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17th international conference on logic programming and Non-monotonic Reasoning

作者： Dreossi, Talissa Dovier, Agostino Formisano, Andrea Law, Mark Manzato, Agostino Russo, Alessandra Tait, Matthew Univ Udine Udine Italy ILASP Ltd London England ARPA FVG OSMER Udine Italy Imperial Coll London London England

ISBN: (纸本)9783031742088;9783031742095

Weather forecasting is important for saving lives, protecting property, and supporting economic activities. It provides timely warnings for severe weather, improves agricultural planning, and aids in disaster management. Neural networks and deep learning methods can achieve impressive accuracy in weather prediction, but their black-box nature lacks in explainability. To address this limitation, we investigated the potential of FastLAS, an inductive logic programming (ilp) framework, to produce reliable and, more important, explainable weather predictions. FastLAS learns ASP programs whose syntax and structural semantics resemble natural human language, making them easily understandable and interpretable by humans. the supportedness of stable models allows a clear explanation of the predictions. Our empirical evaluation on data from an Italian weather forecasting center shows that our approach is capable of learning predictive models from small dataset (a few samples instead of the thousands needed by neural networks) achieving an accuracy higher than statistical machine learning base lines.

关键词： ilp logic programming AI Explainability

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Computable Relations Mapping with Horn Clauses for inductive Program Synthesis 20th

Computable Relations Mapping with Horn Clauses for Inducti...

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Qiu, Taosheng Ichise, Ryutaro National Institute of Informatics Tokyo Japan Tokyo Institute of Technology Tokyo Japan The Graduate University for Advanced Studies SOKENDAI Tokyo Japan

ISBN: (纸本)9789819600250

inductive logic programming (ilp) systems use logic programming languages like Prolog as computational models. Expressivity of these languages is often built on fixed Herbrand vocabulary, thus requires encoding arbitrarily large structures as nested functions. However, most learning systems process structures directly on relations and cannot handle function symbols. To solve this, we propose an alternative problem framework to measure the expressivity of learning systems, by considering any Turing computable mapping between arbitrarily large graphs, namely Computable Relations Mapping (CRM). CRM is a useful framework for its connection to Turing machine and straightforward representation of most computational tasks. then, we propose a minimal CRM-complete fragment of Horn clauses with default negation and function symbols, by constructing programs corresponding to all CRMs. the correctness is given by combining a checked implementation and some proof. Since smaller rules can be more efficiently searched and analyzed, we consider it a potentially better representation and reasoning model for expressive inductive inference systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Turing machines

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Learning large logic programs by going beyond entailment 29

Learning large logic programs by going beyond entailment

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29th international Joint conference on Artificial Intelligence, IJCAI 2020

作者： Cropper, Andrew Dumancic, Sebastijan University of Oxford United Kingdom KU Leuven Belgium

ISBN: (纸本)9780999241165

A major challenge in inductive logic programming (ilp) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. this approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use example-dependent loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ilp system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ilp systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： inductive logic programming (ilp)

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Inducing Rules about Distributed Robotic Systems for Fault Detection & Diagnosis 21

Inducing Rules about Distributed Robotic Systems for Fault D...

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Proceedings of the 20th international conference on Autonomous Agents and MultiAgent Systems

作者： Youssef Mahmoud Youssef Hochschule Bonn-Rhein-Sieg Bonn Germany

ISBN: (纸本)9781450383073

this paper presents an extended abstract for the PhD topic Inducing Rules about Distributed Robotic Systems for Fault Detection & Diagnosis. the research focuses on developing novel methods for fault detection and diagnosis using explainable machine learning. the main field of application is distributed robotic systems. With current developments in distributed robot technology, the problem of detecting and diagnosing faults becomes more complex.

关键词： robotics explainable machine learning inductive logic programming fault detection & diagnosis

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A new algorithm to automate inductive learning of default theories

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thEORY AND PRACTICE OF logic programming 2017年第5-6期17卷 1010-1026页

作者： Shakerin, Farhad Salazar, Elmer Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming. Under consideration for acceptance in TPLP.

关键词： inductive logic programming non-monotonic logic programming default reasoning common-sense reasoning machine learning

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A new algorithm to automate inductive learning of default theories

A new algorithm to automate inductive learning of default th...

