ISSN:
1467-8640
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Computer Science
Notes:
Knowledge engineering for planning is expensive and the resulting knowledge can be imperfect. To autonomously learn a plan operator definition from environmental feedback, our learning system WISER explores an instantiated literal space using a breadth-first search technique. Each node of the search tree represents a state, a unique subset of the instantiated literal space. A state at the root node is called a seed state. WISER can generate seed states with or without utilizing imperfect expert knowledge. WISER experiments with an operator at each node. The positive state, in which an operator can be successfully executed, constitutes initial preconditions of an operator. We analyze the number of required experiments as a function of the number of missing preconditions in a seed state. We introduce a naive domain assumption to test only a subset of the exponential state space. Since breadth-first search is expensive, WISER introduces two search techniques to reorder literals at each level of the search tree. We demonstrate performance improvement using the naive domain assumption and literal-ordering heuristics. To learn the effects of an operator, WISER computes the delta state, composed of the add list and the delete list, and parameterizes it. Unlike previous systems, WISER can handle unbound objects in the delta state. We show that machine-generated effects definitions are often simpler in representation than expert-provided definitions.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/0824-7935.00093
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