On Planning while Learning

On Planning while Learning

This paper introduces a framework for Planning while Learning where an agent is given a goal to achieve in an environment whose behavior is only partially known to the agent. We discuss the tractability of various plan-design processes. We show that for a large natural class of Planning while Learning systems, a plan can be presented and veriied in a reasonable time. However, coming up algorith...

Planning While Learning Operators

This paper describes issues that arise when integrating a planner with a system that learns planning operators incrementally, and our approaches to address these issues. During learning, domain knowledge can be incomplete and incorrect in different ways; therefore the planner must be able to use incomplete domain knowledge. This presents the following challenges for planning: How should the pla...

Probabilistic Exploration in Planning while Learning

Sequential decision tasks with incomplete infor­ mation are characterized by the exploration prob­ lem; namely the trade-off between further exploration for learning more about the environ­ ment and immediate exploitation of the accrued information for decision-making. Within artificial intelligence, there has been an increasing interest in studying planning-while-learning algorithms for these ...

Probabilistic Exploration in Planning while Learning

In decision-theoretic planning Pemberton and Korf (1994) have proposed separate heuristic functions for exploration and decision-making in incremental real-time search algorithms. Draper et al. (1994) have developed a probabilistic planning algorithm that performs both information-producing actions and contingent planning actions. Our exploration strategy could be applied to these planning task...

A Framework for Interleaving Planning-while-Learning and Execution