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Using abduction and induction for operational requirements elaboration

JOURNAL OF APPLIED logic 2009年第3期7卷 275-288页

作者： Alrajeh, D. Ray, O. Russo, A. Uchitel, S. Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Univ Bristol Dept Comp Sci Bristol BS8 1UB Avon England Univ Buenos Aires Dept Computac RA-2160 Buenos Aires DF Argentina

Requirements Engineering involves the elicitation of high-level stakeholder goals and their refinement into operational system requirements. A key difficulty is that stakeholders typically convey their goals indirectly through intuitive narrative-style scenarios of desirable and undesirable system behaviour, whereas goal refinement methods usually require goals to be expressed declaratively using, for instance, a temporal logic. In actual software engineering practice, the extraction of formal requirements from scenario-based descriptions is a tedious and error-prone process that would benefit from automated tool support. This paper presents an inductive logic programming method for inferring operational requirements from a set of example scenarios and an initial but incomplete requirements specification. The approach is based on translating the specification and the scenarios into an event-based logic programming formalism and using a non-monotonic reasoning system, called eXtended Hybrid Abductive inductive Learning, to automatically infer a set of event pre-conditions and trigger-conditions that cover all desirable scenarios and reject all undesirable ones. This learning task is a novel application of logic programming to requirements engineering that also demonstrates the utility of non-monotonic learning capturing pre-conditions and trigger-conditions. (C) 2008 Elsevier B.V. All rights reserved.

关键词： inductive logic programming Abductive reasoning Goal-oriented requirements engineering Scenario-based specification Linear temporal logic Event Calculus

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Hypothesis finding based on upward refinement of residue hypotheses

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THEORETICAL COMPUTER SCIENCE 2003年第1期298卷 5-19页

作者： Yamamoto, A Hokkaido Univ Fac Technol & Meme Media Lab Kita Ku Sapporo Hokkaido 0608628 Japan

For given logical formulae B and E such that B K E, hypothesis finding means the generation of a formula H such that Bboolean ANDH satisfies E. Hypothesis finding constitutes a basic technique for fields of inference, like inductive inference and knowledge discovery. In order to put various hypothesis finding methods proposed previously on one general ground, we use upward refinement and residue hypotheses. We show that their combination is a complete method for solving any hypothesis finding problem in clausal logic. We extend the relative subsumption relation, and show that some hypothesis finding methods previously presented can be regarded as finding hypotheses which subsume examples relative to a given background theory. Noting that the weakening rule may make hypothesis finding difficult to solve, we propose restricting this rule either to the inverse of resolution or to that of subsumption. We also note that this work is related to relevant logic. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： abductive inference inductive inference inductive logic programming clausal logic hypothesis finding

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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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Robust Process Discovery with Artificial Negative Events

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JOURNAL OF MACHINE LEARNING RESEARCH 2009年第6期10卷 1305-1340页

作者： Goedertier, Stijn Martens, David Vanthienen, Jan Baesens, Bart Katholieke Univ Leuven Fac Business & Econ Louvain Belgium Assoc Ghent Univ Univ Coll Ghent Dept Business Adm & Publ Management Ghent Belgium Univ Southampton Sch Management Southampton SO9 5NH Hants England

Process discovery is the automated construction of structured process models from information system event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems such as expressiveness, noise, incomplete event logs, or the inclusion of prior knowledge. In this paper, we present a configurable technique that deals with these challenges by representing process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). This problem formulation allows using learning algorithms and evaluation techniques that are well-know in the machine learning community. Moreover, it allows users to have a declarative control over the inductive bias and language bias.

关键词： graph pattern discovery inductive logic programming Petri net process discovery positive data only

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A multistrategy approach to relational knowledge discovery in databases

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MACHINE LEARNING 1997年第3期27卷 287-312页

作者： Morik, K Brockhausen, P Computer Science Department LS VIII Univ. Dortmund Dortmund

When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a model-driven way by the user and in a data-driven way by structures that are induced by three simple learning algorithms.

