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

作者： Chiariello, Francesco Ielo, Antonio Tarzariol, Alice Univ Toulouse IRIT ANITI Toulouse France Univ Calabria Arcavacata Di Rende Italy Univ Klagenfurt Klagenfurt Austria

ISBN: (纸本)9783031742088;9783031742095

Petri nets are a class of models of computation used to compactly represent discrete event systems. Among many application domains, they have now become the most prominent formalism to express process models in Process Mining, thanks to their formal semantics that enables automated analysis techniques. In this context, model repair is the task of aligning a process model with actual executions of the process. Current solutions to model repair do not allow for embedding domain knowledge, providing guarantees of rigor, and enforcing structural requirements at the same time. In this paper, we fill this gap by proposing an approach based on the inductive logic programming system ILASP. We then implement our approach and perform an experimental evaluation, showing both its expressiveness and feasibility.

关键词： Petri nets Process Mining Model Repair Answer Set programming inductive logic programming

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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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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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A Hardware Approach For Accelerating inductive Learning In Description logic

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2024年第4期23卷 1-37页

作者： Algahtani, Eyad King Saud Univ Riyadh Saudi Arabia

The employment of Machine Learning (ML) techniques in embedded systems has seen constant growth in recent years, especially for black-box ML techniques (such as Artificial Neural Networks (ANNs)). However, despite the successful employment of ML techniques in embedded environments, their performance potential is constrained by the limited computing resources of their embedded computers. Several hardware-based approaches were developed (e.g., using FPGAs and ASICs) to address the constraints of limited computing resources. The scope of this work focuses on improving the performance for inductive logic programming (ILP) on embedded environments. ILP is a powerful logic-based ML technique that uses logic programming to construct human-interpretable ML models, where those logic-based ML models are capable of describing complex and multi-relational concepts. In this work, we present a hardware-based approach that accelerates the hypothesis evaluation task for ILPs in embedded environments that use Description logic (DL) languages as their logic-based representation. In particular, we target the ALCQ((D)) language. According to experimental results (through an FPGA implementation), our presented approach has achieved speedups up to 48.7-fold for a disjunction of 32 concepts on 100 M individuals, where the baseline performance is the sequential CPU performance of the Raspberry Pi 4. For role and concrete role restrictions, the FPGA implementation achieved speedups up to 2.4-fold (for MIN cardinality role restriction on 1M role assertions);all FPGA implemented role and concrete role restrictions have achieved similar speedups. In the worst-case scenario, the FPGA implementation achieved either a similar or slightly better performance than the baseline (for all DL operations);the worst-case scenario resulted from using small datasets such as: using conjunction and disjunction on < 100 individuals, and using role and concrete (float/string) role restrictions on < 100,000 asse

关键词： inductive logic programming description logic embedded artificial intelligence FPGA machine learning

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AUTOMATED REFINEMENT OF FIRST-ORDER HORN-CLAUSE DOMAIN THEORIES

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MACHINE LEARNING 1995年第2期19卷 95-131页

作者： RICHARDS, BL MOONEY, RJ UNIV TEXAS DEPT COMP SCI AUSTIN TX 78712 USA

Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, FORTE (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.

关键词： THEORY REVISION KNOWLEDGE REFINEMENT inductive logic programming

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Toward Effective Knowledge Acquisition with First—Order logic Induction

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Journal of Computer Science & Technology 2002年第5期17卷 565-577页

作者：张晓龙 Masyuki Numao IT Solution Delivery AXA Life Insurance Company Tokyo 194-0022 Japan Department of Computer Science Tokyo Institute of Technology Tokyo 152-8552 Japan

Knowledge acquisition with machine learning techniques is a fundamental re-quirement for knowledge discovery from databases and data mining systems. Two techniquesin particular - inductive learning and theory revision - have been used toward this end. Amethod that combines both approaches to effectively acquire theories (regularity) from a setof training examples is presented. inductive learning is used to acquire new regularity fromthe training examples; and theory revision is used to improve an initial theory. In addition, atheory preference criterion that is a combination of the MDL-based heuristic and the Laplaceestimate has been successfully employed in the selection of the promising theory. The resultingalgorithm developed by integrating inductive learning and theory revision and using the criterionhas the ability to deal with complex problems, obtaining useful theories in terms of its predictiveaccuracy.

关键词： knowledge acquisition inductive logic programming theory revision the MDLprinciple the laplace estimate noisy data

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The complexity of theory revision

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ARTIFICIAL INTELLIGENCE 1999年第2期107卷 175-217页

作者： Greiner, R Univ Alberta Dept Comp Sci Edmonton AB T6G 2H1 Canada

A knowledge-based system uses its database (also known as its "theory") to produce answers to the queries it receives. Unfortunately, these answers may be incorrect if the underlying theory is faulty. Standard "theory revision" systems use a given set of "labeled queries" (each a query paired with its correct answer) to transform the given theory, by adding and/or deleting either rules and/or antecedents, into a related theory that is as accurate as possible. After formally defining the theory revision task, this paper provides both sample and computational complexity bounds for this process. It first specifies the number of labeled queries necessary to identify a revised theory whose error is close to minimal with high probability. It then considers the computational complexity of finding this best theory, and proves that, unless P = NP, no polynomial-time algorithm can identify this optimal revision, even given the exact distribution of queries, except in certain simple situations. It also shows that, except in such simple situations, no polynomial-time algorithm can produce a theory whose error is even close to (i.e., within a particular polynomial factor of) optimal. The first (sample complexity) results suggest reasons why theory revision can be more effective than learning from scratch, while the second (computational complexity) results explain many aspects of the standard theory revision systems, including the practice of hill-climbing to a locally-optimal theory, based on a given set of labeled queries. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： theory revision computational learning theory inductive logic programming agnostic learning

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A process framework for inducing and explaining Datalog theories

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ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2020年第4期14卷 821-835页

作者： Gromowski, Mark Siebers, Michael Schmid, Ute Univ Bamberg Cognit Syst Weberei 5 D-96049 Bamberg Germany

With the increasing prevalence of Machine Learning in everyday life, a growing number of people will be provided with Machine-Learned assessments on a regular basis. We believe that human users interacting with systems based on Machine-Learned classifiers will demand and profit from the systems' decisions being explained in an approachable and comprehensive way. We developed a general process framework for logic-rule-based classifiers facilitating mutual exchange between system and user. The framework constitutes a guideline for how a system can apply inductive logic programming in order to provide comprehensive explanations for classification choices and empowering users to evaluate and correct the system's decisions. It also includes users' corrections being integrated into the system's core logic rules via retraining in order to increase the overall performance of the human-computer system. The framework suggests various forms of explanations-like natural language argumentations, near misses emphasizing unique characteristics, or image annotations-to be integrated into the system.

关键词： Machine learning inductive logic programming Interpretability Explanations Explainability

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Learning programs by learning from failures

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MACHINE LEARNING 2021年第4期110卷 801-856页

作者： Cropper, Andrew Morel, Rolf Univ Oxford Oxford England

We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.

关键词： inductive logic programming Program synthesis Program induction

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A novel ILP framework for summarizing content with high lexical variety

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NATURAL LANGUAGE ENGINEERING 2018年第6期24卷 887-920页

作者： Luo, Wencan Liu, Fei Liu, Zitao Litman, Diane Univ Pittsburgh Pittsburgh PA 15260 USA Univ Cent Florida Orlando FL 32816 USA Pinterest Inc San Francisco CA 94103 USA

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word cooccurrence matrix to intrinsically group semantically similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.

关键词： inductive logic programming news services news Frameworks Dataset

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