Title
Planning with continuous resources for agent teams
Abstract
Many problems of multiagent planning under uncertainty require distributed reasoning with continuous resources and resource limits. Decentralized Markov Decision Problems (Dec-MDPs) are well-suited to address such problems, but unfortunately, prior Dec-MDP approaches either discretize resources at the expense of speed and quality guarantees, or avoid discretization only by limiting agents' action choices or interactions (e.g. assumption of transition independence). To address these shortcomings, this paper proposes M-DPFP, a novel algorithm for planning with continuous resources for agent teams, with three key features: (i) it maintains the agent team interaction graph to identify and prune the suboptimal policies and to allow the agents to be transition dependent, (ii) it operates in a continuous space of probability functions to provide the error bound on the solution quality and finally (iii) it focuses the search for policies on the most relevant parts of this search space to allow for a systematic trade-off of solution quality for speed. Our experiments show that M-DPFP finds high quality solutions and exhibits superior performance when compared with a discretization-based approach. We also show that M-DPFP is applicable to solving problems that are beyond the scope of existing approaches.
Year
DOI
Venue
2009
10.5555/1558109.1558165
AAMAS (2)
Keywords
Field
DocType
quality guarantee,multiagent planning,search space,continuous resource,transition independence,continuous space,agent team,agent team interaction graph,solution quality,high quality solution,markov decision process,multi agent systems,multi agent system
Graph,Discretization,Mathematical optimization,Decision problem,Distributed reasoning,Partially observable Markov decision process,Computer science,Markov chain,Multi-agent system,Limiting
Conference
Citations 
PageRank 
References 
5
0.48
16
Authors
2
Name
Order
Citations
PageRank
Janusz Marecki168549.06
Milind Tambe26008522.25