Mostrando recursos 1 - 20 de 3.150

  1. Deciding entailments in inductive separation logic with tree automata

    Radu Iosif; Adam Rogalewicz
    Abstract. Separation Logic (SL) with inductive definitions is a natural formal-ism for specifying complex recursive data structures, used in compositional veri-fication of programs manipulating such structures. The key ingredient of any au-tomated verification procedure based on SL is the decidability of the entailment problem. In this work, we reduce the entailment problem for a non-trivial subset of SL describing trees (and beyond) to the language inclusion of tree automata (TA). Our reduction provides tight complexity bounds for the problem and shows that entailment in our fragment is EXPTIME-complete. For practical purposes, we leverage from recent advances in automata theory, such...

  2. Theorem Proving for Maude's Rewriting Logic

    Rusu, Vlad; Clavel, Manuel
    We present an approach based on inductive theorem proving for verifying invariance properties of systems specified in Rewriting Logic, an executable specification language implemented (among others) in the Maude tool. Since theorem proving is not directly available for rewriting logic, we define an encoding of rewriting logic into its membership equational (sub)logic. Then, inductive theorem provers for membership equational logic, such as the itp tool, can be used for verifying the resulting membership equational logic specification, and, implicitly, for verifying invariance properties of the original rewriting logic specification. The approach is illustrated first on a 2-process Bakery algorithm and then...

  3. Learning semantic lexicons from a part-of-speech and semantically tagged corpus using inductive logic programming

    Pascale Sébillot; Cécile Fabre; Pierrette Bouillon; Vincent Claveau; Pierrette Bouillon; James Cussens; Alan M. Frisch
    This paper describes an inductive logic programming learning method designed to acquire from a corpus specific Noun-Verb (N-V) pairs—relevant in information retrieval applications to perform index expansion—in order to build up semantic lexicons based on Pustejovsky’s generative lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lexical model, called the qualia structure, words are described in terms of semantic roles. For example, the telic role indi-cates the purpose or function of an item (cut for knife), the agentive role its creation mode (build for house), etc. The qualia structure of a noun is mainly made up of...

  4. Building Rules on top of Ontologies? Inductive Logic Programming can help!

    Francesca A. Lisi; Floriana Esposito
    Abstract. Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially by applying Machine Learning algorithms. In this paper we show that the form of Machine Learning known under the name of Inductive Logic Programming (ILP) can help. In particular, we take a critical look at two ILP proposals based on knowledge representation frameworks that integrate Description Logics and Horn Clausal Logic and draw from them general conclusions that can be considered as guidelines for further ILP research of interest to the Semantic Web. 1

  5. Inducing Evolution-Robust Pointcuts

    Mathieu Braem; Kris Gybels; Andy Kellens; Wim Vanderperren
    One of the problems in Aspect-Oriented Software Development is specifying pointcuts that are robust with respect to evolution of the base program. We propose to use Inductive Logic Programming, and more specifically the FOIL algorithm, to automatically discover intensional pattern-based pointcuts. In this paper we demonstrate this approach using several experiments in Java, where we successfully induce a pointcut from a given set of joinpoints. Furthermore, we present the tool chain and IDE that supports our approach. 1.

  6. CALVIN: A Personalized Web-Search Agent based on Monitoring User Actions

    Sandra Zabala; Gabor Loerincs; Yubelsi Bello; Victor Dias
    In this paper we describe Calvin, an intelligent agent that learns user interests by monitoring user activities while he/she searches and browses the Web. The user profile is created and maintained from a contentbased and event-based analysis of the visited pages using Inductive Logic Programming. The user submits queries which are expanded considering the information represented in her/his profile. Once the expanded query is submitted to and answered byasearch engine, the agent performs a relevance ranking of the results based on the user interests. After some experiments, Calvin has demonstrated tobecapable of learning and adapting user interests without any explicit...

  7. Machine Learning, 43, 7–52, 2001

    Lorenza Saitta
    Abstract. Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the effects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with...

  8. Hierarchical Relational Reinforcement Learning

    Experiments with relational reinforcement learning, a combination of reinforcement learning and inductive logic programming, are presented. This technique offers greater expressive power than that offered by traditional reinforcement learning. We use it to find general solutions in the blocks world domain. We discuss some difficulties associated with relational reinforcement learning, specifically its decreased effectiveness when presented with more complex problems. Finally, we explore ways in which hierarchical methods and learning subroutines may be able to overcome some of these weaknesses. 1.

  9. Combining Bayesian networks with higher-order data representations

    Elias Gyftodimos; Peter A. Flach
    Abstract. This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the expressive power of higher-order logics. We discuss how the proposed graphical model is used in order to define a probability distribution semantics over particular families of higher-order terms. We give an example of the application of our method on the Mutagenesis domain, a popular dataset from the Inductive Logic Programming community, showing how we employ probabilistic inference and model learning for the construction of a probabilistic classifier based on Higher-Order Bayesian Networks. 1

  10. Probabilistic Inductive Logic Programming

    Luc De Raedt; Kristian Kersting
    Abstract. Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be used to learn different types...

