Conflict resolution strategies for nonhierarchical distributed agents
Distributed Artificial Intelligence (Vol. 2)
Constraint-directed negotiation of resource reallocations
Distributed Artificial Intelligence (Vol. 2)
Enabling technology for knowledge sharing
AI Magazine
Coordinating Plans of Autonomous Agents
Coordinating Plans of Autonomous Agents
Distributed Interpretation: A Model and Experiment
IEEE Transactions on Computers
An Operational Semantics for Negotiating Agents
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
On Constraint-Based Reasoning in e-Negotiation Agents
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Distributed Problem Solving and Planning
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
A cooperative approach for composite ontology mapping
Journal on data semantics X
Integrating knowledge through cooperative negotiation: a case study in bioinformatics
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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In our research, we explore the role of negotiation for conflict resolution in distributed search among heterogeneous and reusable agents. We present negotiated search, an algorithm that explicitly recognizes and exploits conflict to direct search activity across a set of agents. In negotiated search, loosely coupled agents interleave the tasks of 1) local search for a solution to some subproblem; 2) integration of local subproblem solutions into a shared solution; 3) information exchange to define and refine the shared search space of the agents; and 4) assessment and reassessment of emerging solutions. Negotiated search is applicable to diverse application areas and problem-solving environments. It requires only basic search operators and allows maximum flexibility in the distribution of those operators. These qualities make the algorithm particularly appropriate for the integration of heterogeneous agents into application systems. The algorithm is implemented in a multi-agent framework, TEAM, that provides the infrastructure required for communication and cooperation.