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

作者： Sugimori, Irumi Inoue, Katsumi Nabeshima, Hidetomo Schaub, Torsten Soh, Takehide Tamura, Naoyuki Banbara, Mutsunori Nagoya Univ Nagoya Aichi Japan Natl Inst Informat Tokyo Japan Univ Yamanashi Kofu Yamanashi Japan Univ Potsdam Potsdam Germany Kobe Univ Kobe Hyogo Japan

ISBN: (纸本)9783031742088;9783031742095

We develop an approach to solve curriculum-based course timetabling (CB-CTT) problems with Large Neighborhood Prioritized Search (LNPS) based on answer set programming (ASP). LNPS is a metaheuristic that starts with an initial solution and then iteratively tries to obtain improved solutions by alternately destroying and prioritized searching for a current solution. Our approach relies on high-level domain-specific LNPS configurations for efficient CB-CTT solving, and both the grounding and solving tasks are delegated to a heuristically-driven answer set optimizer implementing the LNPS algorithm. The resulting system teaspoon-lnps demonstrates that LNPS can significantly enhance the solving performance of ASP for CB-CTT solving. Furthermore, we show the competitiveness of our declarative approach by empirically contrasting it to the best known bounds obtained by more dedicated algorithms.

关键词： answer set programming Large Neighborhood Prioritized Search Course Timetabling

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A Simulation for Supply Chains Contract Execution 17th

A Simulation for Supply Chains Contract Execution

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

作者： Tran, Long Son, Tran Cao Flynn, Dylan Balduccini, Marcello New Mexico State Univ Las Cruces NM 88003 USA St Josephs Univ Philadelphia PA 19131 USA

ISBN: (纸本)9783031742088;9783031742095

Supply chains exhibit complex dynamics and intricate dependencies among their components, whose understanding is crucial for addressing the challenges highlighted by recent global disruptions. This paper presents a novel multi-agent system designed to simulate supply chains, linking reasoning about dynamic domains and multi-agent systems to reasoning about the high-level primitives of the NIST CPS Framework. Our approach synthesizes existing research on supply chain formalization and integrates these insights with multi-agent techniques, employing a declarative approach to model interactions and dependencies. The simulation framework models a set of autonomous agents within a partially observable environment, and whose interactions are dictated by contracts. The system dynamically reconciles agents' actions, assessing their feasibility and consequences. Based on the state of the domain, the simulation framework also draws conclusions about the high-level notions of requirements and concerns of the NIST CPS Framework, which provide a uniform and domain-agnostic vocabulary for the understanding of such complex systems as supply chains.

关键词： answer set programming Dynamic Domains Multi-Agent Systems MAS Simulation Contract Formalization Supply Chains

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answer set Explanations via Preferred Unit-Provable Unsatisfiable Subsets 17th

Answer Set Explanations via Preferred Unit-Provable Unsatisf...

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

作者： Alviano, Mario Hahn, Susana Sabuncu, Orkunt Weichelt, Johannes Univ Calabria DEMACS Via Bucci 30-B I-87036 Arcavacata Di Rende CS Italy Univ Potsdam Potsdam Germany Potassco Solut Potsdam Germany

ISBN: (纸本)9783031742088;9783031742095

Explainability in Artificial Intelligence (XAI) is crucial for enhancing the transparency and trustworthiness of AI systems. Our work focuses on providing clear explanations for why certain atoms in a given answer set are evaluated as such, hence contributing to the understanding of the decisions made by answer set programming (ASP) systems. We employ simple inference rules to elucidate these decisions, avoiding complex derivations to maintain clarity. Moreover, we introduce the notion of preferred unit-provable unsatisfiable subsets (preferred 1-PUS) to identify relevant portions of ASP encodings, prioritizing program rules over assignments, with the objective of minimizing the assumptions involved in the explanation process. The proposed principles are implemented in a new XAI system.

