ALLIANCE is a software architecture that fa- cilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behavior-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.

The paper addresses the question how a group of physically embodied robotic agents may originate meaning and language through adaptive language games. The main principles underlying the approach are sketched as well as the steps needed to implement these principles on physical agents. Some experimental results based on this implementation are presented. 1 Introduction In the past five years, a large number of robotic agents, i.e. physical systems capable of sensori-motor control, have been built in order to investigate a bottom-up approach to artificial intelligence (see the overview in [8]). Important results have been achieved, particularly by using behavior-oriented architectures [14] and learning methods based on neural networks [6] or genetic algorithms [3]. Nevertheless, it is still largely an open question how these robots may reach sufficient complexity in order to qualify as cognitive agents. Most of the experiments have focused on `low level' tasks like obstacle avoidance o...

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

The paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are clarified and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed. 1 Introduction The paper surveys research in which software simulations and experiments with robotic agents are used to explore the viewpoint that language is a complex dynamical system. The main goal of the paper is to outline the approaches and show example experiments. Much more work needs to be done to arrive at a full-fledged theory of the origins of language and even about the work already done much more can be said than is possible in a single paper. Nevertheless, I hope to show that a new exciting approach to the study of the origins and evolution of language is taking shape. The rest of the paper is in four parts. The next section clarifies the notion of a complex system and the multi-agent perspective. Section 3...

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.

. This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed effective networks 9 to 16 times faster than the Adaptive Heuristic Critic and 2 times faster than Q- learning and the GENITOR neuro-evolution approachwithout loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications. Keywords: Neuro-Evolution, Reinforcement Learning, Genetic Algorithms, Neural Networks. 1. Introduction Learning effective decision policies is a difficult problem that appears in m...

This paper proposes a research direction to study the development of `artificial social intelligence' of autonomous robots which should result in `individualized robot societies'. The approach is highly inspired by the `social intelligence hypothesis', derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development ...

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 applies another mechanism than genetic evolution for structure formation to the problem of language formation, namely self-organisation. Self-organisation occurs in complex dynamical systems which are coupled in a particular way. Standard examples are the formation of a termite nest [2] or a path in an ant society. This paper focuses on the formation of vocabularies, i.e. a set of couplings between words and meanings. A common vocabulary will be viewed as a selforganising phenomenon similar to a path in an ant society. Each agent is assumed to create his own vocabulary in a random fashion. But agents are coupled because they must share vocabularies in order to obtain the benefit of cooperating through communication. Agents therefore keep changing their own private vocabulary until it is conform to the common vocabulary. It will be shown that under certain conditions a coherent but still evolving vocabulary emerges. The rest of the paper is in three parts. The first section introduces the kernel mechanism responsible for self-organisation. The second section introduces a spatio-temporal dynamics which ensures that the kernel mechanism copes with combinatorial explosions. The final section reports the results of some simulation experiments. 2 The self-organising kernel

Language is a shared set of conventions for mapping meanings to expressions. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with the expansion of the set of meanings. 1 Introduction How did language originate and how are agents capable to acquire the existing language of their community? Various theories have been offered in the linguistic and cognitive science literature. One theory, proposed and defended by Chomsky and his school, states that grammar is innate [1]. More precisely, there is an innate language acquisition device based on a universal grammar so that the acquisition of a new language is a matter of setting switches (choosing paramete...