Large-scale Multi-agent Systems Research Articles

Software engineering for large-scale multi-agent systems - SELMAS 2004

This paper is intended to sum up the results of the Third International Workshop on Software Engineering for Large-Scale Multi-Agent System (SELMAS 2004) held in Edinburgh, Scotland, May 24--25, 2004, as part of the International Conference on Software Engineering (ICSE 2004). The main purpose of this workshop was to share and pool the collective experience of people, both academics and practitioners, who are actively working on software engineering for large-scale multi-agent systems. The call for papers elicited some 24 submissions, of which 14 papers were accepted for presentation. A set of selected workshop and invited papers are to appear in a Lecture Notes in Computer Science (LNCS, Springer) volume. The workshop consisted of an opening presentation, two keynote talks, four technical sessions of paper presentations, and two panels. During the workshop we informally reviewed ongoing and previous work and debated a number of important issues. The SELMAS 2004 website can be found at <http://www.teccomm.les.inf.puc-rio.br/semas2004>. We begin by presenting an overview of our goals and the workshop structure, and then focus on the workshop technical program.

Software engineering for large-scale multi-agent systems

This paper is intended to sum up the results of the Second International Workshop on Software Engineering for Large-Scale Multi-Agent Systems (SELMAS'03) held in Portland, Oregon, USA, May 3--4, 2003, as part of the International Conference on Software Engineering (ICSE'03). The main purpose of this workshop was to share and pool the collective experience of people, both academics and practitioners, who are actively working on software engineering for large-scale multi-agent systems. The call for papers elicited some 26 submissions, of which 19 papers were accepted for presentation. A selected set of the workshop papers and invited papers are to appear in the 2 nd edition of the book Software Engineering for Large-Scale Multi-Agent Systems (LNCS, Springer, 2003). The workshop consisted of an opening presentation, several paper presentations organized into five sessions, and three panels. During the workshop we informally reviewed ongoing and previous work and debated a number of important issues. The SELMAS'03 Web page, including the papers and the electronic version of this report, can be found at <www.teccomm.les.inf.puc-rio.br/selmas2003>. We begin by presenting an overview of our goals and the workshop structure, and then focus on the workshop technical program.

A large-scale multi-agent system for navy personnel distribution

In the US Navy, at the end of each sailor's tour of duty, he or she is assigned to a new job. The Navy employs some 280 people, called detailers, full time to effect these new assignments. The intelligent distribution agent (IDA) prototype was designed and built to automate, in a cognitively plausible manner, the job of the human detailers. That model is being redesigned to function as a multi-agent system. This is not a trivial matter due to the fact that there would need to be approximately 350 ‐ 000 individual agents. There are also many issues relating to how the agents interact and how all entities involved, including humans, exercise their autonomy. This paper describes both the IDA prototype and the multi-agent IDA system being created from it. We shall also discuss several of the major issues regarding the design, interaction and autonomy of the various agents involved.

RoboCup rescue: A grand challenge for multiagent and intelligent systems

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.

Emergent cooperative goal-satisfaction in large-scale automated-agent systems

Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation (Conte et al., 1991; Rosenschein, 1986), negotiation (Kraus and Wilkenfeld, 1991), distributed planning (Conry et al., 1988), and coalition formation (Ketchpel, 1994; Sandholm and Lesser, 1997), have provided a variety of approaches and several algorithms and solutions to situations wherein cooperation is possible. However, the case of cooperation in large-scale multi-agent systems (MAS) has not been thoroughly examined. Therefore, in this paper we present a framework for cooperative goal-satisfaction in large-scale environments focusing on a low-complexity physics-oriented approach. The multi-agent systems with which we deal are modeled by a physics-oriented model. According to the model, MAS inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. To enable implementation of the model, we provide a detailed algorithm to be used by a single agent within the system. The model and the algorithm are appropriate for large-scale, dynamic, Distributed Problem Solver systems, in which agents try to increase the benefits of the whole system. The complexity is very low, and in some specific cases it is proved to be optimal. The analysis and assessment of the algorithm are performed via the well-known behavior and properties of the modeling physical system.