Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning prob...
Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are wort...
Planning in partially observable environments remains a challenging problem, despite significant recent advances in offline approximation techniques. A few online methods have a...
In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the compu...
Generic SAT solvers have been very successful in solving hard combinatorial problems in various application areas, including AI planning. There is potential for improved performanc...