This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.

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

Recent years have witnessed the birth of a new paradigm for learning environments: animated pedagogical agents. These lifelike autonomous characters cohabit learning environments with students to create rich, face-to-face learning interactions. This opens up exciting new possibilities; for example, agents can demonstrate complex tasks, employ locomotion and gesture to focus students'attention on the most salient aspect of the task at hand, and convey emotional responses to the tutorial situation. Animated pedagogical agents offer great promise for broadening the bandwidth of tutorial communication and increasing learning environments' ability to engage and motivate students. This article sets forth the motivations behind animated pedagogical agents, describes the key capabilities they offer, and discusses the technical issues they raise. The discussion is illustrated with descriptions of a number of animated agents that represent the current state of the art.

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Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demons...

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this article, we introduce periodic team synchronization (PTS) domains as time-critical environments in which agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting. The two main contributions of this article are a flexible team agent structure and a method for inter-agent communication in domains with unreliable, single-channel, low-bandwidth communication. First, the novel team agent structure allows agents to capture and reason about team agreements. We achieve collaboration between agents through the introduction of formations. A formation decomposes the task space defining a set of roles. Homogeneous agents can flexibly switch roles within formations, and agents can change formations dynamically, according to pre-defined triggers to be evaluated at run-time. This flexibility increases the performance of the overall team. Our teamwork structure further includes pre-planning for frequent situations. Second, the novel communication method is designed for use during the lowcommunication periods in PTS domains. It overcomes the obstacles to inter-agent communication in multi-agent environments with unreliable, high-cost, low-bandwidth communication. We fully implemented both the flexible teamwork structure and the communication method in the domain of simulated robotic soccer, and conducted controlled empirical experiments to verify their effectiveness. In addition, our simulator team made it to the semi-finals of the RoboCup-97 competition, in which 29 teams participated.

We describe an approach to intelligent user interfaces, based on the idea of making the computer a collaborator, and an application-independent technology for implementing such interfaces.

We present a new polynomial-space algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multi-agent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a user-specified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel characteristics including the ability for each agent to make local decisions based on currently available information and without necessarily having global certainty. Theoretical analysis shows that Adopt provides provable quality guarantees, while experimental results show that Adopt is significanfly more efficient than synchronous methods. The speedups are shown to be partly due to the novel search strategy employed and partly due to the asynchrony of the algorithm.

Conflict resolution is a critical problem in distributed and collaborative multi-agent systems. Negotiation via argumentation (NVA), where agents provide explicit arguments or justifications for their proposals for resolving conflicts, is an effective approach to resolve conflicts. Indeed, we are applying argumentation in some realworld multi-agent applications. However, a key problem in such applications is that a well-understood computational model of argumentation is currently missing, making it difficult to investigate convergence and scalability of argumentation techniques, and to understand and characterize different collaborative NVA strategies in a principled manner. To alleviate these difficulties, we present distributed constraint satisfaction problem (DCSP) as a computational model for investigating NVA. We model argumentation as constraint propagation in DCSP. This model enables us to study convergence properties of argumentation, and formulate and experimentally compare 16 different NVA strategies with different levels of agent cooperativeness towards others. One surprising result from our experiments is that maximizing cooperativeness is not necessarily the best strategy even in a completely cooperative environment. The paper illustrates the usefulness of these results in applying NVA to multi-agent systems, as well as to DCSP systems in general.