This paper is concerned with those elements of planning knowledge that are common to both understanding someone else's plan and creating a plan for one's own use. This planning knowledge can be divided into two bodies: Knowledge about the world, and knowledge about the planning process itself. Our interest here is primarily with the latter corpus. The central thesis is that much of the knowledge about the planning process itself can be formulated in terms of higher‐level goals and plans called (...) meta‐goals and meta‐plans. These entities can then be used by the same understanding and planning mechanisms that process ordinary goals and plans. However, the meta‐planning knowledge now enables these mechanisms to handle much more complicated situations in a uniform manner.Systems based on meta‐planning would have a number of advantages over existing problem solving and understanding systems. The same knowledge could be shared by both a planner and understander, and both would be able to handle complex situations elegantly. In addition, in planning, the use of meta‐planning has several advantages over more traditional methods involving constraints or critics. Meta‐planning allows the full power of a problem solver to be applied to situations that are generally amenable only to special purpose processing. In addition, meta‐planning facilitates the representation of some situations that are difficult to express otherwise. We have begun to introduce meta‐planning knowledge into two systems: PAM, a story understanding program, and PANDORA, a problem solving and plannin̈g system. (shrink)

Adaptive Planning is an approach to planning in the commonsense domain. An adaptive planner takes advantage of the habitual nature of many of the planning situations for which it plans by bosing its activities on a memory of pre‐stored plans. A critical issue, and the subject of this paper, is the question of flexibility: How does an adaptive planner refit an old plan in order to meet the demands of some new planning situation? An adaptive planner refits prestored plans by (...) constructing an interpretation of the situation in which it is engaged. Key elements in the theory of adaptive planning are its treatment of specific plans and a notion of situation matching. The theory of adaptive planning has been modeled by a computer program called PLEXUS. (shrink)

A new theory of problem solving is presented, which embeds problem solving in the theory of action; in this theory, a problem is just a difficult action. Making this work requires a sophisticated language for‐talking about plans and their execution. This language allows a broad range of types of action, and can also be used to express rules for choosing and scheduling plans. To ensure flexibility, the problem solver consists of an interpreter driven by a theorem prover which actually manipulates (...) formulas of the language. Many examples of the use of the system six given. including an extended treatment of the world of blocks. Limitations and extensions of the system are discussed at length. It is concluded that a rule‐based problem solver is necessary and feasible, but that much more work remains to be done on the underlying theory of planning and acting. (shrink)

Word problems are notoriously difficult to solve. We suggest that much of the difficulty children experience with word problems can be attributed to difficulty in comprehending abstract or ambiguous language. We tested this hypothesis by (1) requiring children to recall problems either before or after solving them, (2) requiring them to generate f'mal questions to incomplete word problems, and (3) modeling performance pattems using a computer simulation. Solution performance was found to be systematically related to recall and question generation performance. (...) Correct solutions were associated with accurate recall of the problem structure and with appropriate question generation. Solution "errors" were found to be correct solutions to miscomprehended problems. Word problems that contained abstract or ambiguous language tended to be miscomprehended more often than those using simpler language, and there was a great deal of systematicity in the way these problems were miscomprehended. Solution error pattems were successfully simulated by manipulating a computer model’s language comprehension strategies, as opposed to its knowledge of logical set relations. o was.. (shrink)