Domain Model Acquisition in the Presence of Static Relations in the LOP System
Keywords:domain model acquisition, learning in planning
This paper addresses the problem of domain model acquisition from only action traces when the underlying domain model contains static relations. Domain model acquisition is the problem of synthesising a planning domain model from example plan traces and potentially other information, such as intermediate states. The LOCM and LOCMII domain model acquisition systems are highly effective at determining the dynamics of domain models with only plan traces as input (i.e. they do not rely on extra inputs such as predicate definitions, initial, final and intermediate states or invariants). Much of the power of the LOCM family of algorithms comes from the assumption that each action parameter goes through a transition. One place that this assumption is too strong is in the case of static predicates. We present a new domain model acquisition algorithm, LOP, that induces static predicates by using a combination of the generalised output from LOCM2 and a set of optimal plans as input to the learning system. We observe that static predicates can be seen as restrictions on the valid groundings of actions. Without the static predicates restricting possible groundings, the domains induced by LOCMII produce plans that are typically shorter than the true optimal solutions. LOP works by finding a minimal static predicate for each operator that preserves the length of the optimal plan.