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inductive equivalence in clausal logic and nonmonotonic logic programming

MACHINE LEARNING 2011年第1期83卷 1-29页

作者： Sakama, Chiaki Inoue, Katsumi Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

This paper provides a logical framework for comparing inductive capabilities among agents having different background theories. A background theory is called inductively equivalent to another background theory if the two theories induce the same hypotheses for any observation. Conditions of inductive equivalence change depending on the logic of representation languages and the logic of induction or inductive logic programming (ILP). In this paper, we consider clausal logic and nonmonotonic logic programs as representation languages for background theories. Then we investigate conditions of inductive equivalence in four different frameworks of induction, cautious induction , brave induction , learning from satisfiability , and descriptive induction . We observe that several induction algorithms in Horn ILP systems require weaker conditions of equivalence under restricted problem settings. We address that inductive equivalence can be used for verification and evaluation of induction algorithms, and argue problems for optimizing background theories in ILP.

关键词： inductive equivalence inductive logic programming Nonmonotonic logic programs

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Minimized Spectral Resource Consumption with Rescaled Failure Probability Constraint in Flexible Bandwidth Optical Networks

Minimized Spectral Resource Consumption with Rescaled Failur...

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Conference on Optical Fiber Communication (OFC)/National Fiber Optic Engineers Conference (NFOEC)

作者： Chen, Bowen Zhang, Jie Zhao, Yongli Chen, Haining Huang, Shanguo Gu, Wanyi Jue, Jason P. Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

We propose an ILP model and an efficient rescaled failure-probability-aware algorithm (RFPA) to minimize spectral resource consumption. Simulation results show that, compared to a traditional algorithm, both ILP model... 详细信息

ISBN: (纸本)9781557529626;9781479904570

关键词： Minimized Spectral Resource Resource utilization spectral efficiency failure probability inductive logic programming Fiber optic networks Bandwidth FLEXIBLE

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AMIE: association rule mining under incomplete evidence in ontological knowledge bases 13

AMIE: association rule mining under incomplete evidence in o...

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Proceedings of the 22nd international conference on World Wide Web

作者： Luis Antonio Galárraga Christina Teflioudi Katja Hose Fabian Suchanek Max-Planck Institute for Informatics Saarbrücken Germany Aalborg University Aalborg Denmark

ISBN: (纸本)9781450320351

Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. inductive logic programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. In this paper, we develop a rule mining model that is explicitly tailored to support the OWA scenario. It is inspired by association rule mining and introduces a novel measure for confidence. Our extensive experiments show that our approach outperforms state-of-the-art approaches in terms of precision and coverage. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches.

关键词： inductive logic programming ilp rule mining

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Fuzzy ILP Classification of web reports after linguistic text mining

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INFORMATION PROCESSING & MANAGEMENT 2012年第3期48卷 438-450页

作者： Dedek, Jan Vojtas, Peter Vomlelova, Marta Charles Univ Prague Dept Software Engn Prague Czech Republic Charles Univ Prague Dept Theoret Comp Sci & Math Log Prague Czech Republic

In this paper we study the problem of classification of textual web reports. We are specifically focused on situations in which structured information extracted from the reports is used for classification. We present an experimental classification system based on usage of third party linguistic analyzers, our previous work on web information extraction, and fuzzy inductive logic programming (fuzzy ILP). A detailed study of the so-called 'Fuzzy ILP Classifier' is the main contribution of the paper. The study includes formal models, prototype implementation, extensive evaluation experiments and comparison of the classifier with other alternatives like decision trees, support vector machines, neural networks, etc. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy inductive logic programming Information extraction Natural language processing Machine learning

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Applying the information bottleneck to statistical relational learning

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MACHINE LEARNING 2012年第1期86卷 89-114页

作者： Riguzzi, Fabrizio Di Mauro, Nicola Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy Univ Bari Aldo Moro Dipartimento Informat Bari Italy

In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.

关键词： Probabilistic inductive logic programming Statistical relational learning inductive logic programming Knowledge based model construction Distribution semantics

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Automatic traffic incident detection based on nFOIL

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第7期39卷 6547-6556页

作者： Lu, Jian Chen, Shuyan Wang, Wei Ran, Bin Southeast Univ Sch Transportat Nanjing 210096 Jiangsu Peoples R China

Traffic incidents inevitably cause traffic delay and deteriorate road safety conditions. Incidents are increasing alongside the fast economic growth. Due to the rampant growth of traffic incidents, developing efficient and effective automated incident detection (AID) techniques has prompted a growing worldwide interest. In this paper, the great efforts on developing a new approach to this problem based on nFOIL, a novel inductive logic programming (ILP), are done. By way of illustration, a simulated traffic data generated from Ayer Rajah Expressway (AYE) highway in Singapore and a real traffic data collected in I-880 freeway in California are used to assess the detection performance of this approach, and performance metrics includes detection rate, false alarm rate, mean time to detection, classification rate and the area under Receiver Operating Characteristic (ROC) curve (AUC). For comparison, we conducted the experiments on neural networks and support vector machine. The experimental results showed that nFOIL is sensitive to the skewed distribution of positive and negative examples in the dataset, and we make use of two different techniques, resampling and ensemble learning, to cope with highly skewed data in the context of ILP classification problems and investigated the effect of them typicality on the performance of AID model. It is concluded that ILP based AID approach are feasible, and have a favorable performance compared to neural networks and support vector machines. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Automated incident detection inductive logic programming nFOIL system The area under ROC curve Rare-class classification Resampling Ensemble learning

