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FOLD-RM: A Scalable, Efficient, and Explainable inductive Learning Algorithm for Multi-Category Classification of Mixed Data

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THEORY AND PRACTICE OF logic programming 2022年第5期22卷 658-677页

作者： Wang, Huaduo Shakerin, Farhad Gupta, Gopal Univ Texas Dallas Richardson TX 75083 USA

FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.

关键词： explainable AI data mining inductive logic programming machine learning

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Efficient Lifting of Symmetry Breaking Constraints for Complex Combinatorial Problems

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THEORY AND PRACTICE OF logic programming 2022年第4期22卷 606-622页

作者： Tarzariol, Alice Schekotihin, Konstantin Gebser, Martin Law, Mark Univ Klagenfurt Klagenfurt Austria Graz Univ Technol Graz Austria Imperial Coll London London England

Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the inductive logic programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.

关键词： answer set programming inductive logic programming symmetry breaking constraints

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Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes 17

Neural-Symbolic Predicate Invention: Learning Relational Con...

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17th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy)

作者： Sha, Jingyuan Shindo, Hikaru Kersting, Kristian Dhami, Devendra Singh Tech Univ Darmstadt Darmstadt Germany Hessian Ctr Artificial Intelligence Hessian AI Darmstadt Germany German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

The predicates used for inductive logic programming (ILP) systems are usually elusive and need to be hand-crafted in advance, which limits the generalization of the system when learning new rules without sufficient background knowledge. Predicate Invention (PI) for ILP is the problem of discovering new concepts that describe hidden relationships in the domain. PI can mitigate the generalization problem for ILP by inferring new concepts, giving the system a better vocabulary to compose logic ruless. Although there are several PI approaches for symbolic ILP systems, PI for NeSy ILP systems that can handle visual input to learn logical rules using differentiable reasoning is relatively unaddressed. To this end, we propose a neural-symbolic approach, NeSy-pi, to invent predicates from visual scenes for NeSy ILP systems based on clustering and extension of relational concepts. (pi denotes the abbrivation of Predicate Invention). NeSy-pi processes visual scenes as input using deep neural networks for the visual perception and invents new concepts that support the task of classifying complex visual scenes. The invented concepts can be used by any NeSy ILP systems instead of hand-crafted background knowledge. Our experiments show that the PI model is capable of inventing high-level concepts and solving complex visual logic patterns more efficiently and accurately in the absence of explicit background knowledge. Moreover, the invented concepts are explainable and interpretable, while also providing competitive results with state-of-the-art NeSy ILP systems based on given knowledge.

关键词： Predicate Invention inductive logic programming Neural Symbolic Artificial Intelligence

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Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

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MACHINE LEARNING 2022年第2期111卷 575-623页

作者： Dash, Tirtharaj Srinivasan, Ashwin Baskar, A. BITS Pilani APPCAIR KK Birla Goa Campus Pilani 403726 Goa India BITS Pilani Dept CS & IS KK Birla Goa Campus Pilani 403726 Goa India

We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in inductive logic programming (ILP). Given a data instance e and background knowledge B, MDIE identifies a most-specific logical formula perpendicular to(B)(e) that contains all the relational information in B that is related to e. We represent perpendicular to(B)(e) by a "bottom-graph" that can be converted into a form suitable for GNN implementations. This transformation allows a principled way of incorporating generic background knowledge into GNNs: we use the term 'BotGNN' for this form of graph neural networks. For several GNN variants, using real-world datasets with substantial background knowledge, we show that BotGNNs perform significantly better than both GNNs without background knowledge and a recently proposed simplified technique for including domain knowledge into GNNs. We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons that use features representing a "propositionalised" form of the background knowledge;and BotGNNs to a standard ILP based on the use of most-specific clauses. Taken together, these results point to BotGNNs as capable of combining the computational efficacy of GNNs with the representational versatility of ILP.

关键词： Neuro-symbolic learning inductive logic programming Mode-directed inverse entailment Graph neural networks Background knowledge

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Meta-interpretive learning as metarule specialisation

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MACHINE LEARNING 2022年第10期111卷 3703-3731页

作者： Patsantzis, S. Muggleton, S. H. Imperial Coll London London England

In Meta-interpretive learning (MIL) the metarules, second-order datalog clauses acting as inductive bias, are manually defined by the user. In this work we show that second-order metarules for MIL can be learned by MIL. We define a generality ordering of metarules by theta-subsumption and show that user-defined sort metarules are derivable by specialisation of the most-general matrix metarules in a language class;and that these matrix metarules are in turn derivable by specialisation of third-order punch metarules with variables quantified over the set of atoms and for which only an upper bound on their number of literals need be user-defined. We show that the cardinality of a metarule language is polynomial in the number of literals in punch metarules. We re-frame MIL as metarule specialisation by resolution. We modify the MIL metarule specialisation operator to return new metarules rather than first-order clauses and prove the correctness of the new operator. We implement the new operator as TOIL, a sub-system of the MIL system Louise. Our experiments show that as user-defined sort metarules are progressively replaced by sort metarules learned by TOIL, Louise's predictive accuracy and training times are maintained. We conclude that automatically derived metarules can replace user-defined metarules.

