Proceedings of the AAAI Conference on Artificial Intelligence, 5
To control problem solving activity, a planner must resolve uncertainty about which specific long-term goals (solutions) to pursue and about which sequences of actions will best achieve those goals. In this paper, we describe a planner that abstracts the problem solving state to recognize possible competing and compatible solutions and to roughly predict the importance and expense of developing these solutions. With this information, the planner plans sequences of problem solving activities that most efficiently resolve its uncertainty about which of the possible solutions to work toward. The planner only details actions for the near future because the results of these actions will influence how (and whether) a plan should be pursued. As problem solving proceeds, the planner adds new details to the plan incrementally, and monitors and repairs the plan to insure it achieves its goals whenever possible. Through experiments, we illustrate how these new mechanisms significantly improve problem solving decisions and reduce overall computation. We briefly discuss our current research directions, including how these mechanisms can improve a problem solver’s real-time response and can enhance cooperation in a distributed problem solving network.