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  • 1
    Online Resource
    Online Resource
    SAT Competition 2016: Recent Developments
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1 ( 2017-02-12)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 31, No. 1 ( 2017-02-12)
    Abstract: We give an overview of SAT Competition 2016, the 2016 edition of thefamous competition for Boolean satisfiability (SAT) solvers with over 20 years of history. A key aim is to point out ``what's hot'' in SAT competitions in 2016, i.e., new developments in thecompetition series, including new competition tracks and new solver techniquesimplemented in some of the award-winning solvers.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v31i1.10641
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 2
    Online Resource
    Online Resource
    SAT Race 2015
    Elsevier BV ; 2016
    In:  Artificial Intelligence Vol. 241 ( 2016-12), p. 45-65
    Details
    In: Artificial Intelligence, Elsevier BV, Vol. 241 ( 2016-12), p. 45-65
    Type of Medium: Online Resource
    ISSN: 0004-3702
    URL: Article
    DOI: 10.1016/j.artint.2016.08.007
    RVK:
    EQ 1000
    RVK:
    SA 2940
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 1468341-6
    detail.hit.zdb_id: 218797-8
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  • 3
    Online Resource
    Online Resource
    No One SATPlan Encoding To Rule Them All
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 6, No. 1 ( 2021-09-01), p. 146-150
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 6, No. 1 ( 2021-09-01), p. 146-150
    Abstract: Solving planning problems via translation to propositional satisfiability (SAT) is one of the most successful approaches to automated planning. An important aspect of this approach is the encoding, i.e., the construction of a propositional formula from a given planning problem instance. Numerous encoding schemes have been proposed in the recent years each aiming to outperform the previous encodings on the majority of the benchmark problems. In this paper we take a different approach. Instead of trying to develop a new encoding that is better for all kinds of benchmarks we take recently developed specialized encoding schemes and design a method to automatically select the proper encoding for a given planning problem instance. In the paper we also examine ranking heuristics for the Relaxed Relaxed Exists-Step encoding, which plays an important role in our algorithm. Experiments show that our new approach significantly outperforms the state-of-the-art encoding schemes when compared on the benchmarks of the 2011 International Planning Competition.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v6i1.18371
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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  • 4
    Online Resource
    Online Resource
    REINFORCED ENCODING FOR PLANNING AS SAT
    Czech Technical University in Prague - Central Library ; 2015
    In:  Acta Polytechnica CTU Proceedings Vol. 2, No. 2 ( 2015-12-31), p. 1-7
    Details
    In: Acta Polytechnica CTU Proceedings, Czech Technical University in Prague - Central Library, Vol. 2, No. 2 ( 2015-12-31), p. 1-7
    Abstract: Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. We propose a new encoding scheme, called Reinforced Encoding, which encodes a planning problem represented in the SAS+ formalism into SAT. The Reinforced Encoding is a combination of the transition-based SASE encoding with the classical propositional encoding. In our experiments we compare our new encoding to other known SAS+ based encodings. The results indicate, that he Reinforced encoding performs well on the benchmark problems of the 2011 International Planning Competition and can outperform all the other known encodings for several domains.
    Type of Medium: Online Resource
    ISSN: 2336-5382
    URL: Article
    DOI: 10.14311/APP.2015.1.0001
    Language: Unknown
    Publisher: Czech Technical University in Prague - Central Library
    Publication Date: 2015
    detail.hit.zdb_id: 2868262-2
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  • 5
    Online Resource
    Online Resource
    On Different Strategies for Eliminating Redundant Actions from Plans
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 5, No. 1 ( 2021-09-01), p. 10-18
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 5, No. 1 ( 2021-09-01), p. 10-18
    Abstract: Satisficing planning engines are often able to generate plans in a reasonable time, however, plans are often far from optimal. Such plans often contain a high number of redundant actions, that are actions, which can be removed without affecting the validity of the plans. Existing approaches for determining and eliminating redundant actions work in polynomial time, however, do not guarantee eliminating the "best" set of redundant actions, since such a problem is NP-complete. We introduce an approach which encodes the problem of determining the "best" set of redundant actions (i.e. having the maximum total-cost) as a weighted MaxSAT problem. Moreover, we adapt the existing polynomial technique which greedily tries to eliminate an action and its dependants from the plan in order to eliminate more expensive redundant actions. The proposed approaches are empirically compared to existing approaches on plans generated by state-of-the-art planning engines on standard planning benchmarks.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v5i1.18318
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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  • 6
    Online Resource
    Online Resource
    Using Algorithm Configuration Tools to Generate Hard SAT Benchmarks
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 9, No. 1 ( 2021-09-01), p. 133-137
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 9, No. 1 ( 2021-09-01), p. 133-137
