Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private k...
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Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents' privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents' privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.
Many agent coordination problems can be modeled as distributed constraint optimization (DCOP) problems. ADOPT is an asynchronous and distributedsearch algorithm that is able to solve DCOP problems optimally. In this ...
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ISBN:
(纸本)9783642032509
Many agent coordination problems can be modeled as distributed constraint optimization (DCOP) problems. ADOPT is an asynchronous and distributedsearch algorithm that is able to solve DCOP problems optimally. In this paper, we introduce Iterative Decreasing Bound ADOPT (IDB-ADOPT), a modification of ADOPT that changes the search strategy of ADOPT from performing one best-first search to performing a series of depth-first searches. Each depth-first search is provided with a bound, initially a, large integer, and returns the first solution whose cost is smaller than or equal to the bound. The bound is then reduced to the cost of this solution minus one and the process repeats. If there is no solution whose cost is smaller than or equal to the bound, it returns a. cost-minimal solution. Thus, IDB-ADOPT is an anytime algorithm that solves DCOP problems with integer costs optimally. Our experimental results for graph coloring problems show that IDB-ADOPT runs faster (that is, needs fewer cycles) than ADOPT oil large DCOP problems, with savings of up to one order of magnitude.
distributed Constraint Optimization (DCOP) is useful for solving agent-coordination problems. Any-space DCOP searchalgorithms require only a small amount of memory but can be sped up by caching information. However, ...
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ISBN:
(纸本)9780981738161
distributed Constraint Optimization (DCOP) is useful for solving agent-coordination problems. Any-space DCOP searchalgorithms require only a small amount of memory but can be sped up by caching information. However, their current caching schemes do not exploit the cached information when deciding which information to preempt from the cache when a new piece of information needs to be cached. Our contributions are three-fold: (1) We frame the problem as an optimization problem. (2) We introduce three new caching schemes (MaxPriority, MaxEffort and MaxUtility) that exploit the cached information in a DCOP-specific way. (3) We evaluate how the resulting speed up depends on the search strategy of the DCOP search algorithm. Our experimental results show that, on all tested DCOP problem classes, our MaxEffort and MaxUtility schemes speed up ADOPT (which uses best-first search) more than the other tested caching schemes, while our MaxPriority scheme speeds up BnB-ADOPT (which uses depth-first branch-and-bound search) at least as much as the other tested caching schemes.
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