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

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.