plugin-AAMAS07_Smith16b - Distributed Management of...

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Distributed Management of Flexible Times Schedules Stephen F. Smith, Anthony Gallagher, Terry Zimmerman, Laura Barbulescu, Zachary Rubinstein The Robotics Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA 15024 { sfs,anthonyg,wizim,laurabar,zbr } ABSTRACT We consider the problem of managing schedules in an un- certain, distributed environment. We assume a team of col- laborative agents, each responsible for executing a portion of a globally pre-established schedule, but none possessing a global view of either the problem or solution. The goal is to maximize the joint quality obtained from the activities executed by all agents, given that, during execution, unex- pected events will force changes to some prescribed activi- ties and reduce the utility of executing others. We describe an agent architecture for solving this problem that couples two basic mechanisms: (1) a “flexible times” representation of the agent’s schedule (using a Simple Temporal Network) and (2) an incremental rescheduling procedure. The former hedges against temporal uncertainty by allowing execution to proceed from a set of feasible solutions, and the latter acts to revise the agent’s schedule when execution is forced out- side of this set of solutions or when execution events reduce the expected value of this feasible solution set. Basic coordi- nat ionw ithotheragentsisach ieveds imp lybycommun icat- ing schedule changes to those agents with inter-dependent activities. Then, as time permits, the core local problem solving infra-structure is used to drive an inter-agent option generation and query process, aimed at identifying opportu- nities for solution improvement through joint change. Using a simulator to model the environment, we compare the per- formance of our multi-agent system with that of an expected optimal (but non-scalable) centralized MDP solver. Categories and Subject Descriptors I.2.11 [ Computing Methodologies ]: ArtiFcial Intelligence— Distributed Artifcial Intelligence General Terms Algorithms, Design Keywords Multi-Agent Scheduling, Agent Architectures Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proFt or commercial advantage and that copies bear this notice and the full citation on the Frst page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speciFc permission and/or a fee. AAMAS’07 May 14–18 2007, Honolulu, Hawai’i, USA. Copyright 2007 I±AAMAS . 1. INTRODUCTION
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This note was uploaded on 04/19/2010 for the course CISC 800 taught by Professor Kd during the Spring '10 term at University of Delaware.

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plugin-AAMAS07_Smith16b - Distributed Management of...

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