As planning is applied to larger and richer domains the effort involved in
constructing domain descriptions increases and becomes a significant burden on
the human application designer. If general planners are to be applied
successfully to large and complex domains it is necessary to provide the domain
designer with some assistance in building correctly encoded domains. One way of
doing this is to provide domain-independent techniques for extracting, from a
domain description, knowledge that is implicit in that description and that can
assist domain designers in debugging domain descriptions. This knowledge can
also be exploited to improve the performance of planners: several researchers
have explored the potential of state invariants in speeding up the performance
of domain-independent planners. In this paper we describe a process by which
state invariants can be extracted from the automatically inferred type
structure of a domain. These techniques are being developed for exploitation by
STAN, a Graphplan based planner that employs state analysis techniques to
enhance its performance.

本文描述了一种从领域的自动推断类型结构中提取状态不变量的过程，并探讨其如何用于领域独立的计划中，从而提升计划的性能。