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检索条件"任意字段=15th International Conference on Inductive Logic Programming, ILP 2005"

共 63 条记录，以下是1-10 订阅

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Learning logic Programs Using Neural Networks by Exploiting Symbolic Invariance 1

引用

30th international conference on inductive logic programming (ilp) held as part of the 1st international Joint conference on Learning and Reasoning (IJCLR)

作者： Phua, Yin Jun Inoue, Katsumi Grad Univ Adv Studies SOKENDAI Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

Learning from Interpretation Transition (LFIT) is an unsupervised learning algorithm which learns the dynamics just by observing state transitions. LFIT algorithms have mainly been implemented in the symbolic method, but they are not robust to noisy or missing data. Recently, research works combining logical operations with neural networks are receiving a lot of attention, with most works taking an extraction based approach where a single neural network model is trained to solve the problem, followed by extracting a logic model from the neural network model. However most research work suffer from the combinatorial explosion problem when trying to scale up to solve larger problems. In particular a lot of the invariance that hold in the symbolic world are not getting utilized in the neural network field. In this work, we present a model that exploits symbolic invariance in our problem. We show that our model is able to scale up to larger tasks than previous work.

关键词： Neural network LFIT Interpretability Neural-Symbolic AI

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Lemmaless Induction in Trace logic 15th

Lemmaless Induction in Trace Logic

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15th international conference on Intelligent Computer Mathematics (CICM) part of the Computational logic Autumn Summit (CLAS)

作者： Bhayat, Ahmed Georgiou, Pamina Eisenhofer, Clemens Kovacs, Laura Reger, Giles Univ Manchester Manchester Lancs England TU Wien Vienna Austria

ISBN: (纸本)9783031166815;9783031166808

We present a novel approach to automate the verification of first-order inductive program properties capturing the partial correctness of imperative program loops with branching, integers and arrays. We rely on trace logic, an instance of first-order logic with theories, to express first-order program semantics by quantifying over program execution timepoints. Program verification in trace logic is translated into a first-order theorem proving problem where, to date, effective reasoning has required the introduction of so-called trace lemmas to establish inductive properties. In this work, we extend trace logic with generic induction schemata over timepoints and loop counters, reducing reliance on trace lemmas. Inferring and proving loop invariants becomes an inductive inference step within superposition-based first-order theorem proving. We implemented our approach in the Rapid framework, using the first-order theorem prover Vampire. Our extensive experimental analysis shows that automating inductive verification in trace logic is an improvement compared to existing approaches.

关键词： Integer programming

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Learning of primitive formal systems defining labelled ordered tree languages via queries 27

Learning of primitive formal systems defining labelled order...

引用

Late Breaking Papers of the 27th international conference on inductive logic programming, LBP-ilp 2017

作者： Uchida, Tomoyuki Matsumoto, Satoshi Shoudai, Takayoshi Suzuki, Yusuke Miyahara, Tetsuhiro Graduate School of Information Sciences Hiroshima City University Japan Faculty of Science Tokai University Japan Faculty of Contemporary Business Kyushu International University Japan

A formal graph system (FGS) is a logic programming system that directly manipulates graphs by dealing with term graph patterns instead of terms of first-order predicate logic. In this paper, based on FGS, we introduce a primitive formal ordered tree system (pFOTS) as a formal system defining labelled ordered tree languages. A pFOTS program is a finite set of graph rewriting rules. A logic program is well-known to be suitable to represent background knowledge. the query learning model is an established mathematical model of learning via queries in computational learning theory. In this learning model, we show the ex- act learnability of a pFOTS program consisting of one graph rewriting rule and background knowledge defined with a pFOTS program using a polynomial number of queries. © by the paper's authors.

关键词： logic programming

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Generating Rules of Action Transition in Errors in Daily Activities from a Virtual Reality-Based Training Data

Generating Rules of Action Transition in Errors in Daily Act...

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19th international conference on Human-Computer Interaction (HCI international)

作者： Martono, Niken Prasasti Abe, Keisuke Yamaguchi, Takehiko Ohwada, Hayato Giovannetti, Tania Tokyo Univ Sci Dept Ind Adm Noda Chiba Japan Tokyo Univ Sci Dept Appl Elect Tokyo Japan Temple Univ Dept Psychol Philadelphia PA 19122 USA

ISBN: (纸本)9783319585246;9783319585239

Developments in virtual reality (VR) have advanced numerous applications in clinical settings in the areas of learning and treatment in neuropsychology. Emerging VR applications today focus on the challenge of diagnosis and cognitive training of mild cognitive impairment (MCI) and dementia patients and address navigation and orientation, face recognition, cognitive functionality, and other instrumental activities of daily living (IADL). the information recorded and captured by VR-based technology is real-time and can be advantageous for further analysis of patients' characteristics. the present study sought to utilize the data collected from VR-based software and a leap-motion device for learning in MCI cases to generate the rules for errors and action slips based on finger-action transitions when performing IADL. the finger motion was recorded as a time-series database, then an induction technique called inductive logic programming (ilp), which uses logical and clausal language to represent the training data, was used to discover a concise classification rule using logical programming.