关键词： answer set programming eXplainable Artificial Intelligence Knowledge Representation and Reasoning

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Deductive Systems for Logic Programs with Counting: Preliminary Report 17th

Deductive Systems for Logic Programs with Counting: Prelimin...

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

作者： Fandinno, Jorge Lifschitz, Vladimir Univ Nebraska Omaha Omaha NE 68182 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9783031742088;9783031742095

In answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. Strong equivalence of two programs can be sometimes established by deriving rules of each program from rules of the other in an appropriate deductive system. This paper shows how to extend this method of proving strong equivalence to programs containing the counting aggregate.

关键词： answer set programming Strong Equivalence Deductive Systems Aggregates

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A Weighted Bipolar Argumentation Framework and Its ASP-Based Implementation 27th

A Weighted Bipolar Argumentation Framework and Its ASP-Base...

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27th International Symposium on Practical Aspects of Declarative Languages, PADL 2025

作者： Yan, Yan Li, Junru Liu, Fangzhou Wang, Zerong Zhang, Zhizheng School of Cyber Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Southeast University Nanjing211189 China Ministry of Education Nanjing China

ISBN: (纸本)9783031849237

A novel Weighted Bipolar Argumentation Framework (WBAF) is proposed in this paper, which deals with attack and support relations equally and takes into account the weight of arguments and relations, and is a more general extension of the classical AF. The semantics of WBAF - minimum-penalty preferred extensions is introduced, which refers to the maximal acceptable argument set inclusions. Furthermore, this paper designs and realizes an ASP-based WBAF reasoning tool - ASPWBART (answer set programming Based Weighted Bipolar Argumentation Reasoning Tool). This tool supports the incorporation of weighted arguments and weighted relations, and culminates in the generation of minimum-penalty preferred extensions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： answer set programming Argumentation framework Bipolar argumentation Weighted argumentation

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Declarative Pattern Mining in Network Security

Declarative Pattern Mining in Network Security

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2024 Discovery Science Late Breaking Contributions, DS-LB 2024

作者： Sterlicchio, Gioacchino Lisi, Francesca Alessandra DMMM Polytechnic University of Bari Italy DIB and CILA University of Bari Aldo Moro Italy

This work addresses the problem of detecting patterns of attacks on 4G-LTE network security by relying on the Contrast Sequential Pattern Mining (CSPM) task leveraging the declarative framework of answer set Programmi... 详细信息

关键词： answer set programming Contrast Sequential Pattern Mining Declarative Pattern Mining Network Security

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ASP-Based Multi-Shot Reasoning via DLV2 with Incremental Grounding

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THEORY AND PRACTICE OF LOGIC programming 2025年

作者： Calimeri, Francesco Ianni, Giovambattista Pacenza, Francesco Perri, Simona Zangari, Jessica Univ Calabria Arcavacata Di Rende Italy

DLV2 is an AI tool for knowledge representation and reasoning that supports answer set programming (ASP) - a logic-based declarative formalism, successfully used in both academic and industrial applications. Given a logic program modeling a computational problem, an execution of DLV2 produces the so-called answer sets that correspond one-to-one to the solutions to the problem at hand. The computational process of DLV2 relies on the typical ground & solve approach, where the grounding step transforms the input program into a new, equivalent ground program, and the subsequent solving step applies propositional algorithms to search for the answer sets. Recently, emerging applications in contexts such as stream reasoning and event processing created a demand for multi-shot reasoning: here, the system is expected to be reactive while repeatedly executed over rapidly changing data. In this work, we present a new incremental reasoner obtained from the evolution of DLV2 toward iterated reasoning. Rather than restarting the computation from scratch, the system remains alive across repeated shots, and it incrementally handles the internal grounding process. At each shot, the system reuses previous computations for building and maintaining a large, more general ground program, from which a smaller yet equivalent portion is determined and used for computing answer sets. Notably, the incremental process is performed in a completely transparent fashion for the user. We describe the system, its usage, its applicability, and performance in some practically relevant domains.

