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8th ACM SIGPLAN International conference on Certified Programs and Proofs (CPP)

作者： Felty, Amy P. Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781450362221

We present a linear logical framework implemented within the Hybrid system [Felty and Momigliano 2012]. Hybrid is designed to support the use of higher-order abstract syntax for representing and reasoning about formal systems, implemented in the Coq Proof Assistant. In this work, we extend the system with a linear specification logic, which provides infrastructure for reasoning directly about object languages with linear features. We developed this framework in order to address the challenges of reasoning about the type system of a quantum lambda calculus. In particular, we started by considering the Proto-Quipper language [Ross 2015], which contains the core of Quipper [Green et al. 2013;Selinger and Valiron 2006]. Quipper is a new quantum programming language under active development with a linear type system. We have completed a formal proof of type soundness for Proto-Quipper [Mahmoud and Felty 2018b]. Our current work includes extending this work to other properties of Proto-Quipper as well as reasoning about other quantum programming languages [Mahmoud and Felty 2018a]. It also includes reasoning about other object languages with linear features in areas such as meta-theory of low-level abstract machine code, proof theory of focused linear sequent calculi, and modeling biological processes as transition systems and proving properties about them [Despeyroux et al. 2018].

关键词： logical frameworks proof assistants linear logic quantum lambda calculus

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Football Match Result Prediction Using Neural Networks and Deep Learning

Football Match Result Prediction Using Neural Networks and D...

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International conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ekansh Tiwari Prasanjit Sardar Sarika Jain Department of Computer Applications National Institute of Technology Kurukshetra India

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170176

In the present world, the prediction of the results of football matches is being done by both football experts and machines. Football as a game produces a huge amount of statistical data about the players of the team, the matches played between the teams, the environment in which the match is being played. this statistical data can be exploited using various machine learning techniques to predict various information related to a particular football match namely the result of a particular game, injury of a player, performance of a player in a particular match, spotting new talents in the game etc. We in this project will attempt to design a prediction system powered by machine Recurrent Neural Networks and LSTMs.

关键词： Games logic gates Genetic programming Machine learning Recurrent neural networks

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Awareness of School Learning Environments 8th

Awareness of School Learning Environments

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8th International conference in Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL)

作者： Figueiredo, Margarida Vicente, Henrique Ribeiro, Jorge Neves, Jose Univ Evora Ctr Invest Educ & Psicol Escola Ciencias & Tecnol Dept Quim Evora Portugal Univ Evora Escola Ciencias & Tecnol Dept Quim Ctr Quim Evora Evora Portugal Univ Minho Ctr Algoritmi Braga Portugal Inst Politecn Viana Do Castelo ARC4DigiT Appl Res Ctr Digital Transformat Escola Super Tecnol & Gestao Viana Do Castelo Portugal

ISBN: (纸本)9783319988726;9783319988719

Now, and in the times that follow, student education should focus on developing inclusive skills such as problem-solving and decision-making, where the role of the learning environment plays a crucial part, i.e., it is a process where the screen of the universe of discourse is accomplished in order to consider not only the complex relationships that flow among the objects that populate it, but also its inner structure, co-existing incomplete/unknown or even self-contradictory information or knowledge. As a result, we will focus on the development of an Intelligent Social Machine to assess Learning Environments in high schools, based on factors like School and Disciplinary Climates as well as Parental Involvement. the formal background will be to use logic programming to define its architecture based on a Deep Learning-Big Data approach to Knowledge Representation and Reasoning, complemented by an Evolutionary approach to Computing grounded on Virtual Intellects.

关键词： Artificial Intelligence Intelligent Learning Environments logic programming Knowledge Representation and Reasoning Evolutionary Computation Intelligent Social Machine

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Multivariate Prediction of Correct Lane for Autonomous Electric Vehicle Using Deep Learning Models

Multivariate Prediction of Correct Lane for Autonomous Elect...

