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Distributional learning of regular formal graph system of bounded degree 1

26th international conference on inductive logic programming, ilp 2016

作者： Shoudai, Takayoshi Matsumoto, Satoshi Suzuki, Yusuke Faculty of International Studies Kyushu International University Kitakyushu Japan Faculty of Science Tokai University Hiratsuka Japan Graduate School of Information Sciences Hiroshima City University Hiroshima Japan

ISBN: (数字)9783319633428

ISBN: (纸本)9783319633411

In this paper, we describe how distributional learning techniques can be applied to formal graph system (FGS) languages. An FGS is a logic program that deals with term graphs instead of the terms of first-order predicate logic. We show that the regular FGS languages of bounded degree with the 1-finite context property (1-FCP) and bounded treewidth property can be learned from positive data and membership queries. © Springer international Publishing AG 2017.

关键词： Graph theory

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Learning disjunctive logic programs from interpretation transition 26

Learning disjunctive logic programs from interpretation tran...

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26th international conference on inductive logic programming (Short Papers), ilp 2016

作者： Huang, Yi Wang, Yisong Zhang, Ying Zhang, Mingyi Guizhou University Guiyang550025 China Guizhou Academy of Sciences Guiyang550001 China Chongqing University of Arts and Sciences Chongqing402160 China

We present a new framework for learning disjunctive logic programs from interpretation transitions, called LFDT. It is a nontrivial extension to Inoue, Ribeiro and Sakama's LF1T learning framework, which learns normal logic programs from interpretation transitions. Two resolutions for disjunctive rules are also presented and used in LFDT to simplify learned disjunctive rules.

关键词： Computer circuits

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26th international Symposium on logic-Based Program Synthesis and Transformation, LOPSTR 2016

26th International Symposium on Logic-Based Program Synthesi...

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26th international Symposium on logic-Based Program Synthesis and Transformation, LOPSTR 2016

ISBN: (纸本)9783319631387

the proceedings contain 20 papers. the special focus in this conference is on logic-Based Program Synthesis and Transformation. the topics include: Partial evaluation of order-sorted equational programs modulo axioms;a formal, resource consumption-preserving translation of actors to Haskell;verification of time-aware business processes using constrained horn clauses;minizinc with strings;slicing concurrent constraint programs;a new functional-logic compiler for curry;symbolic execution and thresholding for efficiently tuning fuzzy logic programs;hierarchical shape abstraction for analysis of free list memory allocators;a productivity checker for logic programming;symbolic abstract contract synthesis in a rewriting framework;on the completeness of selective unification in concolic testing of logic programs;deriving efficient generators for closed simply-typed lambda terms and normal forms;a reversible semantics for erlang;scaling bounded model checking by transforming programs with arrays;intuitionistic logic programming for SQL;coinductive soundness of corecursive type class resolution;nominal unification of higher order expressions with recursive let and automata theory approach to predicate intuitionistic logic.

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First-Order Modular logic Programs and their Conservative Extensions (Extended Abstract) 26

First-Order Modular Logic Programs and their Conservative Ex...

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26th international Joint conference on Artificial Intelligence (IJCAI)

作者： Harrison, Amelia Lierler, Yuliya Univ Texas Austin Austin TX 78712 USA Univ Nebraska Omaha NE 68182 USA

ISBN: (纸本)9780999241103

this paper introduces first-order modular logic programs, which provide a way of viewing answer set programs as consisting of many independent, meaningful modules. We also present conservative extensions of such programs. this concept helps to identify strong relationships between modular programs as well as between traditional programs. For example, we illustrate how the notion of a conservative extension can be used to justify the common projection rewriting. this is a short version of a paper was presented at the 32nd international conference on logic programming [Harrison and Lierler, 2016].

