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Conditionalization: Adapting Forward-Chaining Planners to Partially Observable Environments

We provide a general way to take forward-chaining planners for classical planning domains and conditionalize them, i.e., adapt them to generate policies for partially observable planning domains. For domain-configurable planners such as SHOP2, TLPlan, and TALplanner, our generalization technique preserves the ability to use domain knowledge to achieve highly efficient planning. We demonstrate this experimentally in two problem domains. The experiments compare PKS and MBP (two existing planners for partially observable planning) and CondSHOP2, a version of the HTN planner SHOP2 created by applying our conditionalization method. To our surprise, PKS and MBP could solve only the simplest problems in our test domains. In contrast, CondSHOP2 solved all of the test problems quite easily. This suggests that the ability to use domain knowledge may be not just desirable but indeed essential for solving large problems in partially observable domains.