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International conference on Control, Mechatronics and Automation (ICCMA)

作者： Islam G. Abou Setta Omar M. Shehata Mohammed A. Awad Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781728192109

ISBN: (纸本)9781728192116

the target of a self-driving car researches is to build a better autonomous driver. We control the car to be able to drive itself without falling off the track using appropriate accelerating and braking. Using udacity as an open source simulator to depict a real-time environment. With the help of a model trained by deep neural networks, we mimic the driving behavior of a human on the simulator (Behavioral Cloning) [1]. We use Keras and a high-level API that uses TensorFlow as the backend for dataflow programming. Keras provides sequential models for building a linear stack of network layers. CNN models (Neural network layers were optimized in series combinations of Time-Distributed Convolution Layers, Maxpooling, Flatten, Dense, Dropout and so on) are experimented for reaching the best model of control. Finally, we concluded that model 2 performed the best at the end of 60 epochs with the least loss = 0.007.

关键词： Computer architecture Computational modeling Training logic gates Convolution Automobiles Recurrent neural networks

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programming and verifying a declarative first-order prover in Isabelle/HOL

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AI COMMUNICATIONS 2018年第3期31卷 281-299页

作者： Jensen, Alexander Birch Larsen, John Bruntse Schlichtkrull, Anders Villadsen, Jorgen Tech Univ Denmark DTU Compute DK-2800 Lyngby Denmark

We certify in the proof assistant Isabelle/HOL the soundness of a declarative first-order prover with equality. the LCF-style prover is a translation we have made, to Standard ML, of a prover in John Harrison's Handbook of Practical logic and Automated Reasoning. We certify it by replacing its kernel with a certified version that we program, certify and generate code from;all in Isabelle/HOL. In a declarative proof each step of the proof is declared, similar to the sentences in a thorough paper proof. the prover allows proofs to mix the declarative style with automatic theorem proving by using a tableau prover. Our motivation is teaching how automated and declarative provers work and how they are used. the prover allows studying concrete code and a formal verification of correctness. We show examples of proofs and how they are made in the prover. the entire development runs in Isabelle's ML environment as an interactive application or can be used standalone in OCaml or Standard ML (or in other functional programming languages like Haskell and Scala with some additional work).

关键词： Isabelle verification declarative proofs for first-order logic with equality soundness LCF-style prover

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Estimation of Energy Production by Using Probabilistic programming Methods in Solar Power Plants: the case of Gazi Technopark

Estimation of Energy Production by Using Probabilistic Progr...

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International conference on Renewable Energy Research and Applications (ICRERA)

作者： Mehmet Demirtas Nuran Akkoyun Emrah Akkoyun İpek Cetinbas Gazi University Ankara Turkey Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Middle East Technical University Ankara Turkey Eskişehir Osmangazi University Eskişehir Turkey

In this study, energy production estimation was carried out by using probabilistic programming methods for Gazi Technopark Solar Power Plant (SPP). the measurements obtained from meteorological stations and energy analyzers established within the SPP were created by the real-time recorded data for a year. After analyzing the effect of the measurements on production and selecting the statistically significant ones, energy production was estimated. Firstly, maximum a posteriori (MAP) method was used and the energy production was estimated with 86% successful. Secondly, using a Bayesian approach, which fundamentally enhances subjective belief by the probability of the event, via incorporation of meteorological measurements, was updated the prediction of energy production. In this way, the success of estimation associated with a confidence interval was demonstrated using a probabilistic programming. the probabilistic estimation model, which was specified for the measurements obtained from Gazi Technopark SPP, can be applied for the estimation of energy production in other power plants as well and useful for planning of energy production.

关键词： Production Estimation Energy measurement Temperature measurement Solar radiation Probabilistic logic Power generation

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Splitting an LP^MLN Program 32

Splitting an LP<SUP>MLN</SUP> Program

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32nd AAAI conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Wang, Bin Zhang, Zhizheng Xu, Hongxiang Shen, Jun Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

ISBN: (纸本)9781577358008

the technique called splitting sets has been proven useful in simplifying the investigation of Answer Set programming (ASP). In this paper, we investigate the splitting set theorem for LPMLN that is a new extension of ASP created by combining the ideas of ASP and Markov logic Networks (MLN). Firstly, we extend the notion of splitting sets to LPMLN programs and present the splitting set theorem for LPMLN. then, the use of the theorem for simplifying several LPMLN inference tasks is illustrated. After that, we give two parallel approaches for solving LPMLN programs via using the theorem. the preliminary experimental results show that these approaches are alternative ways to promote an LPMLN solver.

