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3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI)

作者： Komura, Keigo Mutoh, Atsuko Inuzuka, Nobuhiro Nagoya Inst Technol Grad Sch Engn Dept Comp Sci & Engn Nagoya Aichi Japan

ISBN: (纸本)9781479941735

Artificial society is a discipline to study mechanisms of social system and phenomena which the mechanisms make. Emergence is global phenomena occurred by local mechanisms, such as, by collective behavior of autonomous agents. Understanding of emergence phenomena is a challenging subject. In this paper we use the framework of inductive logic programming (ILP) for artificial society and emergence behavior study. ILP is a branch of machine learning based on logic programming and inductive inference. We investigate the possibility of ILP in artificial society study. ILP and logic programming technique are applied to representation of an artificial society model, called Sugarscape, and to rule learning for agent behavior. Although classical ILP algorithms target classification problems, the proposed algorithm grows behavior rule for an evaluation measurement. Phenomena which this paper treate is limited but we showed that ILP technique can be applied to study in the field of artificial society.

关键词： Artificial society Emergent behavior inductive logic programming

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Ontology Population from the Web: an inductive logic programming-Based Approach

Ontology Population from the Web: an Inductive Logic Program...

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11th International Conference on Information Technology - New Generations (ITNG)

作者： Lima, Rinaldo Oliveira, Hilario Freitas, Fred Espinasse, Bernard Univ Fed Pernambuco Informat Ctr Recife PE Brazil Aix Marseille Univ LSIS Marseille France

ISBN: (纸本)9781479931873

The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extraction and classification of instances of the concepts and relations defined by an ontology. Developing IE rules for OP is an intensive and time-consuming process. Thus, an automated mechanism, based on machine-learning techniques, able to convert textual data from web pages into ontology instances may be a crucial path. This paper presents an inductive logic programming-based method that automatic induces symbolic extraction rules, which are used for populating a domain ontology with instances of entity classes. This method uses domain-independent linguistic patterns for retrieving candidate instances from web pages, and a WordNet semantic similarity measure as background knowledge to be used as input by a generic inductive logic programming system. Experiments were conducted concerning both the instance classification problem and a comparison with other popular machine learning algorithms, with encouraging results.

关键词： Ontology Population inductive logic programming Pattern Learning Information Extraction

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Efficient Program Synthesis Using Constraint Satisfaction in inductive logic programming

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JOURNAL OF MACHINE LEARNING RESEARCH 2013年第12期14卷 3649-3682页

作者： Ahlgren, John Yuen, Shiu yin City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

We present NrSample, a framework for program synthesis in inductive logic programming. NrSample uses propositional logic constraints to exclude undesirable candidates from the search. This is achieved by representing constraints as propositional formulae and solving the associated constraint satisfaction problem. We present a variety of such constraints: pruning, input-output, functional (arithmetic), and variable splitting. NrSample is also capable of detecting search space exhaustion, leading to further speedups in clause induction and optimality. We benchmark NrSample against enumeration search (Aleph's default) and Progol's A* search in the context of program synthesis. The results show that, on large program synthesis problems, NrSample induces between 1 and 1358 times faster than enumeration (236 times faster on average), always with similar or better accuracy. Compared to Progol A*, NrSample is 18 times faster on average with similar or better accuracy except for two problems: one in which Progol A* substantially sacrificed accuracy to induce faster, and one in which Progol A* was a clear winner. Functional constraints provide a speedup of up to 53 times (21 times on average) with similar or better accuracy. We also benchmark using a few concept learning (non-program synthesis) problems. The results indicate that without strong constraints, the overhead of solving constraints is not compensated for.