关键词： logic programming

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ilp-assisted de novo drug design

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MACHINE LEARNING 2016年第3期103卷 309-341页

作者： Kaalia, Rama Srinivasan, Ashwin Kumar, Amit Ghosh, Indira Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi 110067 India BITS Dept Comp Sci & Informat Syst Pilani Goa India

De novo design of drugs uses the three-dimensional structure of a target protein (often called the receptor) to design molecules (or ligands) that could bind to the receptor and hence inhibit its functioning. thus, unlike a ligand-based approach, this form of drug design does not require prior knowledge of inhibitors. In this paper, the three-dimensional structure of a receptor is used indirectly, in the form of molecular interaction fields of the receptor and small molecules (or probes). In addition, we also use domain-specific constraints encoding basic geometric and pharmacological requirements imposed by the target. Interaction energies of one or more targets with a set of probes are used to identify three-dimensional constraints that occur in many-preferably all-targets. In a graph-theoretic sense, the constraints are (small, fixed-size) cliques in graphs with labelled vertices representing probe-specific points of high interaction energy, and edges between a pair of vertices are labelled by the three-dimensional distance between the corresponding points of interaction. Our interest is in the discovery of frequent cliques that satisfy domain-specific constraints. In the paper, the discovery of such patterns is done using an inductive logic programming (ilp) engine. the case for the use of ilp stems primarily from the explicit ways of incorporating domain-constraints, but any other technique capable of discovering frequent cliques from data can be used with some additional effort. the frequent cliques discovered are used to hypothesize pharmacophore-like structures on potential ligands. We test the utility of this approach by conducting a case study on the discovery of anti-malarials. Specifically, we test the approach on proteins belonging to the class of aspartic proteases. We are particularly interested in plasmepsin II, which is an enzyme in the haemoglobin degradation pathway of Plasmodium falciparum. We assess the pharmacophore-like constraints using: (a)

关键词： De novo drug design inductive logic programming Pharmacophores Frequent cliques

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Probabilistic logic programming for hybrid relational domains

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MACHINE LEARNING 2016年第3期103卷 407-449页

作者： Nitti, Davide De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Katholieke Univ Leuven Fac Engn Sci Leuven Belgium

We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

关键词： Probabilistic programming Statistical relational learning Discrete and continuous distributions Particle filter Likelihood weighting logic programming

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Extracting Time-oriented Relationships of Nutrients to Losing Body Fat Mass Using inductive logic programming 15

Extracting Time-oriented Relationships of Nutrients to Losin...

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15th IEEE international conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Ushikubo, Sho Kanamori, Katsutoshi Ohwada, Hayato Tokyo Univ Sci Fac Sci & Tech Noda Chiba Japan

ISBN: (纸本)9781509038466

this study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat mass is very meaningful. We obtained lifestyle time-series data on five male subjects who are in their 20s and not obese. the data includes the amount of body fat mass of each subject and a variety of features such as sleep, exercise, and nutrient intake. We used inductive logic programming (ilp) to apply this data because ilp can more flexibly learn rules than other machine-learning methods. As a result of applying the data to ilp, our ilp system successfully extracted rules of time-oriented relationships of nutrients to decrease body fat mass based on limited data. Intake of various nutrients one day and two days prior was effective in reducing body fat mass. Moreover, we determined that nutrients related to losing body fat mass include vitamin B2, pantothenic acid, fat, vitamin B1, and biotin. © 2016 IEEE.

关键词： Body fat mass inductive logic programming Nutrients Time-series data

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Experience-based Learning of Symbolic Numerical Constraints 16

Experience-based Learning of Symbolic Numerical Constraints

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16th IEEE-RAS international conference on Humanoid Robots (Humanoids)

作者： Solak, Gokhan Ak, Abdullah Cihan Sariel, Sanem Istanbul Tech Univ Artificial Intelligence & Robot Lab Comp Engn Dept Istanbul Turkey

