Learning from Previous Execution to Improve Route Planning

Abstract

In this paper, we describe a specific approach to iterative planning in the domain of off-road route planning, in which the objective is to find a cost-minimal path from one point to another. In iterative planning we are concerned with finding a way to solve a succession of planning problems, improving the system’s behavior over time. For example, this improvement might come about through improved heuristics, leading to more effective search of the space of possible plans, or through corrections or additions to the domain model used in planning. In this work, we take the latter approach, modifying the domain model based on differences between plans generated using the existing model and “good” plans.

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