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13th international conference on inductive logic programming

作者： Koenik, Tolga Laird, John E. Stanford Univ Ctr Study Language & Informat Computat Learning Lab Stanford CA 94305 USA Univ Michigan Dept Elect Engn & Comp Sci Artificial Intelligence Lab Ann Arbor MI 48109 USA

We describe a relational learning by observation framework that automatically creates cognitive agent programs that model expert task performance in complex dynamic domains. Our framework uses observed behavior and goal annotations of an expert as the primary input, interprets them in the context of background knowledge, and returns an agent program that behaves similar to the expert. We map the problem of creating an agent program on to multiple learning problems that can be represented in a "supervised concept learning" setting. the acquired procedural knowledge is partitioned into a hierarchy of goals and represented with first order rules. Using an inductive logic programming (ILP) learning component allows our framework to naturally combine structured behavior observations, parametric and hierarchical goal annotations, and complex background knowledge. To deal with the large domains we consider, we have developed an efficient mechanism for storing and retrieving structured behavior data. We have tested our approach using artificially created examples and behavior observation traces generated by AI agents. We evaluate the learned rules by comparing them to hand-coded rules.

关键词： relational learning by observation relational learning inductive logic programming (ILP) behavioral cloning cognitive agent architectures

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Minimised residue hypotheses in relevant logic 13

Minimised residue hypotheses in relevant logic

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13th Annual international conference on Algorithmic Learning theory

作者： Fronhöfer, B Yamamoto, A Tech Univ Dresden Dept Comp Sci Artificial Intelligence Inst D-01062 Dresden Germany Hokkaido Univ MemeMedia Lab Sapporo Hokkaido 0608628 Japan

ISBN: (纸本)3540001700

In the field of deductive logic, relevant logic has been investigated for a long time, as a means to derive only conclusions which are related to all premises. Our proposal is to apply this concept of relevance as a criterion of appropriateness to hypotheses in inductive logic, and in this paper we present some special hypotheses called residue hypotheses, which satisfy such kind of appropriateness. this concept of relevance is different from those often introduced in the field of inductive logic programming. While those aimed at the reduction of search spaces, which went hand in hand with postulating criteria which restricted the appropriateness of formulae as hypotheses, the relevance concept presented in this paper can be regarded as 'logical smallness' of hypotheses, in contrast to 'syntactical smallness'. We also give a further refinement,. so-called minimized residue hypotheses, which constitute an interesting trade-off between these two types of smallness. We also give some results on bottom clauses and relevance.

关键词： Computer circuits

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Mathematical applications of inductive logic programming

Mathematical applications of inductive logic programming

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13th international conference on inductive logic programming

作者： Colton, Simon Muggleton, Stephen Univ London Imperial Coll Sci Technol & Med Dept Comp Computat Bioinformat Lab London SW7 2AZ England

the application of inductive logic programming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. the application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called Automated theory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present

关键词： logic programming

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Learning structure and parameters of Stochastic logic Programs 12

Learning structure and parameters of Stochastic Logic Progra...

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12th international conference on inductive logic programming

作者： Muggleton, S Univ London Imperial Coll Sci Technol & Med Dept Comp London England

ISBN: (纸本)3540005676

Previous papers have studied learning of Stochastic logic Programs (SLPs) either as a purely parametric estimation problem or separated structure learning and parameter estimation into separate phases. In this paper we consider ways in which both the structure and the parameters of an SLP can be learned simultaneously. the paper assumes an ILP algorithm, such as Progol or FOIL, in which clauses are constructed independently. We derive analytical and numerical methods for efficient computation of the optimal probability parameters for a single clause choice within such a search.

关键词： Stochastic logic programs generalisation analytical methods numerical methods

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Unifying logic, topology and learning in Parametric ogic

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thEORETICAL COMPUTER SCIENCE 2006年第1期350卷 103-124页

作者： Martin, É Sharma, A Stephan, F Univ New S Wales Sch Engn & Comp Sci Sydney NSW 2052 Australia Univ New S Wales Natl ICT Australia Sydney NSW 2052 Australia Queensland Univ Technol Div Res & Commercialisat Brisbane Qld 4001 Australia Natl Univ Singapore Sch Comp Singapore 117543 Singapore Natl Univ Singapore Dept Math Singapore 117543 Singapore

Many connections have been established between learning and logic, or learning and topology, or logic and topology. Still, the connections are not at the heart of these fields. Each of them is fairly independent of the others when attention is restricted to basic notions and main results. We show that connections can actually be made at a fundamental level, and result in a logic with parameters that needs topological notions for its early developments, and notions from learning theory for interpretation and applicability. One of the key properties of first-order logic is that the classical notion of logical consequence is compact. We generalize the notion of logical consequence, and we generalize compactness to beta-weak compactness where beta is an ordinal. the effect is to stratify the set of generalized logical consequences of a theory into levels, and levels into layers. Deduction corresponds to the lower layer of the first level above the underlying theory, learning with less than beta mind changes to layer beta of the first level, and learning in the limit to the first layer of the second level. Refinements of Borel-like hierarchies provide the topological tools needed to develop the framework. (c) 2005 Elsevier B.V. All rights reserved.

