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Asymmetric Distributed Constraint Optimization
"... Abstract. The standard model of distributed constraints optimization problems (DCOPs), assumes that the cost of a constraint is checked by one of the agents involved in the constraint. For DCOPs this is equivalent to the assumption that each constraint has a global cost which applies to each of the ..."
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Abstract. The standard model of distributed constraints optimization problems (DCOPs), assumes that the cost of a constraint is checked by one of the agents involved in the constraint. For DCOPs this is equivalent to the assumption that each constraint has a global cost which applies to each of the participating agents and in other words that all constraints are symmetric. Many multi agent system (MAS) problems involve asymmetric constraints. For example, the gain from a scheduled meeting of two agents is naturally different for each of the participants. In order to solve Asymmetric DCOPs (ADCOPs), one needs to design algorithms in which all agents participating in a constraint independently check the gain for each of them. This naturally brings up the question of privacy, enabling agents to keep their cost (or gain) of constraints private, at least partially. The present paper presents search algorithms for ADCOPs which handle asymmetric constraints in a privacy preserving manner. New versions of Asynchronous Forward Bounding and of Synchronous Branch & Bound are proposed. In addition, two local search algorithms are presented in which agents negotiate moves prior to assigning values. All algorithms are empirically evaluated, and their performance in terms of runtime, network load and solution quality is presented.
UDC 519.876.3, DOI:10.2298/CSIS100118012G Cost of Cooperation for Scheduling Meetings
"... Abstract. Scheduling meetings among agents can be represented as a game the Meetings Scheduling Game (MSG). In its simplest form, the twoperson MSG is shown to have a price of anarchy (PoA) which is bounded by 0.5. The PoA bound provides a measure on the efficiency of the worst Nash Equilibrium in ..."
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Abstract. Scheduling meetings among agents can be represented as a game the Meetings Scheduling Game (MSG). In its simplest form, the twoperson MSG is shown to have a price of anarchy (PoA) which is bounded by 0.5. The PoA bound provides a measure on the efficiency of the worst Nash Equilibrium in social (or global) terms. The approach taken by the present paper introduces the Cost of Cooperation (CoC) for games. The CoC is defined with respect to different global objective functions and provides a measure on the efficiency of a solution for each participant (personal). Applying an “egalitarian ” objective, that maximizes the minimal gain among all participating agents, on our simple example results in a CoC which is non positive for all agents. This makes the MSG a cooperation game. The concepts are defined and examples are given within the context of the MSG. Although not all games are cooperation games, a game may be revised by adding a mediator (or with a slight change of its mechanism) so that it behaves as a cooperation game. Rational participants can cooperate (by taking part in a distributed optimization protocol) and receive a payoff which will be at least as high as the worst gain expected by a game theoretic equilibrium point.