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33rd international conference on logic programming colocated with the 23rd international conference on Principles and Practice of Constraint programming / 20th international conference on theory and Applications of Satisfiability Testing

作者： Shakerin, Farhad Salazar, Elmer Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

关键词： inductive logic programming non-monotonic logic programming default reasoning common-sense reasoning machine learning

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Productive corecursion in logic programming

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thEORY AND PRACTICE OF logic programming 2017年第5-6期17卷 906-923页

作者： Komendantskaya, Ekaterina Li, Yue Heriot Watt Univ Edinburgh Midlothian Scotland

logic programming is a Turing complete language. As a consequence, designing algorithms that decide termination and non-termination of programs or decide inductive/ coinductive soundness of formulae is a challenging task. For example, the existing state-of-the-art algorithms can only semi-decide coinductive soundness of queries in logic programming for regular formulae. Another, less famous, but equally fundamental and important undecidable property is productivity. If a derivation is infinite and coinductively sound, we may ask whether the computed answer it determines actually computes an infinite formula. If it does, the infinite computation is productive. this intuition was first expressed under the name of computations at infinity in the 80s. In modern days of the Internet and stream processing, its importance lies in connection to infinite data structure processing. Recently, an algorithm was presented that semi-decides a weaker property -of productivity of logic programs. A logic program is productive if it can give rise to productive derivations. In this paper, we strengthen these recent results. We propose a method that semi-decides productivity of individual derivations for regular formulae. thus, we at last give an algorithmic counterpart to the notion of productivity of derivations in logic programming. this is the first algorithmic solution to the problem since it was raised more than 30 years ago. We also present an implementation of this algorithm.

关键词： Horn clauses (co) recursion (co) induction infinite term trees productivity

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Extracting rules for successful conditions for artificial insemination in dairy cattle using inductive logic programming 2017

Extracting rules for successful conditions for artificial in...

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9th international conference on Machine Learning and Computing, ICMLC 2017

作者： Matsumoto, Atsushi Kubota, Chikara Ohwada, Hayato Department of Industrial Administration Faculty of Science and Technology Tokyo University of Science 2641 Yamazaki Noda Chiba278-8510 Japan Joint Faculty of Veterinary Medicine Laboratory of Theriogenology Kagoshima University 1-24-24 Korimoto Kagoshima Kagoshima890-0065 Japan

ISBN: (纸本)9781450348171

this paper describes a method of pattern extraction to identify successful conditions for artificial insemination in dairy cattle using inductive logic programming. the fertility of lactating dairy cows is economically important but the mean reproductive performance of Holstein cows has declined during the past three decades. the Milk production increases after birth and gradually decreases after several weeks, but it increases again upon the next birth. Increasing the probability of successful artificial insemination is thus economically important. However the success rate of artificial insemination is 20 to 30 %. the amount of progesterone is closely related to the estrus cycle, and can be measured from raw milk. In this study, we extracted variation pattern from time-series data related to dairy cattle including the amount of progesterone using ilp. the success pattern or failure patterns for artificial insemination extracted by the ilp system can be used for decision making on artificial insemination. © 2017 ACM.

关键词： Decision making

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Probabilistic inductive logic programming on the Web 20th

Probabilistic Inductive Logic Programming on the Web

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20th international conference on Knowledge Engineering and Knowledge Management (EKAW)

作者： Riguzzi, Fabrizio Zese, Riccardo Cota, Giuseppe Univ Ferrara Dipartimento Matemat & Informat Via Saragat 1 I-44122 Ferrara Italy Univ Ferrara Dipartimento Ingn Via Saragat 1 I-44122 Ferrara Italy

ISBN: (纸本)9783319586946;9783319586939

Probabilistic inductive logic programming (Pilp) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among Pilp systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning algorithms for PLP, SLIPCOVER. Moreover, an online version was recently developed, cplint on SWISH, that allows users to experiment with the system using just a web browser. In this demo we illustrate cplint on SWISH concentrating on structure learning with SLIPCOVER. cplint on SWISH also includes many examples and a step-by-step tutorial.

关键词： Learning algorithms

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Productive corecursion in logic programming

Productive corecursion in logic programming

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作者： Komendantskaya, Ekaterina Li, Yue Heriot Watt Univ Edinburgh Midlothian Scotland

关键词： Horn clauses (co) recursion (co) induction infinite term trees productivity

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