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25th international conference on inductive logic programming, ilp 2015

25th international conference on inductive logic programming, ilp 2015

ISBN: (纸本)9783319405650

the proceedings contain 14 papers. the special focus in this conference is on inductive logic programming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random forests;distributed parameter learning for probabilistic ontologies;meta-interpretive learning of data transformation programs;statistical relational learning with soft quantifiers;ontology learning from interpretations in lightweight description logics;constructing markov logic networks from first-order default rules;a note on mining all graphs;processing markov logic networks with GPUs;using ilp to identify pathway activation patterns in systems biology;an algebraic prolog for kernel programming;an exercise in declarative modeling for relational query mining;learning inference by induction and identification of transition models of biological systems in the presence of transition noise.

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Towards Explainable Weather Forecasting through FastLAS 17th

Towards Explainable Weather Forecasting Through FastLAS

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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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Bridging Deep Learning and logic programming for Explainability through ilp 40

Bridging Deep Learning and Logic Programming for Explainabil...

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40th international conference on logic programming (ICLP)

作者： Dreossi, Talissa Univ Udine Udine Italy

My research explores integrating deep learning and logic programming to set the basis for a new generation of AI systems. By combining neural networks with inductive logic programming (ilp), the goal is to construct systems that make accurate predictions and generate comprehensible rules to validate these predictions. Deep learning models process and analyze complex data, while ilp techniques derive logical rules to prove the network's conclusions. Explainable AI methods, like eXplainable Answer Set programming (XASP), elucidate the reasoning behind these rules and decisions. the focus is on applying ilp frameworks, specifically ILASP and FastLAS, to enhance explainability in various domains. My test cases span weather prediction, the legal field, and image recognition. In weather forecasting, the system will predict events and provides explanations using FastLAS, with plans to integrate recurrent neural networks in the future. In the legal domain, the research focuses on interpreting vague decisions and assisting legal professionals by encoding Italian legal articles and learning reasoning patterns from Court of Cassation decisions using ILASP. For biological laboratories, we will collaborate with a research group to automate spermatozoa morphology classification for Bull Breeding Soundness Evaluation using YOLO networks and ilp to explain classification outcomes. this hybrid approach aims to bridge the gap between the high performance of deep learning models and the transparency of symbolic reasoning, advancing AI by providing interpretable and trustworthy applications.

关键词： Weather forecasting

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Ontology-Based Skill Description Learning for Flexible Production Systems 25

Ontology-Based Skill Description Learning for Flexible Produ...

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25th IEEE international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Himmelhuher, Anna Grimm, Stephan Runkler, thomas Zillner, Sonja Siemens AG Munich Germany

ISBN: (纸本)9781728189567

the increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated.

关键词： skill description learning ontology-based flexible manufacturing semantic web inductive logic programming class expression learning

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Towards an inductive logic programming Approach for Explaining Black-Box Preference Learning Systems

Towards an Inductive Logic Programming Approach for Explaini...

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17th international conference on Principles of Knowledge Representation and Reasoning (KR and R)

作者： D'Asaro, Fabio A. Spezialetti, Matteo Raggioli, Luca Rossi, Silvia Univ Naples Federico II Naples Italy Univ Aquila Laquila Italy CRdC Tecnol Naples Italy

ISBN: (纸本)9780999241172

In this paper we advocate the use of inductive logic programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ilp system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas.

关键词： Support vector machines

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Relational data factorization

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MACHINE LEARNING 2017年第12期106卷 1867-1904页

作者： Paramonov, Sergey van Leeuwen, Matthijs De Raedt, Luc Katholieke Univ Leuven Machine Learning Dept Comp Sci Leuven Belgium Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. thus, relational data factorization is a relational analog of matrix factorization;it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. the result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.

关键词： Answer set programming inductive logic programming Pattern mining Relational data Factorization Data mining Declarative modeling

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Planning in hybrid relational MDPs

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MACHINE LEARNING 2017年第12期106卷 1905-1932页

作者： Nitti, Davide Belle, Vaishak De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Leuven Belgium Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Katholieke Univ Leuven Fac Engn Sci B-3001 Leuven Belgium

We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects. this combination of hybrid relational domains has so far not received a lot of attention. While both relational and hybrid approaches have been studied separately, planning in such domains is still challenging and often requires restrictive assumptions and approximations. We propose HYPE: a sample-based planner for hybrid relational domains that combines model-based approaches with state abstraction. HYPE samples episodes and uses the previous episodes as well as the model to approximate the Q-function. In addition, abstraction is performed for each sampled episode, this removes the complexity of symbolic approaches for hybrid relational domains. In our empirical evaluations, we show that HYPE is a general and widely applicable planner in domains ranging from strictly discrete to strictly continuous to hybrid ones, handles intricacies such as unknown objects and relational models. Moreover, empirical results showed that abstraction provides significant improvements.

关键词： MDP Probabilistic planning logic programming Relational MDP Hybrid Hybrid relational MDP Probabilistic programming Abstraction logical regression Importance sampling

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Learning disjunctive logic programs from interpretation transition 26

Learning disjunctive logic programs from interpretation tran...

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26th international conference on inductive logic programming (Short Papers), ilp 2016

作者： Huang, Yi Wang, Yisong Zhang, Ying Zhang, Mingyi Guizhou University Guiyang550025 China Guizhou Academy of Sciences Guiyang550001 China Chongqing University of Arts and Sciences Chongqing402160 China

We present a new framework for learning disjunctive logic programs from interpretation transitions, called LFDT. It is a nontrivial extension to Inoue, Ribeiro and Sakama's LF1T learning framework, which learns normal logic programs from interpretation transitions. Two resolutions for disjunctive rules are also presented and used in LFDT to simplify learned disjunctive rules.

关键词： Computer circuits

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inductive logic programming: 24th international conference, ilp 2014 Nancy, France, September 14–16, 2014 Revised Selected Papers 24th

Inductive Logic Programming: 24th International Conference, ...

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24th international conference on inductive logic programming, ilp 2014

作者： Davis, Jesse Ramon, Jan Department of Computer Science KU Leuven Leuven Belgium

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Experiences Using an Interactive Presentation Platform in a Functional and logic programming Course 17

Experiences Using an Interactive Presentation Platform in a ...

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25th ACM international conference on User Modeling, Adaptation and Personalization (UMAP)

作者： Triglianos, Vasileios Labaj, Martin Moro, Robert Simko, Jakub Hucko, Michal Tvarozek, Jozef Pautasso, Cesare Bielikova, Maria USI Lugano Fac Informat Software Inst Lugano Switzerland Slovak Univ Technol Bratislava Fac Informat & Informat Technol Bratislava Slovakia

ISBN: (纸本)9781450350679

Modern constructivist approaches to education dictate active experimentation with the study material and have been linked with improved learning outcomes in STEM fields. During classroom time we believe it is important for students to experiment with the lecture material since active recall helps them to start the memory encoding process as well as to catch misconceptions early and to prevent them from taking root. In this paper, we report on our experiences using ASQ, a Web-based interactive presentation tool in a functional and logic programming course taught at the Faculty of Informatics and Information Technologies at the Slovak University of Technology in Bratislava. ASQ allowed us to collect immediate feedback from students and retain their attention by asking complex types of questions and aggregating student answers in real time. From our experience we identified several requirements and guidelines for successfully adopting ASQ. One of the most critical concerns was how to estimate the time when to stop collecting the students' answers and proceed to their evaluation and discussion with the class. We also report the students' feedback on the ASQ system that we collected in the form of the standard SUS questionnaire.

关键词： functional and logic programming technology enhanced learning experience report clickers interactive lecture asq

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