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检索条件"主题词=Inductive Logic Programming"

共 525 条记录，以下是81-90 订阅

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Learning Strategies of inductive logic programming Using Reinforcement Learning 1

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32nd International Conference on inductive logic programming (ILP)

作者： Isobe, Takeru Inoue, Katsumi SOKENDAI Grad Inst Adv Studies Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

Learning settings are crucial for most inductive logic programming (ILP) systems to learn efficiently. Hypothesis spaces can be huge, and ILP systems take a long time to output solutions or even cannot terminate within time limits. Therefore, users must set suitable learning settings for each ILP task to bring the best performance of the system. However, most users struggle to set appropriate settings for the task they see for the first time. In this paper, we propose a method to make an ILP system more adaptable to tasks with weak learning biases. In particular, we attempt to learn efficient strategies for an ILP system using reinforcement learning (RL). We use Popper, a state-of-the-art ILP system that implements the concept of learning from failures (LFF). We introduce RL-Popper, which divides the hypothesis space into subspaces more minutely than Popper. RL is used to learn the efficient search order of the divided spaces. We provide the details of RL-Popper and showsome empirical results.

关键词： inductive logic programming Meta Learning Learning From Failures Reinforcement Learning

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Application of inductive logic programming for learning ECG waveforms 6th

Application of inductive logic programming for learning ECG ...

引用

6th Conference on Artificial Intelligence in Medicine Europe (AIME 97)

作者： Kokai, G Alexin, Z Gyimothy, T ATTILA JOZSEF UNIV DEPT APPL INFORMAT H-6720 SZEGED HUNGARY HUNGARIAN ACAD SCI RES GRP ARTIFICIAL INTELLIGENCE H-6720 SZEGED HUNGARY

ISBN: (纸本)354062709X

in this paper a learning system is presented which integrates an ECG waveform classifier (called PECG) with an interactive learner (called IMPUT). The PECG system is based on an attribute grammar specification of ECGs that has been transformed to Prolog. The IMPUT system combines the interactive debugging technique IDT with the unfolding algorithm introduced in SPECTRE. Using the IMPUT system we can effectively assist in preparing the correct description of the basic structures of ECG waveforms.(4)

关键词： inductive logic programming program specialization syntactic pattern recognition

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Ontology Population from the Web: an inductive logic programming-Based Approach

Ontology Population from the Web: an Inductive Logic Program...

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11th International Conference on Information Technology - New Generations (ITNG)

作者： Lima, Rinaldo Oliveira, Hilario Freitas, Fred Espinasse, Bernard Univ Fed Pernambuco Informat Ctr Recife PE Brazil Aix Marseille Univ LSIS Marseille France

ISBN: (纸本)9781479931873

The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extraction and classification of instances of the concepts and relations defined by an ontology. Developing IE rules for OP is an intensive and time-consuming process. Thus, an automated mechanism, based on machine-learning techniques, able to convert textual data from web pages into ontology instances may be a crucial path. This paper presents an inductive logic programming-based method that automatic induces symbolic extraction rules, which are used for populating a domain ontology with instances of entity classes. This method uses domain-independent linguistic patterns for retrieving candidate instances from web pages, and a WordNet semantic similarity measure as background knowledge to be used as input by a generic inductive logic programming system. Experiments were conducted concerning both the instance classification problem and a comparison with other popular machine learning algorithms, with encouraging results.

关键词： Ontology Population inductive logic programming Pattern Learning Information Extraction

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Efficient inductive logic programming Based on Particle Swarm Optimization 4th

Efficient Inductive Logic Programming Based on Particle Swar...

