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Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

作者： Todd Gamblin Massimiliano Culpo Gregory Becker Sergei Shudler Lawrence Livermore National Laboratory np-complete S.r.l. Italy

Modern scientific software stacks have become extremely complex, using many programming models and libraries to exploit a growing variety of GPUs and accelerators. Package managers can mitigate this complexity using dependency solvers, but they are reaching their limits. Finding compatible dependency versions is NP-complete, and modeling the semantics of package compatibility modulo build-time options, GPU runtimes, flags, and other parameters is extremely difficult. Within this enormous configuration space, defining a "good" configuration is *** tackle this problem using answer set programming (ASP), a declarative model for combinatorial search problems. We show, using the Spack package manager, that ASP programs can concisely express the compatibility rules of HPC software stacks and provide strong quality-of-solution guarantees. Using ASP, we can mix new builds with preinstalled binaries, and solver performance is acceptable even when considering tens of thousands of packages.

关键词： logic programming software packages high performance computing dependency management package management software reusability answer set programming

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Conditional Updates of answer set programming and Its Application in Explainable Planning 20

Conditional Updates of Answer Set Programming and Its Applic...

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Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

作者： Van Nguyen Tran Cao Son Vasileiou Loukas Stylianos William Yeoh New Mexico State University Las Cruces NM USA Washington University in St. Louis St. Louis MO USA

ISBN: (纸本)9781450375184

In explainable planning, the planning agent needs to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach is called model reconciliation, where the agent reconciles the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as a more general problem as follows: Given two knowledge bases πa and πh and a query q such that πa entails q and πh does not entail q, where the notion of entailment is dependent on the logical theories underlying πa and πh, how to change πh -– given πa and the support for q in πa – so that πh does entail q. In this paper, we study this problem under the context of answer set programming. To achieve this goal, we (1) define the notion of a conditional update between two logic programs πa and πh with respect to a query q; (2) define the notion of an explanation for a query q from a program πa to a program πh using conditional updates; (3) develop algorithms for computing explanations; and (4) show how the notion of explanation based on conditional updates can be used in explainable planning.

关键词： explainable planning answer set programming

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Quantum answer set programming Solver Using Amplitude Amplification

Quantum Answer Set Programming Solver Using Amplitude Amplif...

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Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Esraa Ebdelrehime Ahmed Younes Islam Elkabani Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria Egypt Faculty of Computer Science and Engineering Al Alamein International University Al Alamein Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

In this paper, a Quantum answer set programming solver (QASP) will be proposed to solve NP-hard combinatorial search problems. The paper shows that problems such as Hamiltonian cycle problem and N-queen problem encoded as ASP can be reduced to a MAX-3-SAT problem in a 3-CNF form. The 3-CNF Boolean formula will be solved using the proposed quantum solver with $O(n+ m)$ qubits where $n$ is the input variables number, $m$ is the clauses number in $O(\sqrt{2^{n/m}})$ steps.

关键词： Technological innovation Input variables Qubit answer set programming Search problems Machine intelligence

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From answer set logic programming to circumscription via logic of GK

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ARTIFICIAL INTELLIGENCE 2011年第1期175卷 264-277页

作者： Lin, Fangzhen Zhou, Yi Hong Kong Univ Sci & Technol Dept Comp Sci Kowloon Hong Kong Peoples R China Univ Western Sydney Sch Comp & Math Penrith NSW 1797 Australia

We first embed Pearce's equilibrium logic and Ferraris's propositional general logic programs in Lin and Shoham's logic of GK, a nonmonotonic modal logic that has been shown to include as special cases both Reiter's default logic in the propositional case and Moore's autoepistemic logic. From this embedding, we obtain a mapping from Ferraris's propositional general logic programs to circumscription, and show that this mapping can be used to check the strong equivalence between two propositional logic programs in classical logic. We also show that Ferraris's propositional general logic programs can be extended to the first-order case, and our mapping from Ferraris's propositional general logic programs to circumscription can be extended to the first-order case as well to provide a semantics for these first-order general logic programs. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Logic programming answer set programming Logic of GK Circumscription Nonmonotonic reasoning Knowledge representation and reasoning

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Learning answer set programs with aggregates via sampling and genetic programming

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Machine Learning 2025年第7期114卷 1-23页

作者： Azzolini, Damiano Department of Environmental and Prevention Sciences University of Ferrara Ferrara Italy

The goal of inductive logic programming is to learn a logic program that models the examples provided as input. The search space of the possible programs is constrained by a language bias, which defines the atoms and literals allowed in rules. answer set programming is a powerful formalism to represent complex combinatorial domains, also thanks to syntactic constructs such as aggregates. However, learning answer set programs from data is challenging, and often existing tools do not support the specification of aggregates in the language bias. In this paper, we introduce GENTIANS, a tool based on a genetic algorithm to learn answer set programs possibly with aggregates, arithmetic, and comparison operators, from examples. Empirical results, also against an existing solver, show that GENTIANS is able to provide accurate solutions even when the search space contains millions of clauses. Additionally, experiments on noisy datasets show the effectiveness of our approach. © The Author(s) 2025.

关键词： answer set programming Genetic programming Inductive logic programming

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Integrating MiniZinc with ASP Chef: Browser-Based Constraint programming for Education and Prototyping 17th

Integrating MiniZinc with ASP Chef: Browser-Based Constraint...