  11. Abduction · Probabilistic

    Jianzhong Chen; Stephen Muggleton; José Santos; J. Chen; S. Muggleton; J. Santos; S. Muggleton; J. Santos
    Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge repre-senting a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches—abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to...

  12. On the Query Refinement in Searching a Bibliographic Database

    Nenad Stojanovic
    Abstract: In this paper we present an application of the logic-based query refinement in the searching for information in an information portal. The refinement approach is based on the discovery of causal relationships between queries regarding the inclusion relation between the answers of these queries. We define a formal model for the query-answering pairs and use methods from the inductive logic programming for the efficient calculation of a (lattice) order between them. In a case study we demonstrate the benefits of using our approach in the traditional information retrieval tasks. We focus on the combination of the free-text based querying...

  13. Position Paper

    Ashwin Srinivasan; James Cussens; Alan M. Frisch
    Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and computational logic. Armed with this strong foundation, ILP has been applied to important and interesting problems in the life sciences, engineering and the arts. This paper begins by briefly reviewing some example applications, in order to illustrate the benefits of ILP. In turn, the applications have brought into focus the need for more research into specific topics. We enumerate and elaborate five of these: (1) novel search methods; (2) incorporation of explicit probabilities; (3) incorporation of special-purpose reasoners; (4) parallel execution using commodity components;...

  14. Maintenance of Discovered Knowledge

    Michal Pechoucek; Olga Štepánková; Petr Mikšovský
    The paper addresses the well-known bottleneck of knowledge based system design and implementation – the issue of knowledge maintenance and knowledge evolution throughout its lifecycle. Two different machine learning methodologies, namely Inductive Logic Programming (ILP) and Explanation Based Generalisation (EBG) within the Decision Planning (DP) knowledge representation methodology, have been studied, compared, and tested on the example of industrial configuration of TV transmitters

  15. Abstract ARTICLE IN PRESS Annals of Pure and Applied Logic ( ) – Computational inductive de nability

    Dexter Kozen
    It is shown that over any countable rst-order structure, IND programs with dictionaries accept

  16. Testing by means of inductive program learning

    Francesco Bergadano; Daniele Guneitl
    Given a program P and a set of alternative programs //’, we generate a sequence of test cases that are adequate, in the sense that they distinguish the given program from all alternatives The, m(,thod is related to fault-based approaches to test case generation, but programs in P need not he s]mp]e mutations of P. The technique for generating an adequate test set is based on the inductive learning of programs from finite sets of input-output examples: given a partial test set. we generate inductively a program P ’ E P which is consistent with P on those input values;...

  17. Relational learning as search in a critical region

    Marco Botta; Attilio Giordana; Lorenza Saitta; Michèle Sebag; James Cussens; Alan M. Frisch
    Machine learning strongly relies on the covering test to assess whether a candidate hypothesis covers training examples. The present paper investigates learning relational concepts from examples, termed relational learning or inductive logic programming. In particular, it investigates the chances of success and the computational cost of relational learning, which appears to be severely affected by the presence of a phase transition in the covering test. To this aim, three up-to-date relational learners have been applied to a wide range of artificial, fully relational learning problems. A first experimental observation is that the phase transition behaves as an attractor for relational...

  18. Towards reasoning about the past in neural-symbolic systems

    Rafael V. Borges; Luís C. Lamb
    Reasoning about the past is of fundamental importance in several applications in computer science and artificial intelligence, including reactive systems and planning. In this paper we propose efficient temporal knowledge representation algorithms to reason about and implement past time logical operators in neural-symbolic systems. We do so by extending models of the Connectionist Inductive Learning and Logic Programming System with past operators. This contributes towards integrated learning and reasoning systems considering temporal aspects. We validate the effectiveness of our approach by means of case studies. 1

  19. L: Logical and Relational Learning

    Kristian Kersting
    Abstract. Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and learning. This thesis approaches SRL from an inductive logic programming (ILP) perspective and starts with developing a general framework for SRL: probabilistic ILP. Based on this foundation, the thesis shows how to incorporate the logical concepts of objects and relations among these objects into Bayesian networks. As time and actions are not just other relations, it afterwards develops approaches to probabilistic ILP over time and for making complex decision in...

  20. Exploiting inductive logic programming techniques for declarative process mining

    Federico Chesani; Evelina Lamma; Paola Mello; Marco Montali; Fabrizio Riguzzi; Sergio Storari
    In the last few years, there has been a growing interest in the adoption of declarative paradigms for modeling and verifying pro-cess models. These paradigms provide an abstract and human under-standable way of specifying constraints that must hold among activities executions rather than focusing on a specific procedural solution. Min-ing such declarative descriptions is still an open challenge. In this paper, we present a logic-based approach for tackling this problem. It relies on Inductive Logic Programming techniques and, in particular, on a modi-fied version of the Inductive Constraint Logic algorithm. We investigate how, by properly tuning the learning algorithm, the...

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