Learning Numeric Planning Domain Models From Positive Observations

Domain-independent planning algorithms require as input an action model that specifies the preconditions and effects of each action. Constructing such models is often challenging for domain experts, particularly in domains where actions involve both Boolean and numeric state variables. We address the problem of learning such hybrid action models from observations of successful action executions.

A central challenge is that observing only successful executions provides no explicit evidence about which conditions are necessary for an action to be applicable. This difficulty is especially pronounced for learning numeric preconditions, for which there exist negative theoretical results concerning efficient learnability. To mitigate this limitation, we propose SAM-SVM, a novel numeric action model learning algorithm that learns numeric preconditions by heuristically simulating negative observations, i.e., possible states where actions are inapplicable.

We also implemented NSAM+SVM, a hybrid algorithm that integrates SAM-SVM and NSAM, an existing conservative action model learning algorithm.

Empirical evaluation demonstrates that SAM-SVM can learn more accurate action models and achieves improved planning performance compared to existing methods on standard numeric planning benchmarks, and NSAM+SVM provides the most robust behavior across most domains.