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17th International Conference on Logic programming and Non-monotonic Reasoning

作者： Alviano, Mario Reiners, Luis Angel Rodriguez Univ Calabria DEMACS Via Bucci 30-B I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9783031742088;9783031742095

In this paper, we present the integration of MiniZinc into ASP Chef, expanding its capabilities to include constraint programming alongside answer set programming (ASP). By leveraging the web assembly version of MiniZinc, this integration allows for running MiniZinc models directly in the browser, eliminating the need for additional software installations. This browser-based approach is particularly advantageous for educational settings and rapid prototyping, offering a seamless and accessible environment for learners and practitioners. To facilitate the incorporation of MiniZinc in ASP recipes, we have implemented a mapping mechanism that converts facts to MiniZinc data and vice versa. This integration not only broadens the scope of problems that can be addressed using ASP Chef but also simplifies the workflow for users, making it a versatile tool for complex computational tasks.

关键词： answer set programming Constraint programming system description

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Inductive learning of robot task knowledge from raw data and online expert feedback

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MACHINE LEARNING 2025年第4期114卷 1-33页

作者： Meli, Daniele Fiorini, Paolo Univ Verona Dept Comp Sci Verona Italy Univ Verona Dept Engn Innovat Med Str Le Grazie 15 I-37135 Verona Italy

The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities, possibly based on formal methods as logics for the definition of task specifications. However, prior knowledge is often unavailable in complex realistic scenarios. In this paper, we propose an offline algorithm based on inductive logic programming from noisy examples to extract task specifications (i.e., action preconditions, constraints and effects) directly from raw data of few heterogeneous (i.e., not repetitive) robotic executions. Our algorithm leverages on the output of any unsupervised action identification algorithm from video-kinematic recordings. Combining it with the definition of very basic, almost task-agnostic, commonsense concepts about the environment, which contribute to the interpretability of our methodology, we are able to learn logical axioms encoding preconditions of actions, as well as their effects in the event calculus paradigm. Since the quality of learned specifications depends mainly on the accuracy of the action identification algorithm, we also propose an online framework for incremental refinement of task knowledge from user's feedback, guaranteeing safe execution. Results in a standard manipulation task and benchmark for user training in the safety-critical surgical robotic scenario, show the robustness, data- and time-efficiency of our methodology, with promising results towards the scalability in more complex domains.

关键词： Inductive logic programming answer set programming Learning under uncertainty Online learning Human-robot interaction Explainable AI

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Direct Encoding of Declare Constraints in ASP

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THEORY AND PRACTICE OF LOGIC programming 2025年第1期25卷 92-131页

作者： Chiariello, Francesco Fionda, Valeria Ielo, Antonio Ricca, Francesco Univ Toulouse IRIT ANITI Toulouse France Univ Calabria Arcavacata Di Rende Italy

answer set programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a means to model processes through sets of constraints valid traces must satisfy, that can be expressed in linear temporal logic over finite traces (LTL$_{\text {f}}$). Existing ASP-based solutions encode Declare constraints by modeling the corresponding LTL$_{\text {f}}$ formula or its equivalent automaton which can be obtained using established techniques. In this paper, we introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules, eliminating the need for intermediate representations. We assess the effectiveness of this novel approach on two Process Mining tasks by comparing it with alternative ASP encodings and a Python library for Declare.

关键词： answer set programming process mining

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Knowledge representation and reasoning in the context of automated production planning

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年 1-20页

作者： Sahota, Harkiran Klodowski, Adam Daimler Buses Neu Ulm Germany LUT Univ Lappeenranta Finland

In the manufacturing industry the step of generating the assembly plans for products is crucial but very time and labour intensive as most of the work has to be done manually. Especially for products with high customisability this means, a new plan is required for every custom order. To reduce the complexity, a new hybrid approach for the generation of these assembly plans is introduced, based on knowledge representation and reasoning. The focus of the approach is to be deployable in real-world production instead of only focusing on research environments. Therefore, a knowledge base is generated, specifying the preconditions and details about different operations linked to geometrical features. To preserve these valuable information answer set programming (ASP) is used to define rules. By this, the generic information of when to apply which operation can be separated from the product-specific information of how a product looks, and therefore, be stored centrally and reused for any product or even different applications without any changes. Performing a geometric analysis on the product model allows extracting important geometrical features, that can then be used to derive the necessary information from the corresponding rules. In all of this, the human intervention is crucial to cover even complex assemblies and to strengthen the acceptance of the method. To showcase the functionality of this approach, a simple reproducible example is given on the assembly of the IKEA Hyllis shelf as well as the PUBLIC Bikes Sprout bike.

关键词： Automated assembly planning Rule based system Knowledge representation and reasoning answer set programming

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A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments

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ARTIFICIAL INTELLIGENCE 2025年 340卷

作者： Gebser, Martin Giunchiglia, Enrico Maratea, Marco Mochi, Marco Univ Klagenfurt AICS Klagenfurt Austria Univ Genoa DIBRIS Genoa Italy Univ Calabria DEMACS Arcavacata Di Rende Italy

Stable models of logic programs have been studied and characterized in relation with other formalisms by many researchers. As already argued in previous papers, such characterizations are interesting for diverse reasons, including theoretical investigations and the possibility of leading to new algorithms for computing stable models of logic programs. At the theoretical level, complexity and expressiveness comparisons have brought about fundamental insights. Beyond that, practical implementations of the developed reductions enable the use of existing solvers for other logical formalisms to compute stable models. In this paper, we first provide a simple characterization of stable models that can be viewed as a proof-theoretic counterpart of the standard model- theoretic definition. We further show how it can be naturally encoded in difference logic. Such an encoding, compared to the existing reductions to classical logics, does not require Boolean variables. Then, we implement our novel translation to a Satisfiability Modulo Theories (SMT) formula. We finally compare our approach, employing the SMT solver YICES, to the translation- based ASP solver LP2DIEE and to CLIvGo on domains from the "Basic Decision" track of the 2017 answer set programming competition. The results show that our approach is competitive to and often better than LP2DIEE, and that it can also be faster than CLIvGo on non-tight domains.

关键词： Logic programming Stable models answer set programming Difference logic

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