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Planning under uncertainty in complex structured environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Planning under uncertainty in complex structured environments.
作者:	Guestrin, Carlos Ernesto.
面頁冊數:	346 p.
附註:	Adviser: Daphne Koller.
附註:	Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4457.
Contained By:	Dissertation Abstracts International64-09B.
標題:	Computer Science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3104233
ISBN:	0496518011

Planning under uncertainty in complex structured environments.
Guestrin, Carlos Ernesto.

Planning under uncertainty in complex structured environments. [electronic resource] - 346 p.

Adviser: Daphne Koller.

Thesis (Ph.D.)--Stanford University, 2003.

Distributed coordination: An efficient distributed multiagent decision making algorithm, where the coordination structure arises naturally from the factored representation of the system dynamics.

ISBN: 0496518011Subjects--Topical Terms:

212513
Computer Science.

Planning under uncertainty in complex structured environments.
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500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: B, page: 4457.
502 $a Thesis (Ph.D.)--Stanford University, 2003.
520 # $a Distributed coordination: An efficient distributed multiagent decision making algorithm, where the coordination structure arises naturally from the factored representation of the system dynamics.
520 # $a Empirical evaluation: A detailed evaluation on a variety of large-scale tasks, including multiagent coordination in a real strategic computer game, demonstrating that our formal framework yields effective plans, complex agent coordination, and successful generalization in some of the largest planning problems in the literature.
520 # $a Factored Markov decision processes (MDPs) allow us to represent complex uncertain dynamic systems very compactly by exploiting problem-specific structure. Specifically, the state of the system is described by a set of variables that evolve stochastically over time using a representation, called dynamic Bayesian network, that often allows for an exponential reduction in representation complexity. However, the complexity of exact solution algorithms for such MDPs grows exponentially in the number of variables, and in the number of agents.
520 # $a Factored approximate planning: A suite of algorithms, building on our factored LP decomposition technique, that exploit structure in factored MDPs to obtain exponential reductions in planning time.
520 # $a Factored linear programs: A novel LP decomposition technique, using ideas from inference in Bayesian networks, that can exploit problem structure to reduce exponentially-large LPs to polynomially-sized ones that are provably equivalent.
520 # $a Generalization in relational MDPs: A framework for obtaining general solutions from a small set of environments, allowing agents to act in new environments without replanning.
520 # $a Many real-world tasks require multiple decision makers (agents) to coordinate their actions in order to achieve common long-term goals. Examples include: manufacturing systems, where managers of a factory coordinate to maximize profit; rescue robots that, after an earthquake, must safely find victims as fast as possible; or sensor networks, where multiple sensors collaborate to perform a large-scale sensing task under strict power constraints. All of these tasks require the solution of complex long-term multiagent planning problems in uncertain dynamic environments.
520 # $a This thesis builds a formal framework and approximate planning algorithms that exploit structure in factored MDPs to solve problems with many trillions of states and actions very efficiently. The main contributions of this thesis include:
590 $a School code: 0212.
650 # 0 $a Computer Science. $3 212513
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791 $a Ph.D.
792 $a 2003
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