Year: 1997
Type: article
Abstract: Achieving effective cooperation in a multi-agent system is a difficult problem for a number of reasons such as limited and possibly out-dated views of activities of other agents and uncertainty about the outcomes of interacting non-local tasks. In this paper, we present a learning algorithm that endows agents with the capability to choose the appropriate coordination algorithm from a set of available coordination algorithms based on meta-level information about their problem solving situations. We present empirical results that strongly indicate the effectiveness of the learning algorithm. 1 Introduction Coordination is the act of managing interdependencies in a multi-agent system[Decker & Lesser, 1995] . Achieving effective coordination in a multi-agent system (MAS) is a difficult problem for a number of reasons. An agent's local control decisions about what activity to do next or what information to communicate and to whom or what information to ask others may be inappropriate or su...
Authors: Munaga V. N. K. Prasad, Victor Lesser
Institution: University of Massachusetts Amherst
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1598724299