Many different problems are called distributed or multiagent planning problems. Existing approaches each deal with only a subset of these problems. Which properties are essential in determining a solution method for multiagent planning problems? We argue that mainly the facts that communication is limited, agents have private goals, and the frequency of the dependencies between agents have a si...

Evidence-based medicine relies heavily on the timely dissemination of ‘best evidence’ to a wide audience of health practitioners (Atkins and Louw, 2000). However, finding, assimilating and using this information resource effectively can be difficult. In this paper we describe an infrastructure for facilitating evidencebased health care using Agora a multi-agent system. This paper discusses a ne...

Current mapping and related geospatial technologies are not designed to support group work and we have a limited theoretical or practical basis from which to extend (or reinvent) technologies for group use of geospatial information. To address the challenge of supporting group work with geospatial information, we have developed a comprehensive conceptual approach to geocollaboration and are app...

Cooperation and learning are two ways in which an agent can improve its performance. Cooperative Multiagent Learning is a framework to analyze the tradeoff between cooperation and learning in multiagent systems. We focus on multiagent systems where individual agents are capable of solving problems and learning using CBR (Case-based Reasoning). We present several collaboration strategies for age...

Over the last decade, methods for multiagent planning under uncertainty have increased in scalability. However, many methods assume value factorization or are not able to provide quality guarantees. We propose a novel family of influence-optimistic upper bounds on the optimal value for problems with 100s of agents that do not exhibit value factorization.

This paper proposes an agent-based collaborative architecture for collaborative work. This architectureis applied to knowledge-drivenprocess management, which often requires quick reconfiguration of applications. Using this architecture, an open multi-agent system is integrated with a virtual collaborativeenvironment. The virtual collaborativeenvironmentprovides the representation and managemen...

This paper presents a new agent-based financial market. It is designed to be both simple enough to gain insights into the nature and structure of what is going on at both the agent and macro levels, but remain rich enough to allow for many interesting evolutionary experiments. The model is driven by heterogeneous agents who put varying weights on past information as they design portfolio strate...

The successful integration and acceptance of many multi-agent systems into daily lives crucially depends on the ability to develop effective policies for adjustable autonomy. Adjustable autonomy encompasses the strategies by which an agent selects the appropriate entity (itself, a human user, or another agent) to make a decision at key moments when an action is required. We present two formulat...