ISBN: (纸本)9781509047185

Learning symbolic-level numerical constraints is key to use abstractions in effective reasoning and transfer of knowledge for robot systems. We investigate this problem in an experience-based learning framework which uses inductive logic programming as the learning method. Our particular focus is on learning numerical constraints which is an open issue for ilp systems. Some approaches overcome this by using background knowledge given by domain experts. However, using expert knowledge is both expensive and domain dependent. To obtain more general solutions, numerical constraints should be induced by the robot system itself. For this purpose, we present a constraint induction method based on lazy evaluation, designed for deriving general numerical constraints from observations. We extend Aleph, an existing ilp system based on inverse entailment, with a constraint induction approach using a constraint solver. We analyze our method on some sample scenarios and demonstrate the cases where our method can induce the target concept while the prior lazy evaluation method cannot. Our results indicate that our method can generalize numerical constraints by the self observations of robots.

关键词： experience-based learning inductive logic programming numerical reasoning in robots

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GRAPH-BASED CONCEPT DISCOVERY IN MULTI RELATIONAL DATA 6

GRAPH-BASED CONCEPT DISCOVERY IN MULTI RELATIONAL DATA

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6th international conference on Cloud System and Big Data Engineering (Confluence)

作者： Kavurucu, Yusuf Mutlu, Alev Ensari, Tolga Turkish Naval Acad Comp Engn Istanbul Turkey Kocaeli Univ Comp Engn Kocaeli Turkey Istanbul Univ Comp Engn Istanbul Turkey

ISBN: (纸本)9781467382038

Developments in technology, especially in computer science created the need of storing data in variety of areas. this need created the term database where the data is stored in a useful form. In the database, data is logically integrated in file/files according to relations among them. One of the important issues is to extract knowledge from these databases that hold data in a useful and complete form. this process is called as data mining. the main objective of data mining is to extract implicit and useful knowledge from huge and at first glance meaningless mass of data that is stored in database(s). Multi-Relational databases are the ones in which the data is stored in multiple tables (relations). the relationships between those tables are also stored as tables (relations) in the database. the more effective and commonly known approaches for Multi-Relational Data Mining (MRDM) are based on inductive logic programming (ilp). ilp contains concepts from inductive Learning and logic programming. From this point, the main purpose of MRDM is extracting implicit and trivial knowledge from relational database(s) using ilp approaches and techniques. In this approach, data is represented in graph structures and graph mining techniques are used for knowledge discovery. Concept discovery in multi-relational data mining aims to find relational rules that best describe a relation, called target relation, in terms of other relations in the database, called background knowledge. In this study, a graph-based concept discovery method for concept discovery is presented. the proposed method, namely G-CDS (Graph-based Concept Discovery System), utilizes methods both from substructure-based and path-finding based approaches, hence it can be considered as a hybrid method. G-CDS generates disconnected graph structures for each target relation and its related background knowledge, which are initially stored in a relational database, and utilizes them to guide generation of a summary graph

关键词： Multi relational data mining inductive logic programming (ilp) concept discovery graph

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Constructing Markov logic Networks from First-Order Default Rules 1

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25th international conference on inductive logic programming (ilp)

作者： Kuzelka, Ondrej Davis, Jesse Schockaert, Steven Cardiff Univ Sch Comp Sci & Informat Cardiff S Glam Wales Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (数字)9783319405667

ISBN: (纸本)9783319405667;9783319405650

Expert knowledge can often be represented using default rules of the form "if A then typically B". In a probabilistic framework, such default rules can be seen as constraints on what should be derivable by MAP-inference. We exploit this idea for constructing a Markov logic network M from a set of first-order default rules D, such that MAP inference from M exactly corresponds to default reasoning from D, where we view first-order default rules as templates for the construction of propositional default rules. In particular, to construct appropriate Markov logic networks, we lift three standard methods for default reasoning. the resulting Markov logic networks could then be refined based on available training data. Our method thus offers a convenient way of using expert knowledge for constraining or guiding the process of learning Markov logic networks.

关键词： Markov processes

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