关键词： inductive logic programming Meta interpretive learning Machine learning Top program construction Metarules Metarule learning Second order learning

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Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework

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MACHINE LEARNING 2022年第4期111卷 1523-1549页

作者： Mitchener, Ludovico Tuckey, David Crosby, Matthew Russo, Alessandra Imperial Coll London Exhibit Rd London SW7 2BX England

In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.

关键词： Neuro-symbolic Hierarchical reinforcement learning Deep reinforcement learning inductive logic programming Answer set programming

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Explaining Optimal Trajectories 7th

Explaining Optimal Trajectories

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7th International Joint Conference on Rules and Reasoning (RuleML+RR)

作者： Rouveirol, Celine Aoual, Malik Kazi Soldano, Henry Ventos, Veronique Nukkai Paris France Univ Sorbonne Paris Nord CNRS UMR 7030 Inst GalileeLIPN Villetaneuse France ISYEB Museum Natl Hist Nat CNRS UMR 7205 Paris France

ISBN: (纸本)9783031450716;9783031450723

We propose a definition of common explanation for the label shared by a group of observations described as first order interpretations, and provide algorithms to enumerate minimal common explanations. This was motivated by explaining how performing some action, for instance a card played during a card game play, results in winning a maximum total reward at the end of the trajectory. As there are various ways to reach this reward, each associated to a group of trajectories, we propose to first build groups of trajectories and then build minimal common explanations for each group. The whole method is illustrated on a simplified Bridge game.

关键词： Abductive Explanation inductive logic programming Markov Decision Process

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Learning Distributional Programs for Relational Autocompletion

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THEORY AND PRACTICE OF logic programming 2022年第1期22卷 81-114页

作者： Kumar, Nitesh Kuzelka, Ondrej De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Czech Tech Univ Dept Comp Sci Prague Czech Republic

Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML - an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation-maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.

关键词： probabilistic logic programming statistical relational learning structure learning inductive logic programming

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FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm with Scalable Explainability 26th

FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm ...

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26th International Symposium on Practical Aspects of Declarative Languages (PADL)

作者： Wang, Huaduo Gupta, Gopal Univ Texas Dallas Richardson TX 75080 USA

ISBN: (纸本)9783031520372;9783031520389

We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. The (explainable) model generated by FOLD-SE is represented as a set of default rules. FOLD-SE uses a novel heuristic called Magic Gini Impurity for literal selection that we have devised. FOLD-SE uses a refined data comparison operator and eliminates the long tail effect. Thanks to these innovations, explainability provided by FOLD-SE is scalable, meaning that regardless of the size of the dataset, the number of learned rules and learned literals stay quite small while good accuracy in classification is maintained. Additionally, the rule-set constituting the model that FOLD-SE generates does not change significantly if the training data is slightly varied. FOLD-SE is competitive with state-of-the-art traditional machine learning algorithms such as XGBoost and Multi-Layer Perceptrons (MLP) w.r.t. accuracy of prediction while being an order of magnitude faster. However, unlike XGBoost and MLP, FOLD-SE generates explainable models. The FOLD-SE algorithm outperforms prior rule-learning algorithms such as RIPPER in efficiency, performance, and scalability, especially for large datasets. FOLD-SE generates a far smaller number of rules than earlier algorithms that learn default rules.

关键词： inductive logic programming Machine Learning Explainable AI Negation as Failure Answer Set programming Data mining

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On the non-efficient PAC learnability of conjunctive queries

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INFORMATION PROCESSING LETTERS 2024年 183卷

作者： Cate, Balder ten Funk, Maurice Jung, Jean Christoph Lutz, Carsten Univ Amsterdam ILLC Postbus 94242 NL-1090 GE Amsterdam Netherlands Univ Leipzig Augustuspl 10 D-04109 Leipzig Germany TU Dortmund Univ August Schmidt Str 1 D-44227 Dortmund Germany

This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature;(ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of acyclicity;(iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

关键词： Computational learning theory Conjunctive queries inductive logic programming Databases

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