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Adaptive complex event processing for harmful situation detection

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EVOLVING SYSTEMS 2012年第3期3卷 167-177页

作者： Coffi, Jean-Rene Marsala, Christophe Museux, Nicolas Thales Res & Technol 1Ave Augustin Fresnel F-91767 Palaiseau France Univ Paris 06 UPMC UMR 7606 LIP6 F-75005 Paris France

In the field of infrastructures' surveillance and protection, it is important to make decisions based on activities occurring in the environment and its local context and conditions. In this paper we use an active rule based event processing architecture in order to make sense of situations from the combination of different signals received by the rule engine. However obtaining some high level information automatically is not without risks, especially in sensitive environments, and detection mistakes can happen for various reasons: the signal's source can be defective, whether it is human-miss-interpretation of the signal-or computed-material malfunction;the aggregation rules can be wrong syntaxically, for example when a rule will never be triggered or a situation never detected;the interpretation given to the combination of signals does not correspond to the reality on the field-because the knowledge of the rule designer is subjective or because the environment evolves over-time-the rules are therefore incorrect semantically. In this paper, a new approach is proposed to avoid the third kind of error sources. We present a hybrid machine learning technique adapted to the complexity of the rules' representation, in order to create a system more conform to reality. The proposed approach uses a combination of an Association Rule Mining algorithm and inductive logic programming for rule induction. Empirical studies on simulated datasets demonstrate how our method can contribute to sensible systems such as the security of a public or semi-public place.

关键词： Complex event processing inductive logic programming Association Rule Mining Incremental Learning

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The many faces of insulin-like peptide signalling in the brain

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NATURE REVIEWS NEUROSCIENCE 2012年第4期13卷 225-239页

作者： Fernandez, Ana M. Torres-Aleman, Ignacio CSIC & Ciberned Inst Cajal Madrid 28002 Spain

Central and peripheral insulin-like peptides (ILPs), which include insulin, insulin-like growth factor 1 (IGF1) and IGF2, exert many effects in the brain. Through their actions on brain growth and differentiation, ILPs contribute to building circuitries that subserve metabolic and behavioural adaptation to internal and external cues of energy availability. In the adult brain each ILP has distinct effects, but together their actions ultimately regulate energy homeostasis - they affect nutrient sensing and regulate neuronal plasticity to modulate adaptive behaviours involved in food seeking, including high-level cognitive operations such as spatial memory. In essence, the multifaceted activity of ILPs in the brain may be viewed as a system organization involved in the control of energy allocation.

关键词： inductive logic programming Cerebrum Synaptic plasticity Power control insulin-like growth factor II Spatial Memory Peptides Insulin-Like Growth Factor I

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inductive logic programming by instance patterns

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9th International Symposium on Practical Aspects of Declarative Languages

作者： Liu, Chongbing Pontelli, Enrico New Mexico State Univ Dept Comp Sci Las Cruces NM 88003 USA

ISBN: (纸本)9783540696087

Effectiveness and efficiency are two most important properties of ILP approaches. For both top-down and bottom-up search-based approaches, greater efficiency is usually gained at the expense of effectiveness. In this paper, we propose a bottom-up approach, called ILP by instance patterns, for the problem of concept learning in ILP. This approach is based on the observation that each example has its own pieces of description in the background knowledge, and the example together with these descriptions constitute a instance of the concept subject to learn. Our approach first captures the instance structures by patterns, then constructs the final theory purely from the patterns. On the effectiveness aspect, this approach does not assume determinacy of the learned concept. On the efficiency aspect, this approach is more efficient than existing ones due to its constructive nature, the fact that after the patterns are obtained, both the background and examples are not needed anymore, and the fact that it does not perform coverage test and needs no theorem prover.

关键词： inductive logic programming concept instance patterns

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Inverse subsumption for complete explanatory induction

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MACHINE LEARNING 2012年第1期86卷 115-139页

作者： Yamamoto, Yoshitaka Inoue, Katsumi Iwanuma, Koji Univ Yamanashi Kofu Yamanashi 4008510 Japan Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

Modern explanatory inductive logic programming methods like Progol, Residue procedure, CF-induction, HAIL and Imparo use the principle of inverse entailment (IE). Those IE-based methods commonly compute a hypothesis in two steps: by first constructing an intermediate theory and next by generalizing its negation into the hypothesis with the inverse of the entailment relation. Inverse entailment ensures the completeness of generalization. On the other hand, it imposes many non-deterministic generalization operators that cause the search space to be very large. For this reason, most of those methods use the inverse relation of subsumption, instead of entailment. However, it is not clear how this logical reduction affects the completeness of generalization. In this paper, we investigate whether or not inverse subsumption can be embedded in a complete induction procedure;and if it can, how it is to be realized. Our main result is a new form of inverse subsumption that ensures the completeness of generalization. Consequently, inverse entailment can be reduced to inverse subsumption without losing the completeness for finding hypotheses in explanatory induction.

关键词： Inverse entailment Inverse subsumption Learning from entailment Explanatory induction inductive logic programming

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