent “virtual facilitator” that contains a subset of the expert knowledge of a skilled facilitator. The “virtual facilitator ” models behaviors of an expert facilitator to engineering student teams as they are working together. Albert Bandura’s theory of observational learning suggests that skills can be developed through observation of expert “others ” engaged in practice. Preliminary research indicates that students can increase beneficial team behaviors (such as inquiry) through observation and imitation of an expert system. This paper is an extension of a 2005 FIE Work-In-Progress presentation that documented an expert facilitator system. In this study the system is used as part of an hour-long team exercise for engineering students. This study looks at student interactions during the exercise. Measures include analysis of team conversations for instances of imitation of the expert system, as well as a comparison of differences in team performance. The potential for an easily disseminated method to help engineering students learn effective team skills is discussed.

We present an agent-based model of the emergence of fashion like collective behavior, based on a simple abstraction of individuals neuropsychological conditioning. Our results show that fashion like collective behavior can emerge from social interaction and the working of individuals' neuro-psychological mechanisms, within a wide range of plausible assumptions about the levels of social stratification within a population.

Because of the influential role the mass media play in society, understanding the psychosocial mechanisms through which symbolic communication influences human thought, affect and action is of considerable import. Social cognitive theory provides an agentic conceptual framework within which to examine the determinants and mechanisms of such effects. Human behavior has often been explained in terms of unidirectional causation, in which behavior is shaped and controlled either by environmental influences or by internal dispositions. Social cognitive theory explains psychosocial functioning in terms of triadic reciprocal causation (Bandura, 1986). In this transactional view of self and society, personal factors in the form of cognitive, affective, and biological events; behavioral patterns; and environmental events all operate as interacting determinants that influence each other bidirectionally (Figure 1). Social cognitive theory is founded in an agentic perspective (Bandura, 1986; 2001a). People are self-organizing, proactive, self-reflecting, and self-regulating, not just reactive organisms shaped and shepherded by environmental events or inner forces. Human selfdevelopment, adaptation, and change are embedded in social systems. Therefore, personal agency operates within a broad network of sociostructural influences. In these agentic