关键词： knowledge discovery in databases inductive logic programming functional dependencies numerical intervals background knowledge

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Learning grammar rules of building parts from precise models and noisy observations

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2011年第2期66卷 166-176页

作者： Dehbi, Y. Pluemer, L. Univ Bonn Inst Geodesy & Geoinformat D-5300 Bonn Germany

The automatic interpretation of dense three-dimensional (3D) point clouds is still an open research problem. The quality and usability of the derived models depend to a large degree on the availability of highly structured models which represent semantics explicitly and provide a priori knowledge to the interpretation process. The usage of formal grammars for modelling man-made objects has gained increasing interest in the last few years. In order to cope with the variety and complexity of buildings, a large number of fairly sophisticated grammar rules are needed. As yet, such rules mostly have to be designed by human experts. This article describes a novel approach to machine learning of attribute grammar rules based on the inductive logic programming paradigm. Apart from syntactic differences, logic programs and attribute grammars are basically the same language. Attribute grammars extend context-free grammars by attributes and semantic rules and provide a much larger expressive power. Our approach to derive attribute grammars is able to deal with two kinds of input data. On the one hand, we show how attribute grammars can be derived from precise descriptions in the form of examples provided by a human user as the teacher. On the other hand, we present the acquisition of models from noisy observations such as 3D point clouds. This includes the learning of geometric and topological constraints by taking measurement errors into account. The feasibility of our approach is proven exemplarily by stairs, and a generic framework for learning other building parts is discussed. Stairs aggregate an arbitrary number of steps in a manner which is specified by topological and geometric constraints and can be modelled in a recursive way. Due to this recursion, they pose a special challenge to machine learning. In order to learn the concept of stairs, only a small number of examples were required. Our approach represents and addresses the quality of the given observations and th

关键词： inductive logic programming Attribute grammar 3D model Uncertain projective geometry Probability distributions

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Bottom-up induction of feature terms

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MACHINE LEARNING 2000年第3期41卷 259-294页

作者： Armengol, E Plaza, E CSIC Artificial Intelligence Res Inst Bellaterra 08193 Catalonia Spain

The aim of relational learning is to develop methods for the induction of hypotheses in representation formalisms that are more expressive than attribute-value representation. Most work on relational learning has been focused on induction in subsets of first order logic like Horn clauses. In this paper we introduce the representation formalism based on feature terms and we introduce the corresponding notions of subsumption and anti-unification. Then we explain INDIE, a heuristic bottom-up learning method that induces class hypotheses, in the form of feature terms, from positive and negative examples. The biases used in INDIE while searching the hypothesis space are explained while describing INDIE's algorithms. The representational bias of INDIE can be summarised in that it makes an intensive use of sorts and sort hierarchy, and in that it does not use negation but focuses on detecting path equalities. We show the results of INDIE in some classical relational datasets showing that it's able to find hypotheses at a level comparable to the original ones. The differences between INDIE's hypotheses and those of the other systems are explained by the bias in searching the hypothesis space and on the representational bias of the hypothesis language of each system.

关键词： inductive logic programming relational learning concept induction feature structures

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Resource-bounded relational reasoning: Induction and deduction through stochastic matching

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MACHINE LEARNING 2000年第1-2期38卷 41-62页

作者： Sebag, M Rouveirol, C Ecole Polytech LMS CNRS UMR 7649 F-91128 Palaiseau France Univ Paris 11 LRI CNRS UMR 8623 Orsay France

One of the obstacles to widely using first-order logic languages is the fact that relational inference is intractable in the worst case. This paper presents an any-time relational inference algorithm: it proceeds by stochastically sampling the inference search space, after this space has been judiciously restricted using strongly-typed logic-like declarations. We present a relational learner producing programs geared to stochastic inference, named STILL, to enforce the potentialities of this framework. STILL handles examples described as definite or constrained clauses, and uses sampling-based heuristics again to achieve any-time learning. Controlling both the construction and the exploitation of logic programs yields robust relational reasoning, where deductive biases are compensated for by inductive biases, and vice versa.

关键词： first order logic bounded reasoning inductive logic programming

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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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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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