In numeric AI planning, a state is represented by propositions and numeric variables, actions change the values of numeric variables in addition to adding and deleting propositions, and goals and preconditions of actions may include conditions over numeric variables. While domains of numeric variables are rational numbers in general, upper and lower bounds on variables affected only by constant increase and decrease can sometimes be determined and exploited by a heuristic function. In this paper, we generalize the existing method to variables that are changed by linear effects. We exploit the extracted bounds to improve the numeric LM-cut heuristic, a state-of-the-art admissible heuristic for linear numeric planning. Empirical evaluation shows that our method improves the performance of LM-cut in multiple domains. The proposed method can also detect unsolvability of some numeric tasks in polynomial time.
|Title of host publication
|ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings
|Kobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu
|IOS Press BV
|Number of pages
|Published - 28 Sep 2023
|26th European Conference on Artificial Intelligence, ECAI 2023 - Krakow, Poland
Duration: 30 Sep 2023 → 4 Oct 2023
|Frontiers in Artificial Intelligence and Applications
|26th European Conference on Artificial Intelligence, ECAI 2023
|30/09/23 → 4/10/23
Bibliographical notePublisher Copyright:
© 2023 The Authors.