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Planning as satisfiability: Heuristics

Jussi Rintanen
2012 Artificial Intelligence  
as VSIDS, and often lifts it to the same level with other search methods such as explicit state-space search with heuristic search algorithms.  ...  The strategy is based on generic principles about properties of plans, and its performance with standard planning benchmarks often substantially improves on generic variable selection heuristics, such  ...  These conditions are satisfied by all main encodings of planning as SAT [23] .  ... 
doi:10.1016/j.artint.2012.08.001 fatcat:e75kvn6ncjavzooozno7j725su

SAS+ Planning as Satisfiability

R. Huang, Y. Chen, W. Zhang
2012 The Journal of Artificial Intelligence Research  
Planning as satisfiability is a principal approach to planning with many eminent advantages. The existing planning as satisfiability techniques usually use encodings compiled from STRIPS.  ...  The new scheme exploits the structural information in SAS+, resulting in an encoding that is both more compact and efficient for planning.  ...  Introduction Planning as satisfiability (SAT) is one of the main paradigms for planning.  ... 
doi:10.1613/jair.3442 fatcat:ug5taksm6bgk7nlsldt3q4ngm4

Introducing Preferences in Planning as Satisfiability

E. Giunchiglia, M. Maratea
2010 Journal of Logic and Computation  
Planning as Satisfiability is one of the most well-known and effective techniques for classical planning: SATPLAN has been the winning system in the deterministic track for optimal planners in the 4th  ...  In this paper we extend the Planning as Satisfiability approach in order to handle preferences and SATPLAN in order to solve problems with simple preferences.  ...  In planning as satisfiability, plans for Π n are generated by invoking a SAT solver on Π n .  ... 
doi:10.1093/logcom/exq023 fatcat:fgojeq2rvbg3ve34jppgah72mi

Planning as heuristic search

Blai Bonet, Héctor Geffner
2001 Artificial Intelligence  
Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings.  ...  In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and SAT planners.  ...  It's simple to show that the resulting measures satisfy Eq. (1).  ... 
doi:10.1016/s0004-3702(01)00108-4 fatcat:cm6bir2gbfgxbdcbzqwyonvn5i

Planning as satisfiability: parallel plans and algorithms for plan search

Jussi Rintanen, Keijo Heljanko, Ilkka Niemelä
2006 Artificial Intelligence  
Until now the application of ∃-step semantics in planning as satisfiability was hampered by the cubic size of the obvious encodings.  ...  of a sequential plan with as many operators as in the parallel one.  ...  PLANNING AS SATISFIABILITY Planning as satisfiability was introduced by Kautz and Selman [1992] .  ... 
doi:10.1016/j.artint.2006.08.002 fatcat:kmcw2fkh6vdfzggdqlny4a2dhu

μ-SATPLAN: Multi-agent planning as satisfiability

Yannis Dimopoulos, Muhammad Adnan Hashmi, Pavlos Moraitis
2012 Knowledge-Based Systems  
In this algorithm, agents use l-SATPLAN as the underlying planner for generating individual and joint consistent plans.  ...  As the agents share the same environment, they need to find a coordinated course of action that avoids harmful (or negative) interactions, and benefit from positive interactions, whenever this is possible  ...  The idea of distributed planning as SAT problem has also been discussed in [17] where the distributed planning problem is seen as the process of finding a satisfying assignment for both propositional  ... 
doi:10.1016/j.knosys.2011.07.019 fatcat:yuz5tuzqgvgx3hz7zm2mwsn7uu

Phase Selection Heuristics for Satisfiability Solvers [article]

Jingchao Chen
2011 arXiv   pre-print
This can be seen as an improvement of the heuristic proposed by Jeroslow-Wang (1990).  ...  We incorporate the ACE heuristic and the existing phase selection heuristics in the new solver MPhaseSAT, and select a phase heuristic in a way similar to portfolio methods.  ...  Introduction Satisfiability (SAT) is a classic NP-complete problem, and has applications in numerous fields such as computer aided design, data diagnosis, EDA, logic reasoning, cryptanalysis, planning,  ... 
arXiv:1106.1372v1 fatcat:eamft4sv5bbwzk4lxtddxtscsa

HTN Planning as Heuristic Progression Search

Daniel Höller, Pascal Bercher, Gregor Behnke, Susanne Biundo
2020 The Journal of Artificial Intelligence Research  
In this article, we propose the use of progression search as basis for heuristic HTN planning systems.  ...  Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning.  ...  In this article, we propose the use of progression-based search as basis for heuristic HTN planning systems.  ... 
doi:10.1613/jair.1.11282 fatcat:5pw3r3ciwjhqtefm6lo4huyrd4