关键词： logic programming

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Table Transformation Rule Learner 8

Table Transformation Rule Learner

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8th International conference on Information Science and Technology (ICIST)

作者： Su, Yongchi Li, Chunping Song, Shaoxu Takao, Kenji Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Tsinghua Univ Sch Software Beijing Peoples R China Mitsubishi Heavy Ind Co Ltd ICT Solut Headquarters Hiroshima Japan

ISBN: (纸本)9781538637821

As we know, table data is a popular data form in industry and scientific research fields. However, sometimes the original table data could not meet updating requirements in real applications, so we need to convert them into required form. In this paper we propose an approach to learn the transformation rules that convert original table data to target form. Based on Inductive logic programming(ILP), we design a learning system called Table Transformation Rule Learner (TTRL). It uses specific predicates and background knowledge for this task to generate table transformation rules. We implement a unique heuristic function (HF) in TTRL to accelerate searching process for rule generation, and we use semi-supervised learning (SSL) in order to obtain more information especially from small set of sample data. We also address the problem like over-generalization which may occur when having only positive training examples in ILP learning process. We test our program in several kinds of table data, and the result shows that the transformation rules can be learned correctly. Moreover, our designed searching strategy can greatly reduce the time cost of searching rules.

关键词： inductive logic programming table transformation heuristic function semi-supervised learning

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Forgetting and Unfolding for Existential Rules 32

Forgetting and Unfolding for Existential Rules

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32nd AAAI conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Wang, Zhe Wang, Kewen Zhang, Xiaowang Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781577358008

Existential rules, a family of expressive ontology languages, inherit desired expressive and reasoning properties from both description logics and logic programming. On the other hand, forgetting is a well studied operation for ontology reuse, obfuscation and analysis. Yet it is challenging to establish a theory of forgetting for existential rules. In this paper, we lay the foundation for a theory of forgetting for existential rules by developing a novel notion of unfolding. In particular, we introduce a definition of forgetting for existential rules in terms of query answering and provide a characterisation of forgetting by the unfolding. A result of forgetting may not be expressible in existential rules, and we then capture the expressibility of forgetting by a variant of boundedness. While the expressibility is undecidable in general, we identify a decidable fragment. Finally, we provide an algorithm for forgetting in this fragment.

关键词： logic programming

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Characterization of the Convex Lukasiewicz Fragment for Learning from Constraints 32

Characterization of the Convex Lukasiewicz Fragment for Lear...

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32nd AAAI conference on Artificial Intelligence / 30th Innovative Applications of Artificial Intelligence conference / 8th AAAI Symposium on Educational Advances in Artificial Intelligence

作者： Giannini, Francesco Diligenti, Michelangelo Gori, Marco Maggini, Marco Univ Siena Dept Informat Engn & Math Sci Via Roma 56 Siena Italy

ISBN: (纸本)9781577358008

this paper provides a theoretical insight for the integration of logical constraints into a learning process. In particular it is proved that a fragment of the Lukasiewicz logic yields a set of convex constraints. the fragment is enough expressive to include many formulas of interest such as Horn clauses. Using the isomorphism of Lukasiewicz formulas and McNaughton functions, logical constraints are mapped to a set of linear constraints once the predicates are grounded on a given sample set. In this framework, it is shown how a collective classification scheme can be formulated as a quadratic programming problem, but the presented theory can be exploited in general to embed logical constraints into a learning process. the proposed approach is evaluated on a classification task to show how the use of the logical rules can be effective to improve the accuracy of a trained classifier.

关键词： Quadratic programming

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