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Plan stability: replanning versus plan repair

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TLDR
This work presents arguments to support the claim that plan stability is a valuable property, and proposes an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context.
Abstract
The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the stable repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning.

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Citations
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References
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Journal ArticleDOI

Decision-theoretic planning: structural assumptions and computational leverage

TL;DR: In this article, the authors present an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI.
Journal ArticleDOI

Using temporal logics to express search control knowledge for planning

TL;DR: This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner.
Journal ArticleDOI

The metric-FF planning system: translating Ignoring delete lists to numeric state variables

TL;DR: A natural extension of "ignoring delete lists" to numeric state variables is presented, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is "monotonic".
Journal ArticleDOI

Planning through stochastic local search and temporal action graphs in LPG

TL;DR: This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in LPG using this representation, and shows that these techniques can be very effective.
Book

Planning and Learning by Analogical Reasoning

TL;DR: The problem solver automates the generation of problem solving cases and automates case storage, leading to efficient case retrieval and analogical replay.
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