关键词： inductive logic programming program synthesis theory induction constraint satisfaction Boolean satisfiability problem

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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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Object Identity Reloaded-A Comprehensive Reference for an Efficient and Effective Framework for logic-Based Machine Learning

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ELECTRONICS 2025年第8期14卷 1523-1523页

作者： Ferilli, Stefano Univ Bari Dept Comp Sci Via E Orabona 4 I-70125 Bari Italy

Sub-symbolic Machine Learning (ML) techniques, and specifically Neural Network-based ones, recently took over the research landscape, thanks to their efficiency and impressive effectiveness. On the other hand, the recent debate on ethics and AI and the first regulations on AI are progressively calling for anthropocentricity, which in turn requires explicit, human-understandable, and explainable approaches and representations that allow humans to be active parts in the loop. In these cases, logic-based approaches are more suitable. The inductive logic programming (ILP) branch of research in ML provides an anwer to this need and a uniform and unifying framework for three relevant industrial and research concerns: management of databases, implementation of software systems, and modeling of human-like reasoning strategies. A particular ILP framework based on the Object Identity (OI) assumption was proposed in the 1990s, for which desirable theoretical and pratical properties were demonstrated and working tools and systems that successfully approached real-world and classical problems in AI were developed. In an age when mainstream research and media seem to reduce AI and ML to just deep learning, this paper celebrates the 30th anniversary of OI by providing for the first time a comprehensive overview of the framework to be used as a reference for researchers still interested in investigating the ILP approach to ML.

关键词： machine learing inductive logic programming incrementality object identity

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inductive learning of robot task knowledge from raw data and online expert feedback

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MACHINE LEARNING 2025年第4期114卷 1-33页

作者： Meli, Daniele Fiorini, Paolo Univ Verona Dept Comp Sci Verona Italy Univ Verona Dept Engn Innovat Med Str Le Grazie 15 I-37135 Verona Italy

The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities, possibly based on formal methods as logics for the definition of task specifications. However, prior knowledge is often unavailable in complex realistic scenarios. In this paper, we propose an offline algorithm based on inductive logic programming from noisy examples to extract task specifications (i.e., action preconditions, constraints and effects) directly from raw data of few heterogeneous (i.e., not repetitive) robotic executions. Our algorithm leverages on the output of any unsupervised action identification algorithm from video-kinematic recordings. Combining it with the definition of very basic, almost task-agnostic, commonsense concepts about the environment, which contribute to the interpretability of our methodology, we are able to learn logical axioms encoding preconditions of actions, as well as their effects in the event calculus paradigm. Since the quality of learned specifications depends mainly on the accuracy of the action identification algorithm, we also propose an online framework for incremental refinement of task knowledge from user's feedback, guaranteeing safe execution. Results in a standard manipulation task and benchmark for user training in the safety-critical surgical robotic scenario, show the robustness, data- and time-efficiency of our methodology, with promising results towards the scalability in more complex domains.

关键词： inductive logic programming Answer set programming Learning under uncertainty Online learning Human-robot interaction Explainable AI

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

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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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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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Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach

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MACHINE LEARNING 2024年第7期113卷 4579-4614页

作者： Bueff, Andreas Belle, Vaishak Univ Edinburgh Sch Informat 11 Crichton St Edinburgh EH8 9LE Scotland

Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there's a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision boundaries. In this research, we introduce a neuro-symbolic ILP framework, grounded on differentiable Neural logic networks, tailored for non-linear rule extraction in mixed discrete-continuous spaces. Our methodology consists of a neuro-symbolic approach, emphasising the extraction of non-linear functions from mixed domain data. Our preliminary findings showcase our architecture's capability to identify non-linear functions from continuous data, offering a new perspective in neural-symbolic research and underlining the adaptability of ILP-based frameworks for regression challenges in continuous scenarios.

关键词： inductive logic programming Neuro-symbolic artificial intelligence Knowledge representation and reasoning Non-linear modelling Deep learning

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Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

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Computers, Materials & Continua 2024年第3期78卷 3619-3643页

作者： Xuemin Wang Qiaochen Li Xuguang Bao Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical *** the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of ***,an ethical decision-making framework is constructed by rule-based or statistical *** this paper,we propose an ethical decision-making framework based on incremental ILP(inductive logic programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical *** the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP *** framework consists of two processes:the learning process and the deduction *** first process records bottom clauses with their score functions and learns rules guided by the entailment and the score *** second process obtains an ethical decision based on the *** an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical ***,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set programming)focusing on conflict *** results of comparisons show that our proposed system can generate better-quality rules than most other systems.

关键词： Ethical decision-making inductive logic programming incremental learning conflicts

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