A Negotiation-Based Coalition Formation Model for Agents with Incomplete Information and Time Constraints

Abstract

In this paper we describe a coalition formation model for a cooperative multiagent system in which each agent has incomplete information about its dynamic and uncertain world and must respond to sensed events within time constraints. With incomplete information and uncertain world parameters while lacking time, an agent cannot afford organizing a rationally optimal coalition formation. Instead, our agents use a two-stage methodology. When an agent detects an event in the world, it first compiles a list of coalition candidates that it thinks would be useful, and then negotiates with the candidates. A negotiation is an exchange of information and knowledge for constraint satisfaction until both parties agree on a deal or one opts out. Each successful negotiation adds a new member to the agent's final coalition. The agent that initiates the coalition needs to determine the task distribution among the members of the coalition and designs its coalition strategy to increase the chance of successfully forming a working coalition. Since the environment is dynamic, noisy, and the agents are resource-constrained, agents must form the working coalition to react to events as soon as possible and with whatever partial information they currently hold.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA461997

Entities

People

  • Leen-kiat Soh

Organizations

  • University of Nebraska-Lincoln Department of Computer Science and Engineering

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Agreements
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Chain Reactions
  • Chemical Reactions
  • Communication Channels
  • Computations
  • Computer Science
  • Frequency
  • Information Exchange
  • Measurement
  • Multiagent Systems
  • Negotiations
  • Operating Systems
  • Reinforcement Learning
  • Target Tracking

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Game Theory.
  • Systems Analysis and Design