@InProceedings{turkett03a,
  author =	 {William H. Turkett and John R. Rose},
  title =	 {Planning with Agents: An Efficient Approach Using
                  Hierarchical Dynamic Decision Networks},
  booktitle =	 {Proceedings of the Fourth International Workshop on
                  Engineering Societies in the Agents World},
  year =	 2003,
  abstract =	 {To be useful in solving real world problems, agents
                  need to be able to act in environments in which it
                  may not be possible to be completely aware of the
                  current state and where actions do not always work
                  as planned. Additional complexity is added to the
                  problem when one considers groups of agents working
                  together. By casting the agent planning problem as a
                  partially observable Markov decision problem
                  (POMDP), optimal policies can be generated for
                  partially observable and stochastic
                  environments. Exact solutions, however, are
                  notoriously di±cult to ¯nd for problems of a
                  realistic nature. We introduce a hierarchical
                  decision network-based planning algorithm that can
                  generate high quality plans during execution while
                  demonstrating signi¯cant time savings. We also
                  discuss how this approach is particularly applicable
                  to planning in a multiagent environment as compared
                  to other POMDP-based planning algorithms. We present
                  experimental results comparing our algorithm with
                  results obtained by current POMDP and hierarchical
                  POMDP (HPOMDP) methods.},
  keywords =     {planning},
  url =		 {http://jmvidal.cse.sc.edu/library/turkett03a.pdf},
  googleid = 	 {MyhwWL8I0eIJ:scholar.google.com/},
  cluster = 	 {16343854140619761715}
}