Encoding Lifted Classical Planning in Propositional Logic

Höller, Daniel; Behnke, Gregor

doi:10.1609/icaps.v32i1.19794

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…Lifted planners were dominant in the 1990s (Penberthy and Weld 1992;Younes and Simmons 2003). Although progress in this area was slow during the early 2000s, recent work showed its usefulness in hard-to-ground benchmarks (Corrêa et al 2021;Lauer et al 2021;Wichlacz, Höller, and Hoffmann 2022;Höller and Behnke 2022;Shaik and van de Pol 2022;Horčík, Fišer, and Torralba 2022;Ståhlberg 2023). We used the Powerlifted planner, but other lifted heuristic search planners could be extended to deal with object creation (e.g., Horčík and Fišer 2021).…”

Section: Related Workmentioning

confidence: 99%

Planning with Object Creation

Corrêa,

De Giacomo,

Helmert

et al. 2024

ICAPS

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Classical planning problems are defined using some specification language, such as PDDL. The domain expert defines action schemas, objects, the initial state, and the goal. One key aspect of PDDL is that the set of objects cannot be modified during plan execution. While this is fine in many domains, sometimes it makes modeling more complicated. This may impact the performance of planners, and it requires the domain expert to bound the number of required objects beforehand, which can be a challenge. We introduce an extension to the classical planning formalism, where action effects can create and remove objects. This problem is semi-decidable, but it becomes decidable if we can bound the number of objects in any given state, even though the state space is still infinite. On the practical side, we extend the Powerlifted planning system to support this PDDL extension. Our results show that this extension improves the performance of Powerlifted while supporting more natural PDDL models.

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Section: Related Workmentioning

confidence: 99%

Planning with Object Creation

Corrêa,

De Giacomo,

Helmert

et al. 2024

ICAPS

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“…• h lmc hom : lifted A * with LM-cut computed on the task reduced with PDDL homomorphisms that was subsequently grounded [14], we use the best performing variant, as reported in [14], that preserves goal objects and reduces the planning task by 95% of objects. • LiSAT: translation of the lifted problem into a propositional SAT problem [13]. We use the non-incremental, length-optimal configuration with the Kissat [1] solver.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Be aware that neither h lmc hom nor LiSAT uses a heuristic computed on the lifted model, making h Lmax the system closest to ours. We evaluate our system on a benchmark set that has been introduced specifically for lifted planning [6,18] and has been used in recent papers (see also [6,18,13,26]).…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…However, most methods focused on satisficing planning, where no guarantee on plan quality is given and hence inadmissible heuristics can be used. The only heuristic for lifted optimal search is h max [7], but there are two other systems for lifted optimal planning: h lmc hom guides a (lifted) search with ground heuristics computed on a reduced model [14]; and LiSAT translates lifted planning problems into a propositional SAT problem [13]. The latter can -however -only do length-optimal and not cost-optimal planning.…”

Section: Introductionmentioning

confidence: 99%

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A Landmark-Cut Heuristic for Lifted Optimal Planning

Wichlacz,

Höller,

Fišer

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Lifted planning – finding plans directly on the PDDL input model – has attracted renewed attention during the last years. This avoids the process of grounding, which can become computationally prohibitive very easily. However, the main focus of recent research in this area has been on satisficing, i.e., (potentially) suboptimal planning. We present a novel heuristic for optimal lifted planning. Our basic idea is inspired by the LM-cut heuristic, which has been very successful in grounded optimal planning. Like LM-cut, we generate cut-based landmarks via back-chaining from the goal, generating cuts of partially grounded actions. However, exactly mimicking the ground formulation is not feasible, this includes computing the hmax heuristic several times for one computation of the LM-cut heuristic (which is already NP-hard to compute). We show that our heuristic is admissible and evaluate it in a cost optimal setting.

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“…Next, I present the idea of expressing lifted classical planning as SAT (Höller and Behnke 2022). The resulting planner LiSAT was the first lifted SAT-based planner -and proved highly efficient and outperformed all other lifted planners at the time of publication.…”

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confidence: 99%

Symbolic Reasoning Methods for AI Planning

Behnke

2024

AAAI

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Planning is the act of deliberative thinking before acting. It is based on a symbolic model of the world and the options to act in it, usually defined in function-free first-order logic. The task is to find a sequence of actions (a plan) that leads from a given current state to a desired goal state. The basic, purely physical description may be augmented with a partially ordered grammar-like structure (a Hierarchical Task Network or HTN), which can describe expert knowledge, or practical, legal, or operational requirements. In this talk, I will survey a variety of methods for automatically deriving plans using symbolic methods for planning -- from both my past and future research. These symbolic methods -- in some sense -- translate planning problems into other, simpler symbolic representations and reason over them to find plans. As a basis for these methods, I will firstly introduce relevant theoretical results on planning. First, I will discuss the expressive power of planning formalisms (ECAI'14, ICAPS'16) and second, the computational complexity of HTN planning and related tasks such as HTN plan verification, plan modification, and plan recognition (ICAPS'15, ICAPS'16). Based on these theoretical results, I will develop why SAT-based HTN planning is possible and how it can be implemented. To this end, I will survey several of my publications at top-tier conferences, including papers at ICAPS'17, AAAI'18, AAAI'19, IJCAI'19, AAAI'20, and ICAPS'21 -- in which I developed an highly SAT-based planner for HTN problems including the ability to find optimal plans as well as the grounding as a preprocessing step. Here I will also give an outlook on future developments and new ideas that I propose for SAT-based planning -- including the exploitation of structures in plan (e.g.\ landmarks or operator-counting constraints). Next, I will present the idea of expressing lifted classical planning as SAT (ICAPS'22). The resulting planner LiSAT was the first lifted SAT-based planner -- and proved highly efficient and outperformed all other lifted planners at the time of publication. Notably, LiSAT was the first planner (lifted or grounded) and still is the only one to solve the challenging OrganicSynthesis benchmark -- and could even prove optimality for all plans. I will also outline future ideas to further improve the efficiency of LiSAT. Lastly, I introduce the notion of planning with symbolic symbolic representations (AAAI'21 and ICAPS'23). Here one uses Binary Decision Diagrams to encode large sets of states efficiently. For expressing the additional structure encoded by HTNs, I show how BDDs can be suitably integrated into finite automata. Based on this representation, an efficient and optimal planning algorithm can be derived. Additionally, I show how this algorithm can be extended to also cover oversubscription planning.

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Encoding Lifted Classical Planning in Propositional Logic

Cited by 4 publications

References 12 publications

Planning with Object Creation

Planning with Object Creation

A Landmark-Cut Heuristic for Lifted Optimal Planning

Symbolic Reasoning Methods for AI Planning

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