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4th European-Alliance-for-Innovation (EAI) International Conference on Artificial Intelligence for Communications and Networks (AICON)

作者： Obara, Kyosuke Takimoto, Munehiro Kumazawa, Tsutomu Kambayashi, Yasushi Tokyo Univ Sci Dept Informat Sci Chiba Japan Software Res Associates Inc Toshima Ku Tokyo Japan Nippon Inst Technol Dept Comp & Informat Engn Saitama Japan

ISBN: (纸本)9783031291258;9783031291265

inductive logic programming (ILP) is an inductive reasoning method based on the first-order predicative logic. This technology is widely used for data mining using symbolic artificial intelligence. ILP searches for a suitable hypothesis that covers positive examples and uncovers negative examples. The searching process requires a lot of execution cost to interpret many given examples for practical problems. In this paper, we propose a new hypothesis search method using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm based on behaviors of particles. In our approach, each particle repeatedly moves from a hypothesis to another hypothesis within a hypothesis space. At that time, some hypotheses are refined based on the value returned by a predefined evaluation function. Since PSO just searches a part of the hypothesis space, it contributes to the speed up of the execution of ILP. In order to demonstrate the effectiveness of our method, we have implemented it on Progol that is one of the ILP systems [6], and then we conducted numerical experiments. The results showed that our method reduced the hypothesis search time compared to another conventional Progol.

关键词： inductive logic programming Particle swarm optimization Progol

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Qualitative knowledge driven approach to inductive logic programming

Qualitative knowledge driven approach to inductive logic pro...

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International Conference on Industrial and Information Systems

作者： Ranasinghe, D. D. M. Karunananda, A. S. Open Univ Sri Lanka Dept Math & Comp Sci Fac Nat Sci Nugegoda Sri Lanka Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

ISBN: (纸本)9781424403219

Automating the learning of context specific robust rules from an evolving scene has been a research challenge in the area of cognitive vision systems. A research has been conducted to develop a system that learns context specific rules from an evolving scene by abstracting a model of human visual learning. Our system treats a set of symbolic data generated from an evolving real world scene and background knowledge as input to the system and inductive logic programming techniques are used to learn rules of the scene. The observed visual scene is represented in terms of qualitative spatial and temporal relations and these learnt relations are considered as input examples for inductive learning. A prototype has been developed for learning from various scenes of setting dinner tables. Currently the system is being tested to incorporate learning from different real world scenes thus improving the generalization power as well as combine more spatial and temporal representation and reasoning mechanisms to further enhance human like learning. This work can be adopted in automating a robot learning of object manipulation based on a visual scene.

关键词： cognitive systems inductive logic programming learning by example cognitive vision systems dinner table setting human visual learning qualitative knowledge robot learning visual scene

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Differentiable neural logic networks and their application onto inductive logic programming

Differentiable neural logic networks and their application o...

引用

作者： Payani, Ali Georgia Institute of Technology

学位级别：博士

Despite the impressive performance of Deep Neural Networks (DNNs), they usually lack the explanatory power of disciplines such as logic programming. Even though they can learn to solve very difficult problems, the learning is usually implicit and it is very difficult, if not impossible, to interpret and decipher the underlying explanations that is implicitly stored in the weights of the neural network models. On the other hand, standard logic programming is usually limited in scope and application compared to the DNNs. The objective of this dissertation is to bridge the gap between these two disciplines by presenting a novel paradigm for learning algorithmic and discrete tasks via neural networks. This novel approach, uses the differentiable neural network to design interpretable and explanatory models that can learn and represent Boolean functions efficiently. We will investigate the application of these differentiable Neural logic (dNL) networks in disciplines such as inductive logic programming, Relational Reinforcement Learning, as well as in discrete algorithmic tasks such as decoding LDPC codes over Binary erasure Channels. inductive logic programming (ILP) is an important branch of machine learning that uses formal logic to define and solve problems. Compared to the DNNs, ILP provides high degree of interpretability and can learn from small number of examples and has superior generalization ability. However, standard ILP solvers are not differentiable and cannot benefit from the impressive power of DNNs. In this dissertation, we reformulate the ILP as a differentiable neural network by exploiting the explanatory power of dNL networks. This novel neural based ILP solver (dNL-ILP) is capable of learning auxiliary and invented predicates as well as learning complex recursive predicates. We show that dNL-ILP outperforms the current state of the art ILP solvers in a variety of benchmark algorithmic tasks as well as larger scale relational classification tasks. In

关键词： inductive logic programming ILP Neural logic Relational reinforcement learning

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Exploring Abstract Argumentation-Based Approaches to Tackle Inconsistent Observations in inductive logic programming 17th

Exploring Abstract Argumentation-Based Approaches to Tackle ...

