Learning Lifted Action Models from Traces with Minimal Information About Actions and States

TL;DR

This paper extends the learning of STRIPS+ action models from traces to scenarios with partial observability of actions and states, providing algorithms and completeness results for various observability conditions.

Contribution

It introduces algorithms and theoretical results for learning STRIPS+ models from traces with partial information, relaxing previous assumptions of full observability.

Findings

Algorithms for learning from traces with no state observability.

Conditions for successful learning with partial state observability.

Experimental validation of the proposed methods.

Abstract

It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequences from a hidden STRIPS model. The result is remarkable because the states are not assumed to be observable at all, and yet it is not practical enough as STRIPS actions include arguments that are not needed for selecting the actions. This shortcoming has been addressed by assuming that the action traces come instead from a hidden STRIPS+ model where some action arguments are implicit in the hidden action preconditions. A limitation of this approach, however, is that it assumes that the states are fully observable. In this work, we relax these restrictions and consider the problem of learning STRIPS+ action domains from traces in a more general context where the traces carry partial information about both actions and states. In particular, we formulate algorithms and completeness results for three general cases, all of which assume full observability of selected action arguments. In the first case, no observability of the state is assumed; in the second case, full observability of some state predicates is assumed, and in the third case, local observability of some state predicates is assumed instead. Given a STRIPS+ domain, these results characterize the conditions under which an equivalent domain can be learned from traces. Experimental results are reported.