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...

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 paper proposes a set of principles and a general architecture that may explain how language and meaning may originate and complexify in a group of physically grounded distributed agents. An experimental setup is introduced for concretising and validating specific mechanisms based on these principles. The setup consists of two robotic heads that watch a scene in which a robot moves around in its ecosystem. The first results from experiments showing the emergence of distinctions, of a lexicon, and of primitive syntactic structures are reported. 1 Introduction Artificial Intelligence research has made remarkable progress the last decades by showing how operations over symbolic models may explain various aspects of intelligent behavior, such as planning, problem solving, natural language processing, etc. However, the problem of the origin of these symbolic models has so far not been adequately addressed. Most of the time it is the programmer who designs formalisms and datastructures, ...

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

this paper I put forward a new approach to understanding the origins of some of the key ingredients in a syntactic system. I show, using a computational model, that compositional syntax is an inevitable outcome of the dynamics of observationally learned communication systems. In a simulated population of individuals, language develops from a simple idiosyncratic vocabulary with little expressive power, to a compositional system with high expressivity, nouns and verbs, and word order expressing meaning distinctions.

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...

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The paper reports on experiments in which robotic agents and software agents are set up to originate language and meaning. The experiments test the hypothesis that mechanisms for generating complexity commonly found in biosystems, in particular self-organisation, co-evolution, and level formation, also may explain the spontaneous formation, adaptation, and growth in complexity of language.

The paper reports experiments to test the hypothesis that language is an autonomous evolving adaptive system maintained by a group of distributed agents without central control. The experiments show how a coherent lexicon may spontaneously emerge in a group of agents engaged in language games and how a lexicon may adapt to cope with new meanings that arise or new agents that enter the group. The lexicon has several characteristics of natural language lexicons, such as polysemy, synonymy and ambiguity. Keywords: origins of language, lexicon acquisition, self-organization. 1 Introduction The origins and evolution of language is still clouded in mystery, despite an extensive literature within linguistics, psychology, anthropology and neurobiology (see a recent overview in [15]). The most common hypothesis being explored in American linguistics is that language is based on a species-specific innate ability (a kind of language organ) and on the refinement of innate knowledge (universal gr...