Coordination of Distributed Problem SolversSpringer 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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... Builds on the DVMT 3 Identifying Local Goals Through Clustering 3.1 Background 3.2 Overview 3.3 Details 3.4 Generalizing • 4 Planning Local Problem Solving 4.1 Background 4.2 Overview 4.3 Details . 4.4 Generalizing vii 1 3 11 15 26 27 ...
... Builds on the DVMT 3 Identifying Local Goals Through Clustering 3.1 Background 3.2 Overview 3.3 Details 3.4 Generalizing • 4 Planning Local Problem Solving 4.1 Background 4.2 Overview 4.3 Details . 4.4 Generalizing vii 1 3 11 15 26 27 ...
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... build effective distributed coordination mechanisms . This research not only has conceptual clarity and elegance in providing a unify- ing framework for previous work , but is also empirically grounded . In a research area such as ...
... build effective distributed coordination mechanisms . This research not only has conceptual clarity and elegance in providing a unify- ing framework for previous work , but is also empirically grounded . In a research area such as ...
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... build more complete solutions ) . Each node maintains a set of PGPs that represents its own local view of network activity , and the nodes can exchange information about their local plans and PGPs to improve each other's view ...
... build more complete solutions ) . Each node maintains a set of PGPs that represents its own local view of network activity , and the nodes can exchange information about their local plans and PGPs to improve each other's view ...
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... build more resilient PGPs , and how they can build PGPs that assign tasks to better exploit network resources . Our work also explores the complexities involved in coordinating larger networks , and how the complexity can be reduced ...
... build more resilient PGPs , and how they can build PGPs that assign tasks to better exploit network resources . Our work also explores the complexities involved in coordinating larger networks , and how the complexity can be reduced ...
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... build plans for achieving them ( Chapter 4 ) . The other thrust is developing partial global planning mech- anisms . Given self - awareness of its own plans and , through communication , network awareness about others ' plans , a node ...
... build plans for achieving them ( Chapter 4 ) . The other thrust is developing partial global planning mech- anisms . Given self - awareness of its own plans and , through communication , network awareness about others ' plans , a node ...
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Acknowledgments | 251 |
Bibliography | 257 |
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achieve actions activities agents allows nodes alternative-goals attributes belief blackboard blackboard-level broadcast chapter clustering hierarchy combined communication computation computation overhead control decisions cooperation coordinating node Corkill costs d₁ d₂ data for sensed data structures develop di-de distributed distributed computing domain domain-level DVMT environment event-classes example exchange expected Experiment Set future node-plan goal processing highly-rated PGP hypotheses i-goal identify initial integration interactions invoked local plans long-term goals merged messages meta-level organization models modified multi-agent planning network-model node-models node's node2 overhead partial global planning partial results partial solutions performance PGGs PGP-partial-solution PGP's PGPlanner PGPlanning plan-activities plan-activity-map plan's planner planning mechanisms predictive information problem solving processor pursue queue R₁ received recognize redundancy relationships reordered represent sensor sequence short-term simulated situation solution-construction-graph solver specific storage subgoals task-passing tasks time-cushion time-locations time-regions total number tracking-levels updated vehicle monitoring vehicle-event-classes