Symbolic non-deterministic planning represents action effects as sets of possible next states. In this paper, we move toward a more probabilistic uncertainty model by distinguishi...
Rune M. Jensen, Manuela M. Veloso, Randal E. Bryan...
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...
In a market-based scheduling mechanism, the allocation of time-specific resources to tasks is governed by a competitive bidding process. Agents bidding for multiple, separately al...
Jeffrey K. MacKie-Mason, Anna Osepayshvili, Daniel...
In this paper, we show how a planner can use a modelchecking verifier to guide state space search. In our work on hard real-time, closed-loop planning, we use a modelchecker'...
Robert P. Goldman, Michael J. S. Pelican, David J....
LPG is a planner that performed very well in the last International planning competition (2002). The system is based on a stochastic local search procedure, and it incorporates se...
We describe and evaluate a system for learning domainspecific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well ...
Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The p...
Coordinated action for a team of robots is a challenging problem, especially in dynamic, unpredictable environments. Robot soccer is an instance of a domain where well defined goa...
Michael H. Bowling, Brett Browning, Manuela M. Vel...