关键词： inductive logic inductive inference Borel and difference hierarchies weak forms of compactness

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Implementing an instantiation-based theorem prover for first-order logic

Implementing an instantiation-based theorem prover for first...

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6th international Workshop on the Implementation of logics, IWIL 2006 - Held at the 13th international conference on logic for programming, Artificial Intelligence and Reasoning

作者： Korovin, Konstantin University of Manchester United Kingdom

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An Instance Based Model for Scalable θ-Subsumption 29

An Instance Based Model for Scalable θ-Subsumption

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29th Annual IEEE international conference on Tools with Artificial Intelligence (ICTAI)

作者： Leger, Hippolyte Bouthinon, Dominique Lebbah, Mustapha Azzag, Hanene Univ Paris 13 LIPN UMR CNRS 7030 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

ISBN: (纸本)9781538638767

the theta-subsumption test is known to be a bottleneck in inductive logic programming. the state-of-the-art learning systems in this field are hardly scalable. Last year, we have created a distributed theta-subsumption process based on an Actor Model, with the aim of being able to decide subsumption on very large clauses. this model was correct and complete, but was also very slow. this is why we introduce ANTS (Actor Network based theta-Subsumption), a new model also based on an actor network, which is significantly faster than the previous one.

关键词： theta-subsumption inductive logic programming actor model distributed computing Akka

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Safety PLC programming Based on UML Statechart 13

Safety PLC Programming Based on UML Statechart

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13th international ELEKTRO conference (ELEKTRO)

作者： Medvedik, Milan Zdansky, Juraj Univ Zilina Fac Elect Engn & Informat Technol Dept Control & Informat Syst Zilina Slovakia

ISBN: (纸本)9781728175423

the paper deals with a systematic approach to programming a program for safety PLC (Programmable logic Controllers) based on the description of the required function by the UML (Unified Modeling Language) statechart. this procedure can be used to achieve systematic safety integrity of the control system with the safety PLC. Rhapsody tool is used as software support for UML and the application example is implemented on safety PLC Simatic.

关键词： UML Rhapsody safety PLC statechart safety function

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Hypothesis finding with proof theoretical appropriateness criteria

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thEORETICAL COMPUTER SCIENCE 2006年第1期350卷 140-162页

作者： Fronhöfer, B Yamamoto, A Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan Tech Univ Dresden Dept Comp Sci D-01062 Dresden Germany

For two given formulae B and C with B does not satisfy C, hypothesis finding means to produce a formula S such that B boolean AND S satisfies C. Hypothesis finding, or variants thereof, is central to various types of inference, e.g., abductive inference, inductive inference, machine learning, and machine discovery. Clarifying the nature of hypothesis finding is still in its infancy, a situation similar to the establishment of logical foundations of inference related to induction and discovery. Although trivial solutions to hypothesis finding are easy to give, finding appropriate hypotheses still remains as a great challenge. A central role in this context plays the question, what it means for a hypothesis to be appropriate? In this paper we propose an answer to this question, which is based on proof theoretical criteria. this is in contrast to most previous approaches where appropriateness of hypotheses was based on concepts of semantical weakness in classical logic. More precisely, we use provability in Relevance logic instead of classical semantical entailment, we demand utmost exploitation of the inferential potential (deductive content) inherent in B -> C and we demand S to be a minimal deductive supplement to B -> C. Along these lines we developed the concept of a minimized residue hypothesis which also constitutes an interesting trade-off between 'logical smallness' and 'syntactical smallness'. Published by Elsevier B.V.

关键词： hypothesis finding connection method relevance logic residue hypotheses inductive logic abduction and knowledge discovery

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Nonmonotonic inductive logic programming by instance patterns 07

Nonmonotonic inductive logic programming by instance pattern...

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9th international ACM SIGPLAN conference on Principles and Practice of Declarative programming, PPDP'07

作者： Liu, Chongbing Pontelli, Enrico New Mexico State University

ISBN: (纸本)1595937692

In this paper, we present a new approach, called NM-ILP-IP, for inductive learning in the context of nonmonotonic logic frameworks. this approach is based on the notations of concept instances and instance patterns introduced in [13]. When a strictly correct Horn theory cannot be induced, this approach induces a normal logic program, by specializing a previously learned overly-general theory. the advantages of this approach over others include: (a) it does not rely on existing ILP systems, and it avoids many of the effectiveness and efficiency drawbacks of ordinary ILP systems;(b) no theorem prover is needed during the learning process;(c) it introduces negation as failure (NAF) of existing predicates and introduces new abnormality predicates only when necessary, making the final theory more compact. Copyright © 2007 ACM.

关键词： logic programming

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