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Learning an approximation to inductive logic programming clause evaluation

14th international conference on inductive logic programming (ilp 2004)

作者： DiMaio, F Shavlik, J Univ Wisconsin Comp Sci Dept Madison WI 53706 USA

ISBN: (纸本)3540229418

One challenge faced by many inductive logic programming (ilp) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as stochastic clause selection (SCS) and rapid random restarts (RRR) have proven somewhat successful at addressing this weakness. However, on datasets where hypothesis evaluation is computationally expensive, even these algorithms may take unreasonably long to discover a good solution. We attempt to improve the performance of these algorithms on datasets by learning an approximation to ilp hypothesis evaluation. We generate a small set of hypotheses, uniformly sampled from the space of candidate hypotheses, and evaluate this set on actual data. these hypotheses and their corresponding evaluation scores serve as training data for learning an approximate hypothesis evaluator. We outline three techniques that make use of the trained evaluation-function approximator in order to reduce the computation required during an ilp hypothesis search. We test our approximate clause evaluation algorithm using the popular ilp system Aleph. Empirical results are provided on several benchmark datasets. We show that the clause evaluation function can be accurately approximated.

关键词： logic programming

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Systems biology: A new challenge for ilp

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14th international conference on inductive logic programming (ilp 2004)

作者： Oliver, S Univ Manchester Sch Biol Sci Manchester M13 9PL Lancs England

ISBN: (纸本)3540229418

the generation and testing of hypotheses is widely considered to be the primary method by which Science progresses. So much so, that it is still common to find a scientific proposal or an intellectual argument damned on the grounds that it has no hypothesis being tested, it is merely a fishing expedition, and so on. Extreme versions run if there is no hypothesis, it is not Science, the clear implication being that hypothesis-driven programmes (as opposed to data-driven studies) are the only contributor to the scientific endeavour. this misrepresents how knowledge and understanding are actually generated from the study of natural phenomena and laboratory experiments. Hypothesis-driven and inductive modes of reasoning are not competitive, but complementary, and both are required in post-genomic biology.

关键词： logic programming

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Bottom-up ilp using large refinement steps

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14th international conference on inductive logic programming (ilp 2004)

作者： Arias, M Khardon, R Tufts Univ Dept Comp Sci Medford MA 02155 USA

ISBN: (纸本)3540229418

the LOGAN-H system is a bottom up ilp system for learning multi-clause and multi-predicate function free Horn expressions in the framework of learning from interpretations. the paper introduces a new implementation of the same base algorithm which gives several orders of magnitude speedup as well as extending the capabilities of the system. New tools include several fast engines for subsumption tests, handling real valued features, and pruning. We also discuss using data from the standard ilp setting in our framework, which in some cases allows for further speedup. the efficacy of the system is demonstrated on several ilp datasets.

关键词： Artificial intelligence

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Inducing multi-level association rules from multiple relations

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MACHINE LEARNING 2004年第2期55卷 175-210页

作者： Lisi, FA Malerba, D Univ Bari Dipartimento Informat I-70125 Bari Italy

Recently there has been growing interest both to extend ilp to description logics and to apply it to knowledge discovery in databases. In this paper we present a novel approach to association rule mining which deals with multiple levels of description granularity. It relies on the hybrid language AL-log which allows a unified treatment of both the relational and structural features of data. A generality order and a downward refinement operator for AL-log pattern spaces is defined on the basis of query subsumption. this framework has been implemented in SPADA, an ilp system for mining multi-level association rules from spatial data. As an illustrative example, we report experimental results obtained by running the new version of SPADA on geo-referenced census data of Manchester Stockport.

关键词： inductive logic programming description logics spatial data mining

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A simulated annealing framework for ilp

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14th international conference on inductive logic programming (ilp 2004)

作者： Serrurier, M Prade, H Richard, G Univ Toulouse 3 IRIT F-31062 Toulouse France

ISBN: (纸本)3540229418

In inductive logic programming (ilp), algorithms which are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them axe greedy ones, these algorithms find clauses in local optima, according to the "quality" measure used for evaluating the results. Moreover, when learning clauses one by one, induced clauses become less interesting to cover few remaining examples. In this paper, we propose a simulated annealing framework to overcome these problems. Using a refinement operator, we define neighborhood relations on clauses and on hypotheses (i.e. sets of clauses). With these relations and appropriate quality measures, we show how to induce clauses (in a coverage approach), or to induce hypotheses directly by using simulated annealing algorithms. We discuss the necessary conditions on the refinement operators and the evaluation measures in order to increase the algorithm's effectivity. Implementations are described and experimentation results are presented.

关键词： Simulated annealing

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Learning logic programs with annotated disjunctions

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14th international conference on inductive logic programming (ilp 2004)

作者： Riguzzi, F Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

ISBN: (纸本)3540229418

logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for integrating probabilistic reasoning and logic programming. In this paper we propose an algorithm for learning LPADs. the learning problem we consider consists in starting from a sets of interpretations annotated with their probability and finding one (or more) LPAD that assign to each interpretation the associated probability. the learning algorithm first finds all the disjunctive clauses that are true in all interpretations, then it assigns to each disjunct in the head a probability and finally decides how to combine the clauses to form an LPAD by solving a constraint satisfaction problem. We show that the learning algorithm is correct and complete.