关键词： Virtual reality inductive logic programming Micro slips Micro errors Mild cognitive impairment

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Distributional learning of regular formal graph system of bounded degree 1

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

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

引用

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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Processing Markov logic Networks with GPUs: Accelerating Network Grounding 1

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25th international conference on inductive logic programming (ilp)

作者： Alberto Martinez-Angeles, Carlos Dutra, Ines Costa, Vitor Santos Buenabad-Chavez, Jorge CINVESTAV IPN Dept Computac Av Inst Politecn Nacl 2508 Mexico City 07360 DF Mexico CRACS INESC TEC LA Dept Ciencia Comp Rua Campo Alegre 1021 P-4169007 Oporto Portugal Univ Porto Rua Campo Alegre 1021 P-4169007 Oporto Portugal

ISBN: (数字)9783319405667

ISBN: (纸本)9783319405667;9783319405650

Markov logic is an expressive and widely used knowledge representation formalism that combines logic and probabilities, providing a powerful framework for inference and learning tasks. Most Markov logic implementations perform inference by transforming the logic representation into a set of weighted propositional formulae that encode a Markov network, the ground Markov network. Probabilistic inference is then performed over the grounded network. Constructing, simplifying, and evaluating the network are the main steps of the inference phase. As the size of a Markov network can grow rather quickly, Markov logic Network (MLN) inference can become very expensive, motivating a rich vein of research on the optimization of MLN performance. We claim that parallelism can have a large role on this task. Namely, we demonstrate that widely available Graphics Processing Units (GPUs) can be used to improve the performance of a state-of-the-art MLN system, Tuffy, with minimal changes. Indeed, comparing the performance of our GPU-based system, TuGPU, to that of the Alchemy, Tuffy and RockIt systems on three widely used applications shows that TuGPU is up to 15x times faster than the other systems.

关键词： Statistical relational learning Markov logic Markov logic networks Datalog Parallel computing GPUs

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Extracting time-oriented relationships of nutrients to losing body fat mass using inductive logic programming

Extracting time-oriented relationships of nutrients to losin...

引用

IEEE international conference on Cognitive Informatics

作者： Sho Ushikubo Katsutoshi Kanamori Hayato Ohwada Faculty of Sci. and Tech. Tokyo University of Science Noda-shi Japan

ISBN: (纸本)9781509038473

关键词： Fats Diseases Gaussian distribution Statistical analysis Feature extraction Calcium logic programming

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15th international conference on Italian Association for Artificial Intelligence, AIIA 2016

15th International Conference on Italian Association for Art...

引用

15th international conference on Italian Association for Artificial Intelligence, AIIA 2016

ISBN: (纸本)9783319491295

the proceedings contain 39 papers. the special focus in this conference is on Optimization, Evolutionary Algorithms, Classification, Pattern Recognition, Computer Vision, Multi-agent Systems, Knowledge Representation, Reasoning and Formal Verification. the topics include: On-line dynamic station redeployments in bike-sharing systems;efficient search of relevant structures in complex systems;flat and hierarchical classifiers for detecting emotion in tweets;spam filtering using regularized neural networks with rectified linear units;user mood tracking for opinion analysis on twitter;educational concept maps for personalized learning path generation;answer set enumeration via assumption literals;on the application of answer set programming to the conference paper assignment problem;abducing compliance of incomplete event logs;a self-adaptive context-aware group recommender system;a comparative study of inductive and transductive learning with feedforward neural networks;structural knowledge extraction from mobility data;predicting process behavior in woman;on-line learning on temporal manifolds;learning and reasoning with logic tensor networks;probabilistic logical inference on the web;reasoning about multiple aspects in rational closure for DLs;reasoning about surprising scenarios in description logics of typicality;analysis of the impact of machine translation evaluation metrics for semantic textual similarity;spoken language understanding for service robotics in Italian;distributed and randomized dispatching and scheduling;steps in assessing a timeline-based planner;learning for verification in embedded systems and learning in physical domains.

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Experience-based Learning of Symbolic Numerical Constraints

Experience-based Learning of Symbolic Numerical Constraints

引用

IEEE-RAS international conference on Humanoid Robots

作者： Gokhan Solak Abdullah Cihan Ak Sanem Sariel Artificial Intelligence and Robotics Laboratory Computer Engineering Department Istanbul Technical University

ISBN: (纸本)9781509047192

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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