Partially Observable Markov Decision Processes (POMDPs) provide a general framework for AI planning, but they lack the structure for representing real world planning problems in a...
The major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments and the view of integration as a basic ...
Alan C. Schultz, William Adams, Brian Yamauchi, Mi...
Two major themes of our research include the creation of mobile robot systems that are robust and adaptive in rapidly changing environments, and the view of integration as a basic ...
Two of the most efficient planners for planning in nondeterministic domains are MBP and ND-SHOP2. MBP achieves its efficiency by using Binary Decision Diagrams (BDDs) to represent...
We propose a purely logical framework for planning in partially observable environments. Knowledge states are expressed in a suitable fragment of the epistemic logic S5. We show h...