关键词： Learning algorithms

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inductive logic programming Based Bottlenose Delphin Optimization to Fake New Detection 14

Inductive Logic Programming Based Bottlenose Delphin Optimiz...

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14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)

作者： thangarasu, Gunasekar Alla, Kesava Rao Kannan, K. Nattar IMU Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Digital Hlth & Hlth Informat Kuala Lumpur Malaysia MAHSA Univ Saujana Putra Selangor Malaysia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350348798;9798350348804

the integration of inductive logic programming (ilp) and Bottlenose Dolphin Optimization (BDO) in this research addresses a pressing issue in today's information-saturated landscape: the proliferation of fake news. In an era where misleading information can spread rapidly, traditional methods often fall short in effectively identifying deceptive *** combat this challenge, our approach harnesses the synergies of ilp and BDO. ilp plays a crucial role in constructing logical rules that capture intricate relationships within news data. By doing so, it delves deep into the content, seeking out patterns and inconsistencies that may not be obvious at first glance. BDO, on the other hand, takes inspiration from the social behavior of bottlenose dolphins to optimize the process of generating these rules. Just as dolphins collaborate and communicate to solve complex problems, BDO helps refine the logical rules for better accuracy. Ultimately, this research underscores the potential of bio-inspired optimization, such as BDO, combined with the precision of logic programming (ilp) to strengthen the integrity of information dissemination platforms. In an age where the veracity of information is paramount, this innovative approach offers a promising solution to combat the spread of fake news and promote the dissemination of authentic, reliable information.

关键词： inductive logic programming Bottlenose Dolphin Optimization Fake News Detection Information Proliferation

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SkILL - a Stochastic inductive logic Learner 14

SkILL - a Stochastic Inductive Logic Learner

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IEEE 14th international conference on Machine Learning and Applications ICMLA

作者： Corte-Real, Joana Mantadelis, theofrastos Dutra, Ines Rocha, Ricardo Burnside, Elizabeth Univ Porto CRACS Rua Campo Alegre P-4169007 Oporto Portugal Univ Porto INESC TEC P-4169007 Oporto Portugal Univ Wisconsin Dept Radiol Madison WI 53706 USA

ISBN: (纸本)9781509002870

Probabilistic inductive logic programming (Pilp) is a relatively unexplored area of Statistical Relational Learning which extends classic inductive logic programming (ilp). Within this scope, we introduce SkILL, a Stochastic inductive logic Learner, which takes probabilistic annotated data and produces First Order logic (FOL) theories. Data in several domains such as medicine and bioinformatics have an inherent degree of uncertainty, and because SkILL can handle this type of data, the models produced for these areas are closer to reality. SkILL can then use probabilistic data to extract non-trivial knowledge from databases, and also address efficiency issues by introducing an efficient search strategy for finding hypotheses in Pilp environments. SkILL's capabilities are demonstrated using a real world medical dataset in the breast cancer domain.

关键词： Probabilistic inductive logic programming Statistical relational learning

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Modelling inhibition in metabolic pathways through abduction and induction

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14th international conference on inductive logic programming (ilp 2004)

作者： Tamaddoni-Nezhad, A Kakas, A Muggleton, S Pazos, F Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2BZ England Univ Cyprus Dept Comp Sci Nicosia Cyprus Univ London Imperial Coll Sci Technol & Med Dept Biol Sci London SW7 2BZ England

ISBN: (纸本)3540229418

In this paper, we study how a logical form of scientific modelling that integrates together abduction and induction can be used to understand the functional class of unknown enzymes or inhibitors. We show how we can model, within Abductive logic programming (ALP), inhibition in metabolic pathways and use abduction to generate facts about inhibition of enzymes by a particular toxin (e.g. Hydrazine) given the underlying metabolic pathway and observations about the concentration of metabolites. these ground facts, together with biochemical background information, can then be generalised by ilp to generate rules about the inhibition by Hydrazine thus enriching further our model. In particular, using Progol 5.0 where the processes of abduction and inductive generalization are integrated enables us to learn such general rules. Experimental results on modelling in this way the effect of Hydrazine in a real metabolic pathway are presented.

关键词： logic programming

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Circumscription policies for induction

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14th international conference on inductive logic programming (ilp 2004)

作者： Inoue, K Saito, H Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan NEC Corp Ltd Internet Syst Res Labs Nara 6300101 Japan

ISBN: (纸本)3540229418

there are two types of formalization for induction in logic. In descriptive induction, induced hypotheses describe rules with respect to observations with all predicates minimized. In explanatory induction, on the other hand, hypotheses abductively account for observations without any minimization principle. Both inductive methods have strength and weakness, which are complementary to each other. In this work, we unify these two logical approaches. In the proposed framework, not all predicates are minimized but minimality conditions can be flexibly determined as a circumscription policy. Constructing appropriate policies, we can intentionally minimize models of an augmented axiom set. As a result, induced hypotheses can have both conservativeness and explainability, which have been considered incompatible with each other in the literature. We also give two procedures to compute inductive hypotheses in the proposed framework.

关键词： logic programming

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