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.
this paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductivelogicprogramming (ilp) and Large Language Models (LLMs). As AI and robotics continue to ...
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ISBN:
(纸本)9798400704635
this paper explores a hybrid approach to the multimodal co-construction of explanations for robot faults, integrating inductivelogicprogramming (ilp) and Large Language Models (LLMs). As AI and robotics continue to permeate various aspects of daily life, the ability of these systems to explain their actions and failures is crucial for fostering user trust and ensuring safe interactions. We propose a framework that combines the interpretability of ilp, which generates logical rules from data, withthe linguistic capabilities of LLMs, which provide natural language explanations. this approach enables the generation of coherent, contextually appropriate explanations that can be tailored to the needs of users.
the logicprogramming paradigm provides a flexible setting for representing, manipulating, checking, and elaborating proof structures. this is particularly true when the logicprogramming language allows for bindings ...
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the proceedings contain 10 papers. the special focus in this conference is on Functional and Constraint logicprogramming. the topics include: Code Generation for Higher inductive Types: A Study in Agda Metaprogrammin...
ISBN:
(纸本)9783030162016
the proceedings contain 10 papers. the special focus in this conference is on Functional and Constraint logicprogramming. the topics include: Code Generation for Higher inductive Types: A Study in Agda Metaprogramming;measuring Coverage of Prolog Programs Using Mutation Testing;runtime Verification in Erlang by Using Contracts;Enhancing POI Testing through the Use of Additional Information;synthesizing Set Functions;Towards a Constraint Solver for Proving Confluence with Invariant and Equivalence of Realistic CHR Programs;reference Type logic Variables in Constraint-logic Object-Oriented programming;FMS: Functional programming as a Modelling Language.
this book constitutes the thoroughly refereed post-conference proceedings of the 24thinternationalconference on inductivelogicprogramming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers ...
ISBN:
(纸本)9783319237077
this book constitutes the thoroughly refereed post-conference proceedings of the 24thinternationalconference on inductivelogicprogramming, ilp 2014, held in Nancy, France, in September 2014. the 14 revised papers presented were carefully reviewed and selected from 41 submissions. the papers focus on topics such as the inducing of logic programs, learning from data represented withlogic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.
the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random ...
ISBN:
(纸本)9783319405650
the proceedings contain 14 papers. the special focus in this conference is on inductivelogicprogramming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random forests;distributed parameter learning for probabilistic ontologies;meta-interpretive learning of data transformation programs;statistical relational learning with soft quantifiers;ontology learning from interpretations in lightweight description logics;constructing markov logic networks from first-order default rules;a note on mining all graphs;processing markov logic networks with GPUs;using ilp to identify pathway activation patterns in systems biology;an algebraic prolog for kernel programming;an exercise in declarative modeling for relational query mining;learning inference by induction and identification of transition models of biological systems in the presence of transition noise.
the integration of abduction and induction has lead to a variety of non-monotonic ilp systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other han...
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the integration of abduction and induction has lead to a variety of non-monotonic ilp systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other hand, Peircebayes is a recently proposed logic-based probabilistic programming approach that combines abduction with parameter learning to learn distributions of most likely explanations. In this paper, we propose an approach for integrating probabilistic inference withilp. the basic idea is to redefine the inductive task of XHAIL as a statistical abduction, and to use Peircebayes to learn probability distribution of hypotheses. An initial evaluation of the proposed algorithm is given using synthetic data.
We present an inductive spatio-temporal learning framework rooted in inductivelogicprogramming. With an emphasis on visuo-spatial language, logic, and cognition, the framework supports learning with relational spati...
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We present an inductive spatio-temporal learning framework rooted in inductivelogicprogramming. With an emphasis on visuo-spatial language, logic, and cognition, the framework supports learning with relational spatio-temporal features identifiable in a range of domains involving the processing and interpretation of dynamic visuo-spatial imagery. We present a prototypical system, and an example application in the domain of computing for visual arts and computational cognitive science.
Probabilistic inductivelogicprogramming (Pilp) systems extend ilp by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predicti...
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Statistical Relational Learning (SRL) approaches have been developed to learn in presence of noisy relational data by combining probability theory with first order logic. While powerful, most learning approaches for t...
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