Domain model acquisition in domains with action costs

Abstract

This paper addresses the challenge of automated numeric domain model acquisition from observations. Many industrial and commercial applications of planning technology rely on numeric planning models. For example, in the area of autonomous systems and robotics, an autonomous robot often has to reason about its position in space, power levels and storage capacities. It is essential for these models to be easy to construct. Ideally, they should be automatically constructed. Learning the structure of planning domains from observations of action traces has produced successful results in classical planning. In this work, we present the first results in generalising approaches from classical planning to numeric planning. We restrict the numeric domains to those that include fixed action costs. Taking the finite state automata generated by the LOCM family of algorithms, we learn costs associated with machines; specifically to the object transitions and the state parameters. We learn action costs from action traces (with only the final cost of the plans as extra information) using a constraint programming approach. We demonstrate the effectiveness of this approach on standard benchmarks.