Lilotane : A Lifted SAT-based Approach to Hierarchical Planning

One of the oldest and most popular approaches to automated planning is to encode the problem at hand into a propositional formula and use a Satisfiability (SAT) solver to find a solution. In all established SAT-based approaches for Hierarchical Task Network (HTN) planning, grounding the problem is necessary and oftentimes introduces a combinatorial blowup in terms of the number of actions and reductions to encode. Our contribution named Lilotane (Lifted Logic for Task Networks) eliminates this issue for Totally Ordered HTN planning by directly encoding the lifted representation of the problem at hand. We lazily instantiate the problem hierarchy layer by layer and use a novel SAT encoding which allows us to defer decisions regarding method arguments to the stage of SAT solving. We show the correctness of our encoding and compare it to the best performing prior SAT encoding in a worst-case analysis. Empirical evaluations confirm that Lilotane outperforms established SAT-based approaches, often by orders of magnitude, produces much smaller formulae on average, and compares favorably to other state-of-the-art HTN planners regarding robustness and plan quality. In the International Planning Competition (IPC) 2020, a preliminary version of Lilotane scored the second place. We expect these considerable improvements to SAT-based HTN planning to open up new perspectives for SAT-based approaches in related problem classes

Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Raffinement des intentions

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

GoCo: planning expressive commitment protocols

Acknowledgements We gratefully thank those who shared their code with us. Special thanks to Ugur Kuter. We thank the anonymous reviewers, and also acknowledge with gratitude the reviewers at ProMAS’11, AAMAS’13, AAAI’13, and AAMAS’15, where preliminary parts of this work appeared. FM thanks the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the support within process numbers 306864/2013-4 under the PQ fellowship and 482156/2013-9 under the Universal project programs. NYS acknowledges support of the AUB University Research Board Grant Number 102853 and the OSB Grant OFFER_C1_2013_2014.Peer reviewe

Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models

arXiv admin note: substantial text overlap with arXiv:2103.11692Preprin