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MP-HTHEDL: A Massively Parallel Hypothesis Evaluation Engine in Description logic

IEEE ACCESS 2024年 12卷 89113-89123页

作者： Algahtani, Eyad King Saud Univ Coll Appl Comp Sci Dept Informat Syst Riyadh 145111 Saudi Arabia

We present MP-HTHEDL, a massively parallel hypothesis evaluation engine for inductive learning in description logic (DL). MP-HTHEDL is an extension on our previous work HT-HEDL, which also targets improving hypothesis evaluation performance for inductive logic programming (ILP) algorithms, that uses DL as their representation language. Unlike our previous work (HT-HEDL), MP-HTHEDL is a massively parallel approach that improves hypothesis evaluation performance through horizontal scaling, by exploiting the computing capabilities of all CPUs and GPUs from networked machines in Hadoop clusters. Many modern CPUs, have extended instruction sets for accelerating specific types of computations - especially for data parallel or vector computations. For CPU-based hypothesis evaluation, MP-HTHEDL employs vectorized multiprocessing as opposed to HT-HEDL's vectorized multithreading;though, both MP-HTHEDL and HT-HEDL combine the classical scalar processing of multi-core CPUs with the extended vector instructions of each CPU core. This combination of CPUs' scalar and vector processing, resulted in more extracted performance from CPUs. According to experimental results through Apache Spark implementation, on a Hadoop cluster of 3 worker nodes that have a total of 36 CPU cores and 7 GPUs;the performance improvement achieved using the pure scalar processing power of multi-core CPUs, has yielded a speedup of up to similar to 25.4 folds. When combining the scalar-processing and the extended vector instructions of those multi-core CPUs, the performance gains increased from similar to 25.4 folds to similar to 67 folds, on the same cluster of 3 worker nodes - these large speedups are achieved using only CPU-based processing. In terms of GPU-based evaluation, MP-HTHEDL achieved a speedup of up to similar to 161 folds, using the GPUs from the same 3 worker nodes.

关键词： logic Engines Description logic Knowledge based systems Cognition Big Data Machine learning Learning systems inductive logic programming description logic GPU big data MapReduce Apache Spark

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Parameter Screening and Optimisation for ILP using Designed Experiments

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JOURNAL OF MACHINE LEARNING RESEARCH 2011年第2期12卷 627-662页

作者： Srinivasan, Ashwin Ramakrishnan, Ganesh S Asian Univ Sch Math Sci New Delhi 110067 India S Asian Univ ICT New Delhi 110067 India Indian Inst Technol Dept Comp Sci & Engn Bombay 400076 Maharashtra India Univ New S Wales Sch CSE Sydney NSW Australia

Reports of experiments conducted with an inductive logic programming system rarely describe how specific values of parameters of the system are arrived at when constructing models. Usually, no attempt is made to identify sensitive parameters, and those that are used are often given "factory-supplied" default values, or values obtained from some non-systematic exploratory analysis. The immediate consequence of this is, of course, that it is not clear if better models could have been obtained if some form of parameter selection and optimisation had been performed. Questions follow inevitably on the experiments themselves: specifically, are all algorithms being treated fairly, and is the exploratory phase sufficiently well-defined to allow the experiments to be replicated? In this paper, we investigate the use of parameter selection and optimisation techniques grouped under the study of experimental design. Screening and response surface methods determine, in turn, sensitive parameters and good values for these parameters. Screening is done here by constructing a stepwise regression model relating the utility of an ILP system's hypothesis to its input parameters, using systematic combinations of values of input parameters (technically speaking, we use a two-level fractional factorial design of the input parameters). The parameters used by the regression model are taken to be the sensitive parameters for the system for that application. We then seek an assignment of values to these sensitive parameters that maximise the utility of the ILP model. This is done using the technique of constructing a local "response surface". The parameters are then changed following the path of steepest ascent until a locally optimal value is reached. This combined use of parameter selection and response surface-driven optimisation has a long history of application in industrial engineering, and its role in ILP is demonstrated using well-known benchmarks. The results suggest that computatio

关键词： inductive logic programming parameter screening and optimisation experimental design

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Confirmation-guided discovery of first-order rules with Tertius

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MACHINE LEARNING 2001年第1-2期42卷 61-95页

作者： Flach, PA Lachiche, N Univ Bristol Dept Comp Sci Bristol BS8 1TH Avon England

This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal best-first search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal either with individual-based representations by upgrading propositional representations to first-order, or with general logical rules. We describe a number of experiments demonstrating the feasibility and flexibility of our approach.

