the proceedings contain 12 papers. the special focus in this conference is on . the topics include: Pruning hypothesis spaces using learned domain theories;an investigation into the role of domain-knowledge on the use...
ISBN:
(纸本)9783319780894
the proceedings contain 12 papers. the special focus in this conference is on . the topics include: Pruning hypothesis spaces using learned domain theories;an investigation into the role of domain-knowledge on the use of embeddings;positive and unlabeled relational classification through label frequency estimation;on applying probabilistic logicprogramming to breast cancer data;logical vision: One-shot meta-interpretive learning from real images;demystifying relational latent representations;parallel online learning of event definitions;relational restricted boltzmann machines: A probabilistic logic learning approach;parallel inductivelogicprogramming system for superlinear speedup;inductive learning from state transitions over continuous domains.
the proceedings contain 10 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Estimation-based search space traversal in Pilp environments;inductivelogicprogramming m...
ISBN:
(纸本)9783319633411
the proceedings contain 10 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Estimation-based search space traversal in Pilp environments;inductivelogicprogramming meets relational databases;online structure learning for traffic management;learning through advice-seeking via transfer;distributional learning of regular formal graph system of bounded degree;learning relational dependency networks for relation extraction;towards nonmonotonic relational learning from knowledge graphs;learning predictive categories using lifted relational neural networks and generation of near-optimal solutions using ilp-guided sampling.
In this study, we improve our parallel inductivelogicprogramming (ilp) system to enable superlinear speedup. this improvement redesigns several features of our ilp learning system and parallel mechanism. the redesig...
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ISBN:
(数字)9783319780900
ISBN:
(纸本)9783319780900;9783319780894
In this study, we improve our parallel inductivelogicprogramming (ilp) system to enable superlinear speedup. this improvement redesigns several features of our ilp learning system and parallel mechanism. the redesigned ilp learning system searches and gathers all rules that have the same evaluation. the redesigned parallel mechanism adds a communication protocol for sharing the evaluation of the identified rules, thereby realizing superlinear speedup.
Following the initiative in 2010 taken by the Association for logicprogramming and Cambridge University Press, the full papers accepted for the internationalconference on logicprogramming again appear as a special ...
Following the initiative in 2010 taken by the Association for logicprogramming and Cambridge University Press, the full papers accepted for the internationalconference on logicprogramming again appear as a special issue of theory and Practice of logicprogramming (TPLP)—the 27thinternationalconference on logicprogramming Special Issue. Papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere were solicited in all areas of logicprogramming including but not restricted to: theory: Semantic Foundations, Formalisms, Non- monotonic Reasoning, Knowledge Representation. Implementation: Compilation, Memory Management, Virtual Machines, Parallelism. Environments: Program Analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing. Language Issues: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, programming Techniques. Related Paradigms: Abductive logicprogramming, inductivelogicprogramming, Constraint logicprogramming, Answer-Set programming. Applications: Databases, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics.
Here we describe a Description logic (DL) based inductivelogicprogramming (ilp) algorithm for learning relations of order. We test our algorithm on the task of learning user preferences from pairwise comparisons. th...
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In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normati...
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In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductivelogicprogramming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. the learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. the learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. the process integrates a novel and general methodology for theory revision based on ASP.
We present a method to prune hypothesis spaces in the context of inductivelogicprogramming. the main strategy of our method consists in removing hypotheses that are equivalent to already considered hypotheses. the d...
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ISBN:
(数字)9783319780900
ISBN:
(纸本)9783319780900;9783319780894
We present a method to prune hypothesis spaces in the context of inductivelogicprogramming. the main strategy of our method consists in removing hypotheses that are equivalent to already considered hypotheses. the distinguishing feature of our method is that we use learned domain theories to check for equivalence, in contrast to existing approaches which only prune isomorphic hypotheses. Specifically, we use such learned domain theories to saturate hypotheses and then check if these saturations are isomorphic. While conceptually simple, we experimentally show that the resulting pruning strategy can be surprisingly effective in reducing both computation time and memory consumption when searching for long clauses, compared to approaches that only consider isomorphism.
In this paper, we address the problem of defining a fixpoint semantics for Constraint Handling Rules (CHR) that captures the behavior of both simplification and propagation rules in a sound and complete way with respe...
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In this paper, we address the problem of defining a fixpoint semantics for Constraint Handling Rules (CHR) that captures the behavior of both simplification and propagation rules in a sound and complete way with respect to their declarative semantics. Firstly, we show that the logical reading of states with respect to a set of simplification rules can be characterized by a least fixpoint over the transition system generated by the abstract operational semantics of CHR. Similarly, we demonstrate that the logical reading of states with respect to a set of propagation rules can be characterized by the greatest fixpoint. then, in order to take advantage of both types of rules without losing fixpoint characterization, we present a new operational semantics with persistent constraints. We finally establish that this semantics can be characterized by two nested fixpoints, and we show that the resulting language is an elegant framework to program using coinductive reasoning.
Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discre...
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ISBN:
(数字)9783319780900
ISBN:
(纸本)9783319780900;9783319780894
Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discrete variables or suppose a discretization of continuous data. However, when working with real data, the discretization choices are critical for the quality of the model learned by LFIT. In this paper, we focus on a method that learns the dynamics of the system directly from continuous time-series data. For this purpose, we propose a modeling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.
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