Coordination of Distributed Problem Solvers
Springer Science & Business Media, 06/12/2012 - 270 páginas
As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.
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Distributed Problem Solving and the DVMT
Identifying Local Goals Through Clustering
Planning Local Problem Solving
Recognizing Partial Global Goals
Coordination Through Partial Global Planning
Experiments and Evaluation
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achieve activities agents allow nodes alternative-goals attributes belief blackboard blackboard-level broadcast build chapter clustering hierarchy combined communication computation computation overhead control decisions coordinating node costs data for sensed data structures develop di-d distributed computing distributed problem solving domain domain-level DVMT environment event-classes example exchange expected Experiment Set future node-plan goal processing highly-rated PGP hypotheses i-goals identify implementation indicates initial integration interactions invoked long-term goals merged messages meta-level organization models modified multi-agent planning network-model node forms node-models node's overhead partial global planning partial results partial solutions performance PGGs PGP-partial-solution PGP’s PGPlanner PGPlanning plan-activities plan-activity-map plan’s plan1 plan3 plan4 planner planning mechanisms predictive information processor pursue queue received recognize reduce redundancy relationships reordered represent sensor sequence simulated situation solution-construction-graph solver specific storage subgoals subproblems task-passing tasks time-cushion time-locations time-regions total number tracking-levels updated vehicle monitoring vehicle-event-classes