Learning from Opportunities : Storing and Re-using Execution-Time Bptimizations *

In earlier work (Hammond 1986), we proposed a mechanism for learning from execution-time plan failure. In this paper, we suggest a corollary notion of learning from execution-time planning opportunities. We argue that both are special cases of learning from expectation failure (Schank 1982). The result of this type of learning is a set of plans for frequently occurring conjuncts of goals, indexed by the features in the world that predict their usefulness. We discuss this notion, using examples from the University of Chicago planner TRUCKER, an implementation of casebased planning in the domain of a UPS-like pickup and delivery service. 1 Planning for conjunctive goals. Current research in planning has taken a rather dramatic change in course. Fundamental to this change has been the acknowledgement that a planner cannot exhaustively pre-plan for a set of goals prior to execution. In part this change is the result of demonstrations that pre-planning for conjunctive goals is undecidable (Chapman 1987). A more important factor has been the realization that the dual assumptions of a closed world and complete knowledge of operators are untenable in any complex domain. Alternative approaches to planning have included work in situated activity (Agre & Chapman 1987), reactive planning (Firby 1987)) planning by analogy (Carbonell 1983) as well as approaches that include opportunistic elements (Birnbaum 1987). Although different, all of these approaches have included the use of domain-tailored methods for solving planning problems. In particular, they have used what amounts to libraries of plans for conjunctive goals specific to their domains. Rather than applying weak methods to construct new plans, these planners make use of these domain level knowledge structures. As a result, much of these planners’ knowledge consists of specific plans for multiple goals, optimized over parameters such as execution time, resource consumption or likelihood of success. Such plans are specific to particular conjuncts of goals, and trade off generality for efficiency. This trade-off ends up being profitable if the planner is able to correctly anticipate which sets of goals will arise and use those plans that deal with them. *This work was supported in part by the Office of Naval Research under contract number N00014-88-K-0295. This trend in planning suggests a need for work in learning: learning the particular plans to deal with conjunctive goal situations, and learning the features that predict their usefulness. In particular, it suggests that executiontime opportunities can be used to indicate when a planner should learn, and what it should learn. Along with this, however, a planner must be able to learn the features of the situation that actually predict *the usefulness of the plan it has constructed. Using this type of execution-time information in learning is the topic of this paper. 2 Learning and plan interactions. A great deal of work has been done in planning on the problem of interactions between the steps of plans (Sacerdoti 1975, Tate 1980, Dean, Miller & Firby 1986). Most of this work has been concerned with using temporal projection to tease out interactions at planning time. Unfortunately, this approach relies on the assumption that a planner can project all possible effects of its actions as well as anticipate all effects of the actions of other agents. In recent years, this assumption has essentially been abandoned by the planning community for all but the sparsest domains. Sussman’s HACKER (1975) was a clear counterexample to this approach in that it dealt with the problem of step interaction at execution-time. In this work, Sussman suggested that partial plans could be constructed and run in a careful mode during which a planner could observe and learn from the effects of the steps being run. Sussman’s idea was that a planner could learn about negative interactions (e.g., the effects of one step negating the preconditions required by a later one) and later on anticipate and thus avoid them. This idea was expanded on and implemented in our own work in CHEF (Hammond 1986), in which we suggested that a planner could learn from its own planning failures, In particular we argued that a planner must be able to learn the conditions under which planning problems could later be predicted. In CHEF, the focus was on learning about negative interactions between steps. In this paper, we explore how a planner might also learn from the positive interactions that it encounters during plan execution. The difference between this approach and the one taken in CHEF lies in the relationship between expectations and plans. In CHEF, we studied expectation failures (Schank 1982) that corresponded to actual plan failures. In our current research, we are looking at expectation failures that are failures to anticipate planning opportunities. In CHEF, we argued that a planner has to respond to failure by repairing its current plan and by repairing the knowledge 536 Learning and Knowledge Acquisition From: AAAI-88 Proceedings. Copyright ©1988, AAAI (www.aaai.org). All rights reserved.