关键词： first-order inductive learning inductive logic programming knowledge discovery

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Meta-Interpretive Learning from noisy images

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MACHINE LEARNING 2018年第7期107卷 1097-1118页

作者： Muggleton, Stephen Dai, Wang-Zhou Sammut, Claude Tamaddoni-Nezhad, Alireza Wen, Jing Zhou, Zhi-Hua Imperial Coll London Dept Comp London England Nanjing Univ LAMDA Grp Nanjing Jiangsu Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia Univ Surrey Dept Comp Sci Guildford Surrey England Shanxi Univ Sch Comp & Informat Technol Taiyuan Shanxi Peoples R China

Statistical machine learning is widely used in image classification. However, most techniques (1) require many images to achieve high accuracy and (2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. This paper describes an inductive logic programming approach called logical Vision which overcomes some of these limitations. LV uses Meta-Interpretive Learning (MIL) combined with low-level extraction of high-contrast points sampled from the image to learn recursive logic programs describing the image. In published work LV was demonstrated capable of high-accuracy prediction of classes such as regular polygon from small numbers of images where Support Vector Machines and Convolutional Neural Networks gave near random predictions in some cases. LV has so far only been applied to noise-free, artificially generated images. This paper extends LV by (a) addressing classification noise using a new noise-telerant version of the MIL system Metagol, (b) addressing attribute noise using primitive-level statistical estimators to identify sub-objects in real images, (c) using a wider class of background models representing classical 2D shapes such as circles and ellipses, (d) providing richer learnable background knowledge in the form of a simple but generic recursive theory of light reflection. In our experiments we consider noisy images in both natural science settings and in a RoboCup competition setting. The natural science settings involve identification of the position of the light source in telescopic and microscopic images, while the RoboCup setting involves identification of the position of the ball. Our results indicate that with real images the new noise-robust version of LV using a single example (i.e. one-shot LV) converges to an accuracy at least comparable to a thirty-shot statistical machine learner on bot

关键词： inductive logic programming Meta-interpretive learning High-level vision Robocup

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The subsumption lattice and query learning

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2006年第1期72卷 72-94页

作者： Khardon, R Arias, M Tufts Univ Dept Comp Sci Medford MA 02155 USA Columbia Univ Ctr Computat Learning Syst New York NY 10115 USA

The paper identifies several new properties of the lattice induced by the subsumption relation over first-order clauses and derives implications of these for learnability. In particular, it is shown that the length of subsumption chains of function free clauses with bounded size can be exponential in the size. This suggests that simple algorithmic approaches that rely on repeating minimal subsumption-based refinements may require a long time to converge. It is also shown that with bounded size clauses the subsumption lattice has a large branching factor. This is used to show that the class of first-order length-bounded monotone clauses is not properly learnable from membership queries alone. Finally, the paper studies pairing, a generalization operation that takes two clauses and returns a number of possible generalizations. It is shown that there are clauses with an exponential number of pairing results which are not related to each other by subsumption. This is used to show that recent pairing-based algorithms can make exponentially many queries on some learning problems. (c) 2005 Elsevier Inc. All rights reserved.