关键词： knowledge representation and reasoning nonmonotonic reasoning logic programming answer set programming grounding stream reasoning

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Polynomial Formal Verification of Sequential Circuits Using Weighted-AIGs

Polynomial Formal Verification of Sequential Circuits Using ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mohamed Nadeem Chandan Kumar Jha Rolf Drechsler University of Bremen Bremen Germany DFKI GmbH Bremen Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Ensuring the functional correctness of a digital system is achievable through formal verification. Despite the increased complexity of modern systems, formal verification still needs to be done in a reasonable time. Hence, Polynomial Formal Verification (PFV) techniques are being explored as they provide a guaranteed upper bound on the run time for verification. Recently, it was shown that combinational circuits characterized by a constant cutwidth can be verified in linear time using answer set programming (ASP). However, most of the designs used in digital systems are sequential. Hence, in this paper, we propose a linear time formal verification approach using ASP for sequential circuits with constant cutwidth. We achieve this by proposing a new data structure called Weighted-And Inverter Graph (W-AIG). Unlike existing formal verification methods, we prove that our approach can verify any sequential circuit with a constant cutwidth in a linear time. Finally, we also implement our approach and experimentally show the results on a variety of sequential circuits like pipelined adders, serial adders, and shift registers to confirm our theoretical findings.

关键词： Upper bound Digital systems answer set programming Logic gates Data structures Shift registers Polynomials Sequential circuits Adders Formal verification

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Combining Logic and Large Language Models for Assisted Debugging and Repair of ASP Programs

Combining Logic and Large Language Models for Assisted Debug...

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Ricardo Brancas Vasco Manquinho Ruben Martins INESC-ID/IST - Universidade de Lisboa Lisbon Portugal Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Logic programs are a powerful approach for solving NP-Hard problems. However, their declarative nature poses significant challenges in debugging. Unlike procedural paradigms, which allow for step-by-step inspection of program state, logic programs require reasoning about logical statements for fault localization. This complexity is especially significant in learning environments due to students' inexperience. We introduce FormHe, a novel tool that integrates logic-based techniques with Large Language Models (LLMs) to detect and correct issues in answer set programming submissions. FormHe consists of two main components: a fault localization module and a program repair module. First, the fault localization module identifies specific faulty statements in need of modification. Next, FormHe applies program mutation techniques and leverages LLMs to repair the flawed code. The resulting repairs are then used to generate hints that guide students in correcting their programs. Our experiments with real buggy programs submitted by students show that FormHe accurately detects faults in 94% of cases and successfully repairs 58% of incorrect submissions.

关键词： Location awareness Fault diagnosis Software testing NP-hard problem Large language models answer set programming Debugging Machine learning Maintenance engineering Logic

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Online Inductive Learning from answer sets for Efficient Reinforcement Learning Exploration 3rd

Online Inductive Learning from Answer Sets for Efficient R...

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3rd International Workshop on Hybrid Models for Coupling Deductive and Inductive Reasoning, HYDRA 2024

作者： Veronese, Celeste Meli, Daniele Farinelli, Alessandro Department of Computer Science University of Verona Verona Italy

ISBN: (纸本)9783031893650

This paper presents a novel approach combining inductive logic programming with reinforcement learning to improve training performance and explainability. We exploit inductive learning of answer set programs from noisy examples to learn a set of logical rules representing an explainable approximation of the agent’s policy at each batch of experience. We then perform answer set reasoning on the learned rules to guide the exploration of the learning agent at the next batch, without requiring inefficient reward shaping and preserving optimality with soft bias. The entire procedure is conducted during the online execution of the reinforcement learning algorithm. We preliminarily validate the efficacy of our approach by integrating it into the Q-learning algorithm for the Pac-Man scenario in two maps of increasing complexity. Our methodology produces a significant boost in the discounted return achieved by the agent, even in the first batches of training. Moreover, inductive learning does not compromise the computational time required by Q-learning and learned rules quickly converge to an explanation of the agent’s policy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： answer set programming Explainable AI Inductive Logic programming Neurosymbolic AI Reinforcement Learning

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