    Abstract: Algorithm configuration tools have been successfully used to speed up local search satisfiability (SAT) solvers and other search algorithms by orders of magnitude. In this paper, we show that such tools are also very useful for generating hard SAT formulas with a planted solution, which is useful for benchmarking SAT solving algorithms and also has cryptographic applications. Our experiments with state-of-the-art local search SAT solvers show that by using this approach we can randomly generate satisfiable formulas that are considerably harder than uniform random formulas of the same size from the phase-transition region or formulas generated by state-of-the-art approaches. Additionally, we show how to generate small satisfiable formulas that are hard to solve by CDCL solvers.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v9i1.18461
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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  • 7
    Online Resource
    Online Resource
    On Improving Plan Quality via Local Enhancements
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 3, No. 1 ( 2021-08-20), p. 154-156
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 3, No. 1 ( 2021-08-20), p. 154-156
    Abstract: There exist planning algorithms that can quickly find sub-optimal plans even for large problems and planning algorithms finding optimal plans but only for smaller problems. We attempt to integrate both approaches. We present an anytime technique for improving plan quality (decreasing the plan makespan) via substituting parts of the plan by better sub-plans. The technique guarantees optimality though it is primarily intended to quickly improve plan quality. We experimentally compare various approaches to local improvements.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v3i1.18260
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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  • 8
    Online Resource
    Online Resource
    Accelerating SAT Based Planning with Incremental SAT Solving
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2017
    In:  Proceedings of the International Conference on Automated Planning and Scheduling Vol. 27 ( 2017-06-05), p. 135-139
    Details
    In: Proceedings of the International Conference on Automated Planning and Scheduling, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 27 ( 2017-06-05), p. 135-139
    Abstract: One of the most successful approaches to automated planning is the translation to propositional satisfiability (SAT). We employ incremental SAT solving to increase the capabilities of several modern encodings for SAT based planning. Experiments based on benchmarks from the 2014 International Planning Competition show that an incremental approach significantly outperforms non incremental solving. Although we are using sequential scheduling of makespans, we can outperform the state-of-the-art SAT based planning system Madagascar in the number of solved instances.
    Type of Medium: Online Resource
    ISSN: 2334-0843 , 2334-0835
    URL: Article
    DOI: 10.1609/icaps.v27i1.13798
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2017
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  • 9
    Online Resource
    Online Resource
    Using an Algorithm Portfolio to Solve Sokoban
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 8, No. 1 ( 2021-09-01), p. 165-166
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 8, No. 1 ( 2021-09-01), p. 165-166
    Abstract: The game of Sokoban is an interesting platform for algorithm research. It is hard for humans and computers alike. Even small levels can take a lot of computation for all known algorithms. In this paper we will describe how a search based Sokoban solver can be structured and which algorithms can be used to realize each critical part. We implement a variety of those, construct a number of different solvers and combine them into an algorithm portfolio. The solver we construct this way can outperform existing solvers when run in parallel, that is, our solver with 16 processors outperforms the previous sequential solvers.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v8i1.18416
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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  • 10
    Online Resource
    Online Resource
    PASAR — Planning as Satisfiability with Abstraction Refinement
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the International Symposium on Combinatorial Search Vol. 10, No. 1 ( 2021-09-01), p. 70-78
    Details
    In: Proceedings of the International Symposium on Combinatorial Search, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 10, No. 1 ( 2021-09-01), p. 70-78
    Abstract: One of the classical approaches to automated planning is the reduction to propositional satisfiability (SAT). Recently, it has been shown that incremental SAT solving can increase the capabilities of several modern encodings for SAT-based planning. In this paper, we present a further improvement to SAT-based planning by introducing a new algorithm named PASAR based on the principles of counterexample guided abstraction refinement (CEGAR). As an abstraction of the original problem, we use a simplified encoding where interference between actions is generally allowed. Abstract plans are converted into actual plans where possible or otherwise used as a counterexample to refine the abstraction. Using benchmark domains from recent International Planning Competitions, we compare our approach to different state-of-the-art planners and find that, in particular, combining PASAR with forward state-space search techniques leads to promising results.
    Type of Medium: Online Resource
    ISSN: 2832-9163 , 2832-9171
    URL: Article
    DOI: 10.1609/socs.v10i1.18504
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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