关键词： subsumption lattice query learning inductive logic programming learning theory

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Inverse reinforcement learning through logic constraint inference

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MACHINE LEARNING 2023年第7期112卷 2593-2618页

作者： Baert, Mattijs Leroux, Sam Simoens, Pieter Univ Ghent imec Dept Informat Technol IDLab Technol pk 126 B-9052 Ghent Belgium

Autonomous robots start to be integrated in human environments where explicit and implicit social norms guide the behavior of all agents. To assure safety and predictability, these artificial agents should act in accordance with the applicable social norms. However, it is not straightforward to define these rules and incorporate them in an agent's policy. Particularly because social norms are often implicit and environment specific. In this paper, we propose a novel iterative approach to extract a set of rules from observed human trajectories. This hybrid method combines the strengths of inverse reinforcement learning and inductive logic programming. We experimentally show how our method successfully induces a compact logic program which represents the behavioral constraints applicable in a Tower of Hanoi and a traffic simulator environment. The induced program is adopted as prior knowledge by a model-free reinforcement learning agent to speed up training and prevent any social norm violation during exploration and deployment. Moreover, expressing norms as a logic program provides improved interpretability, which is an important pillar in the design of safe artificial agents, as well as transferability to similar environments.

关键词： inductive logic programming Inverse reinforcement learning Answer set programming Constraint inference Constrained Markov decision process

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Composition of relational features with an application to explaining black-box predictors

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MACHINE LEARNING 2024年第3期113卷 1091-1132页

作者： Srinivasan, Ashwin Baskar, A. Dash, Tirtharaj Shah, Devanshu BITS Pilani Dept CS&IS KK Birla Goa Campus Pilani Goa India BITS Pilani APPCAIR Pilani Goa India Univ Calif San Diego San Diego CA USA

Three key strengths of relational machine learning programs like those developed in inductive logic programming (ILP) are: (1) The use of an expressive subset of first-order logic that allows models that capture complex relationships amongst data instances;(2) The use of domain-specific relations to guide the construction of models;and (3) The models constructed are human-readable, which is often one step closer to being human-understandable. The price for these advantages is that ILP-like methods have not been able to capitalise fully on the rapid hardware, software and algorithmic developments fuelling current developments in deep neural networks. In this paper, we treat relational features as functions and use the notion of generalised composition of functions to derive complex functions from simpler ones. Motivated by the work of McCreath and Sharma, we formulate the notion of a set of M-simple features in a mode language M and identify two composition operators (rho(1) and rho(2)) from which all possible complex features can be derived. We use these results to implement a form of "explainable neural network" called Compositional Relational Machines, or CRMs. CRMs are labelled directed-acyclic graphs. The vertex-label for any vertex j in the CRM contains a feature-function f(j) and an continuous activation function g(j). If j is a "non-input" vertex, then f(j) is the composition of features associated with vertices in the direct predecessors of j. Our focus is on CRMs in which input vertices (those without any direct predecessors) all have M-simple features in their vertex-labels. We provide a randomised procedure for constructing the structure of such CRMs, and a procedure for estimating the parameters (the w(ij)'s) using back-propagation and stochastic gradient descent. Using a notion of explanations based on the compositional structure of features in a CRM, we provide empirical evidence on synthetic data of the ability to identify appropriate explanations;and

关键词： Explainable neural networks Relational features inductive logic programming Neuro-symbolic learning

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logical settings for concept-learning

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ARTIFICIAL INTELLIGENCE 1997年第1期95卷 187-201页

作者： DeRaedt, L Department of Computer Science Katholieke Universiteit Leuven Celestijnenlaan 200A B-3001 Heverlee Belgium

Three different formalizations of concept-learning in logic (as well as some variants) are analyzed and related. It is shown that learning from interpretations reduces to learning from entailment, which in rum reduces to learning from satisfiability. The implications of this result for inductive logic programming and computational learning theory are then discussed, and guidelines for choosing a problem-setting are formulated. (C) 1997 Elsevier Science B.V.

关键词： inductive logic programming computational learning theory concept-learning logic

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Generating numerical constraints in CILP

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2005年第1期19卷 91-108页

作者： Zheng, L Liu, CN Jia, L Zhong, N Beijing Univ Technol Coll Comp Sci Beijing Municipal Key Lab Multimed & Intelligent Beijing 100022 Peoples R China Maebashi Inst Technol Dept Informat Engn Maebashi Gumma 3710816 Japan

A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple dimensions, without additional background knowledge. As a result, the constraint logic program covering all positive examples and consistent with all negative examples can be derived automatically.

关键词： inductive logic programming constraint inductive logic programming pattern recognition multivariate statistical analysis

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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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