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability [chapter]

Alfonso Emilio Gerevini, Alessandro Saetti, Mauro Vallati
2011 Lecture Notes in Computer Science  
Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it.  ...  In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning  ...  In this paper, we consider two types of learned knowledge for optimal planners in the "planning as satisfiability" framework (also called SAT-based planning) [11] : macro-actions and the planning horizon  ... 
doi:10.1007/978-3-642-23954-0_19 fatcat:vfbz5f7h3venbfwf4f4qjdm2l4

Planning as satisfiability with IPC simple preferences and action costs

Marco Maratea
2012 AI Communications  
At the same time, as a side effect of this analysis, challenging Max-SAT and PB benchmarks have been identified, as well as the Max-SAT and PB solvers performing best on these planning problems.  ...  Moreover, as we said before, the approach in [GM11] relies on an ordering of the heuristic that can hit performance, while the optimization solvers employed do not rely on such heuristics.  ...  Even if they solve a different problem to us, the overall goal is the same as in our paper, i.e. to allow the planning as satisfiability approach to take into account plan quality issues other than the  ... 
doi:10.3233/aic-2012-0540 fatcat:7c7zcgz7ufazte7xwpn2b3vrqm

Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators [article]

Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael Katz
2022 arXiv   pre-print
These classical heuristics act as dense reward generators to alleviate the sparse-rewards issue and enable our RL agent to learn domain-specific value functions as residuals on these heuristics, making  ...  We demonstrate on several classical planning domains that using classical heuristics for RL allows for good sample efficiency compared to sparse-reward RL.  ...  Value Iteration for Classical Planning Our main approach will be to learn a value function that can be used as a heuristic to guide planning.  ... 
arXiv:2109.14830v2 fatcat:s4bxrobylzgpbdjdm4i4rggnnm

Deterministic Oversubscription Planning as Heuristic Search: Abstractions and Reformulations

Carmel Domshlak, Vitaly Mirkis
2015 The Journal of Artificial Intelligence Research  
While in classical planning the objective is to achieve one of the equally attractive goal states at as low total action cost as possible, the objective in deterministic oversubscription planning (OSP)  ...  is to achieve an as valuable as possible subset of goals within a fixed allowance of the total action cost.  ...  All common heuristic search algorithms for optimal classical planning, such as A * , require admissible heuristics.  ... 
doi:10.1613/jair.4443 fatcat:gqxpk3jwlzbmtglj63xvndgexq

Automated Discovery of Local Search Heuristics for Satisfiability Testing

Alex S. Fukunaga
2008 Evolutionary Computation  
The development of successful metaheuristic algorithms such as local search for a difficult problems such as satisfiability testing (SAT) is a challenging task.  ...  We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks.  ...  into SAT from other problem formulations such as AI planning instances.  ... 
doi:10.1162/evco.2008.16.1.31 pmid:18386995 fatcat:2xtt63nr7nf3lolz7lzgg4qosq

Scaling-up Generalized Planning as Heuristic Search with Landmarks [article]

Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson, Laura Sebastiá
2022 arXiv   pre-print
Landmarks are one of the most effective search heuristics for classical planning, but largely ignored in generalized planning.  ...  Our two orthogonal contributions are analyzed in an ablation study, showing that both improve the state-of-the-art in GP as heuristic search, and that both benefit from each other when used in combination  ...  In more detail, we build on top of the GP as heuristic search approach (Segovia-Aguas, Jiménez, and Jonsson 2021), which represents generalized plans as planning programs. Definition 2 (GP problem).  ... 
arXiv:2205.04850v1 fatcat:g4ynrotu2zgp3oowyozvfeskka

Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

C. Domshlak, J. Hoffmann, A. Sabharwal
2009 The Journal of Artificial Intelligence Research  
Planning as satisfiability, as implemented in, for instance, the SATPLAN tool, is a highly competitive method for finding parallel step-optimal plans.  ...  Surprisingly, the idea turns out to appear quite hopeless in the context of planning as satisfiability.  ...  A preliminary version of this work appeared at ICAPS'06, the 16 th International Conference on Automated Planning and Scheduling (Hoffmann, Sabharwal, & Domshlak, 2006  ... 
doi:10.1613/jair.2817 fatcat:wlgeq6fx6nhqhcvobi37zqr3se
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