Graphplan

Abstract: The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as GRAPHPLAN and compilers that convert planning problems into propositional conjunctive normal form formulas for solution using systematic or stochastic SAT methods. Related work, in the context of spacecraft control, advances our understanding of interleaved planning and execution. In this survey, I explain the latest techniques and suggest areas for future research.

Abstract: If an agent does not have complete information about the world-state, it must reason about alternative possible states of the world and consider whether any of its actions can reduce the uncertainty. Agents controlled by a contingent planner seek to generate a robust plan, that accounts for and handles all eventualities, in advance of execution. Thus a contingent plan may include sensing actions which gather information that is later used to select between different plan branches. Unfortunately, previous contingent planners suffered defects such as confused semantics, incompleteness, and inefficiency. In this paper we describe SGP, a descendant of Graphplan that solves contingent planning problems. SGP distinguishes between actions that sense the value of an unknown proposition from those that change its value. SGP does not suffer from the forms of incompleteness displayed by CNLP and Cassandra. Furthermore, SGP is relatively fast.