Solving Generalized Open Constraint Optimization Problem Using Two-Level Multi-Agent Framework - Improved Results

Publication Type

Conference Paper

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refers to the COP where constraints and variable domains can change over time and agents? opinions have to be sought over a distributed network to form a solution. The openness of the problem has caused conventional approaches to COP such as branch-and-bound to fail to find optimal solutions. OCOP is a new problem and the approach to find an optimal solution (minimum total cost) introduced in [1] is based on an unrealistic assumption that agents are willing to report their options in nondecreasing order of cost. In this paper, we study a generalized OCOP where agents are self-interested and not obliged to reveal their private information such as the order of their options with respect to cost. The objective of the generalized OCOP is to find a solution with low total cost and high overall satisfaction level of agents. A Two-Level Structured Multi-Agent Framework has been proposed: in the upper level, a neutral central solver allows agents report their preferred options in tiers and find a feasible initial solution from top tiers of options by constraint propagation and guided tiers expansion; in the lower level, agents form coalitions and negotiate among themselves on the initial solution by an argument of Persuasive Points. Experimental results have shown that this two-level structure yields very promising results that seek a good balance between the total cost of solution and the agents? overall satisfaction level in the long run.


Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics


European Conference on AI (ECAI) 2006 Workshop on Distributed CSP (DisCSP)

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