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17th International Conference of the Italian-Association-for-Artificial-Intelligence (AI*IA)

作者： Pazienza, Andrea Ferilli, Stefano Univ Bari Dipartimento Informat Bari Italy

ISBN: (纸本)9783030038403;9783030038397

Noisy (uncertain, missing, or inconsistent) information, typical of many real-world domains, may dramatically affect the performance of logic-based Machine Learning. Multistrategy Learning approaches have been tried to solve this problem by coupling inductive logic programming with other kinds of inference. While uncertainty has been tackled using probabilistic approaches, and abduction has been used to deal with missing data, inconsistency is still an open problem. In the Multistrategy Learning perspective, this paper proposes to attack this latter kind of noise using (abstract) Argumentation, an inferential strategy aimed at handling conflicting information. More specifically, it defines a pre-processing operator based on abstract argumentation that can detect and remove noisy atoms from the observations before running the learning system on the polished data. Quantitative and qualitative experiments point out some strengths and weaknesses of the proposed approach, and suggest lines for future research on this topic.

关键词： inductive logic programming Conflicting information Abstract argumentation Multistrategy Learning

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Active inductive logic programming for Code Search 19

Active Inductive Logic Programming for Code Search

引用

41st IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Sivaraman, Aishwarya Zhang, Tianyi Van den Broeck, Guy Kim, Miryung Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9781728108698

Modern search techniques either cannot efficiently incorporate human feedback to refine search results or cannot express structural or semantic properties of desired code. The key insight of our interactive code search technique ALICE is that user feedback can be actively incorporated to allow users to easily express and refine search queries. We design a query language to model the structure and semantics of code as logic facts. Given a code example with user annotations, ALICE automatically extracts a logic query from code features that are tagged as important. Users can refine the search query by labeling one or more examples as desired (positive) or irrelevant (negative). ALICE then infers a new logic query that separates positive examples from negative examples via active inductive logic programming. Our comprehensive simulation experiment shows that ALICE removes a large number of false positives quickly by actively incorporating user feedback. Its search algorithm is also robust to user labeling mistakes. Our choice of leveraging both positive and negative examples and using nested program structure as an inductive bias is effective in refining search queries. Compared with an existing interactive code search technique, ALICE does not require a user to manually construct a search pattern and yet achieves comparable precision and recall with much fewer search iterations. A case study with real developers shows that ALICE is easy to use and helps express complex code patterns.

关键词： Code Search Active Learning inductive logic programming

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A Review of inductive logic programming Applications for Robotic Systems 1

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32nd International Conference on inductive logic programming (ILP)

作者： Youssef, Youssef Mahmoud Mueller, Martin E. Hsch Bonn Rhein Sieg Grantham Allee 20 D-53757 Bonn Germany

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

This study presents a review of applications of inductive logic programming (ILP) for robotic systems. The aim of the paper is to demonstrate the different methods of applying ILP to a robotic system and to also highlight some of the limitations that already exist. ILP can aid in the development of explainable and trustworthy robotics systems.

关键词： inductive logic programming Robotics Review

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Fuzzy inductive logic programming: Learning fuzzy rules with their implication

Fuzzy inductive logic programming: Learning fuzzy rules with...

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IEEE International Conference on Fuzzy Systems

作者： Serrurier, M Sudkamp, T Dubois, D Prade, H Univ Toulouse 3 IRIT F-31062 Toulouse France

ISBN: (纸本)0780391586

inductive logic programming (ILP) is a generic tool aiming at learning rules from relational databases. Introducing fuzzy sets and fuzzy implication connectives in this framework allows us to increase the expressive power of the induced rules while keeping the readability of the rules. Moreover, fuzzy sets facilitate the handling of numerical attributes by avoiding crisp and arbitrary transitions between classes. In this paper, the meaning of a fuzzy rule is encoded by its implication operator, which is to be determined in the learning process. An algorithm is proposed for inducing first order rules having fuzzy predicates, together with the most appropriate implication operator. The benefits of introducing fuzzy logic in ILP and the validation process of what has been learnt are discussed and illustrated on a benchmark.

关键词： Implication inductive logic programming Fuzzy rules Fuzzy Learning

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