Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is

The distributed coordination and control of a set of autonomous mobile robots is a problem widely studied in a variety of fields, such as engineering, artificial intelligence, artificial life, robotics. Generally, in these areas the problem is studied mostly from an empirical point of view. In contrast, we aim to understand the fundamental limitations on what a set of autonomous mobile robots can achieve. In this paper we describe the current investigations on what autonomous mobile robots can and cannot do with respect to some coordination problems.

The development of enabling infrastructure for the next generation of multi-agent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multi-agent community. Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a high-level support structure that will be the focus of this paper. A domain-independent (generic) agent architecture is proposed that wraps around an agent’s problem-solving component in order to make problem-solving responsive to real-time constraints, available network resources and the need to coordinate — both in the large and small, with problem-solving activities of other agents. This architecture contains five components, local agent scheduling, multi-agent coordination, organizational design, detection and diagnosis and on-line learning, that are designed to interact so that a range of different situation-specific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the

Scientific research and practice in multiagent systems focuses on constructing computational frameworks, principles, and models for how both small and large societies of intelligent, semiautonomous agents can interact effectively to achieve their goals. This article provides a personal view of the key application areas for cooperative multiagent systems, the major intellectual problems in building such systems, the underlying principles governing their design, and the major directions and challenges for future developments in this field. Index Terms---Multiagent systems, coordination, cooperation, distributed problem solving, distributed artificial intelligence, computational organizations. ------------------------------ ###p### ------------------------------ 1INTRODUCTION ULTIAGENT systems are computational systems in which two or more agents interact or work together to perform some set of tasks or to satisfy some set of goals. These systems may be comprised of homogeneous o...

In this paper we study the problem of gathering in the same location of the plane a collection of identical oblivious mobile robots. Previous investigations have focused mostly on the unlimited visibility setting, where each robot can always see all the other ones, regardless of their distance. In the more difficult and realistic setting where the robots have limited visibility, the existing algorithmic results are only for convergence (towards a common point, without ever reaching it) and only for synchronous environments, where robots’ movements are assumed to be performed instantaneously. In contrast, we study this problem in a totally asynchronous setting, where robots ’ actions, computations, and movements require a finite but otherwise unpredictable amount of time. We present a protocol that allows anonymous oblivious robots with limited visibility to gather in the same location in finite time, provided they have orientation (i.e., agreement on a coordinate system). Our result indicates that, with respect to gathering, orientation is at least as powerful as instantaneous movements.

Abstract. We consider a collection of robots which are identical (anonymous), have limited visibility of the environment, and no memory of the past (oblivious); furthermore, they are totally asynchronous in their actions, computations, and movements. We show that, even in such a totally asynchronous setting, it is possible for the robots to gather in the same location in finite time, provided they have a compass.

In this paper, we study the distributed coordination and control of a set of asynchronous, anonymous, memoryless mobile vehicles that can freely move on a twodimensional plane but cannot communicate among themselves. In particular, we analyze the problem of forming a certain pattern and following a designated vehicle, referred to as the leader, while maintaining the pattern: the ocking problem.

. Cooperation plays a fundamental role in multi-agent systems in which individual agents must interact for the overall system to function effectively. However, cooperation inherently involves an element of risk, due to the unpredictable nature of other's behaviour. In this paper, we consider the information needed by an agent to be able to assess the degree of risk involved in a particular course of action. In particular, we consider how this information can be used in the process of plan selection in BDI-like agents. 1 Introduction BDI agent architectures represent an important class of system that has been used in an increasing number of applications. Based on the folk-psychology mental notions of belief, desire and intention, they are also distinctly popular among the plethora of existing agent architectures. Apart from the intuitive understanding of the BDI model, this popularity may be due to the successful practical application of BDI systems such as PRS [8] and dMARS [5]...

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications.

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