A novel iterative approach to top-k planning

43Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.

Abstract

While cost-optimal planning aims at finding one best quality plan, top-k planning deals with finding a set of solutions, such that no better quality solution exists outside that set. We propose a novel iterative approach to top-k planning, exploiting any cost-optimal planner and reformulating a planning task to forbid exactly the given set of solutions. In addition, to compare to existing approaches to finding top-k solutions, we implement the K* algorithm in an existing PDDL planner, creating the first K* based solver for PDDL planning tasks. We empirically show that the iterative approach performs better for up to a large required size solution sets (thousands), while K* based approach excels on extremely large ones.

References Powered by Scopus

The fast downward planning system

1229Citations
N/AReaders
Get full text

COMPLEXITY RESULTS FOR SAS<sup>+</sup> PLANNING

435Citations
N/AReaders
Get full text

Landmarks, critical paths and abstractions: What's the difference anyway?

367Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Learning interpretable models expressed in linear temporal logic

77Citations
N/AReaders
Get full text

Bayesian inference of linear temporal logic specifications for contrastive explanations

47Citations
N/AReaders
Get full text

Chatbot testing using AI planning

43Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Katz, M., Sohrabi, S., Udrea, O., & Winterer, D. (2018). A novel iterative approach to top-k planning. In Proceedings International Conference on Automated Planning and Scheduling, ICAPS (Vol. 2018-June, pp. 132–140). AAAI press. https://doi.org/10.1609/icaps.v28i1.13893

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 8

100%

Readers' Discipline

Tooltip

Computer Science 8

89%

Engineering 1

11%

Save time finding and organizing research with Mendeley

Sign up for free