• Publications
  • Influence
Dynamic Programming for Structured Continuous Markov Decision Problems
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned intoExpand
  • 103
  • 13
  • PDF
Symbolic heuristic search for factored Markov decision processes
We describe a plnning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. ForwardExpand
  • 98
  • 10
  • PDF
Region-Based Incremental Pruning for POMDPs
We present a major improvement to the incremental pruning algorithm for solving partially observable Markov decision processes. Our technique targets the cross-sum step of the dynamic programmingExpand
  • 56
  • 10
  • PDF
Dynamic Programming for POMDPs Using a Factored State Representation
Contingent planning - constructing a plan in which action selection is contingent, on imperfect information received during plan execution - can be formalized as the problem of solving a partiallyExpand
  • 119
  • 8
  • PDF
Adaptive Peer Selection
In a peer-to-peer file-sharing system, a client desiring a particular file must choose a source from which to download. The problem of selecting a good data source is difficult because some peers mayExpand
  • 72
  • 2
  • PDF
Symbolic Heuristic Search Using Decision Diagrams
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deterministic and decision-theoretic planning problems. A symbolic approach exploits state abstraction byExpand
  • 36
  • 2
  • PDF
Approximate Planning for Factored POMDPs
We describe an approximate dynamic programming algorithm for partially observable Markov decision processes represented in factored form. Two complementary forms of approximation are used to simplifyExpand
  • 38
  • 2
An Approach to State Aggregation for POMDPs
A partially observable Markov decision process (POMDP) provides an elegant model for problems of planning under uncertainty. Solving POMDPs is very computationally challenging, however, and improvingExpand
  • 15
  • 2
  • PDF
Symbolic Generalization for On-line Planning
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line planners. In additionExpand
  • 45
  • 1
  • PDF