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8th international conference on logic programming and nonmonotonic reasoning

作者： Vennekens, J Denecker, M Katholieke Univ Leuven Dept Comp Sci B-3001 Louvain Belgium

ISBN: (纸本)3540285385

ID-logic uses ideas from the field of logic programming to extend second order logic with non-monotone inductive defintions. In this work, we reformulate the semantics of this logic in terms of approximation theory, an algebraic theory which generalizes the semantics of several non-monotonic reasoning formalisms. this allows us to apply certain abstract modularity theorems, developed within the framework of approximation theory, to ID-logic. As such, we are able to offer elegant and simple proofs of generalizations of known theorems, m well as some new results.

关键词： logic programming

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On Uniform Equivalence of Epistemic logic Programs

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thEORY AND PRACTICE OF logic programming 2019年第5-6期19卷 826-840页

作者： Faber, Wolfgang Morak, Michael Woltran, Stefan Univ Klagenfurt Klagenfurt Austria TU Wien Vienna Austria

Epistemic logic Programs (ELPs) extend Answer Set programming (ASP) with epistemic negation and have received renewed interest in recent years. this led to the development of new research and efficient solving systems for ELPs. In practice, ELPs are often written in a modular way, where each module interacts with other modules by accepting sets of facts as input, and passing on sets of facts as output. An interesting question then presents itself: under which conditions can such a module be replaced by another one without changing the outcome, for any set of input facts? this problem is known as uniform equivalence, and has been studied extensively for ASP. For ELPs, however, such an investigation is, as of yet, missing. In this paper, we therefore propose a characterization of uniform equivalence that can be directly applied to the language of state-of-the-art ELP solvers. We also investigate the computational complexity of deciding uniform equivalence for two ELPs, and show that it is on the third level of the polynomial hierarchy.

关键词： logic programming Epistemic Negation non-monotonic reasoning Uniform Equivalence

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Annotated defeasible logic

Annotated defeasible logic

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33rd international conference on logic programming colocated with the 23rd international conference on Principles and Practice of Constraint programming / 20th international conference on theory and Applications of Satisfiability Testing

作者： Governatori, Guido Maher, Michael J. CSIRO Data61 Brisbane Qld Australia Reasoning Res Inst Canberra ACT Australia

Defeasible logics provide several linguistic features to support the expression of defeasible knowledge. there is also a wide variety of such logics, expressing different intuitions about defeasible reasoning. However, the logics can only combine in trivial ways. this limits their usefulness in contexts where different intuitions are at play in different aspects of a problem. In particular, in some legal settings, different actors have different burdens of proof, which might be expressed as reasoning in different defeasible logics. In this paper, we introduce annotated defeasible logic as a flexible formalism permitting multiple forms of defeasibility, and establish some properties of the formalism.

关键词： defeasible logic non-monotonic reasoning annotated logics legal reasoning

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INDUCTIVE logic programming AS ABDUCTIVE SEARCH

INDUCTIVE LOGIC PROGRAMMING AS ABDUCTIVE SEARCH

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26th international conference on logic programming (ICLP)

作者： Corapi, Domenico Russo, Alessandra Lupu, Emil Imperial Coll London Dept Comp 180 Queens Gate London SW7 2AZ England

ISBN: (纸本)9783939897170

We present a novel approach to non-monotonic ILP and its implementation called TAL (Top-directed Abductive Learning). TAL overcomes some of the completeness problems of ILP systems based on Inverse Entailment and is the first top-down ILP system that allows background theories and hypotheses to be normal logic programs. the approach relies on mapping an ILP problem into an equivalent ALP one. this enables the use of established ALP proof procedures and the specification of richer language bias with integrity constraints. the mapping provides a principled search space for an ILP problem, over which an abductive search is used to compute inductive solutions.

关键词： Inductive logic programming Abductive logic programming non-monotonic reasoning

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Belief logic programming: Uncertainty reasoning with Correlation of Evidence

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10th international conference on logic programming and nonmonotonic reasoning

作者： Wan, Hui Kifer, Michael SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9783642042379

Belief logic programming (BLP) is a novel form of quantitative logic programming in the presence of uncertain and inconsistent, information, which was designed to be able to combine and correlate evidence obtained from non-independent information sources. BLP has non-monotonic semantics based on the concepts of belief combination functions and is inspired by Dempster-Shafer theory of evidence. Most importantly, unlike the previous efforts to integrate uncertainty and logic programming, BLP can correlate structural information contained in rules and provides more accurate certainty estimates. the results are illustrated via simple, yet, realistic examples of rule-based Web service integration.

关键词： logic programming

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Revisiting Explicit Negation in Answer Set programming

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thEORY AND PRACTICE OF logic programming 2019年第5-6期19卷 908-924页

作者： Aguado, Felicidad Cabalar, Pedro Fandinno, Jorge Pearce, David Perez, Gilberto Vidal, Concepcion Univ A Coruna CITIC Informat Retrieval Lab La Coruna Spain Univ Toulouse IRIT CNRS Toulouse France Univ Potsdam Potsdam Germany Univ Politecn Madrid Madrid Spain

A common feature in Answer Set programming is the use of a second negation, stronger than default negation and sometimes called explicit, strong or classical negation. this explicit negation is normally used in front of atoms, rather than allowing its use as a regular operator. In this paper we consider the arbitrary combination of explicit negation with nested expressions, as those defined by Lifschitz, Tang and Turner. We extend the concept of reduct for this new syntax and then prove that it can be captured by an extension of Equilibrium logic with this second negation. We study some properties of this variant and compare to the already known combination of Equilibrium logic with Nelson's strong negation.

关键词： Answer set programming non-monotonic reasoning Equilibrium logic Explicit negation

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A Roadmap for Neuro-argumentative Learning 17

A Roadmap for Neuro-argumentative Learning

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17th international Workshop on Neural-Symbolic Learning and reasoning (NeSy)

作者： Proietti, Maurizio Toni, Francesca CNR IASI Rome Italy Imperial Coll London Dept Comp London England

Computational argumentation (CA) has emerged, in recent decades, as a powerful formalism for knowledge representation and reasoning in the presence of conflicting information, notably when reasoning non-monotonically with rules and exceptions. Much existing work in CA has focused, to date, on reasoning with given argumentation frameworks (AFs) or, more recently, on using AFs, possibly automatically drawn from other systems, for supporting forms of XAI. In this short paper we focus instead on the problem of learning AFs from data, with a focus on neuro-symbolic approaches. Specifically, we overview existing forms of neuro-argumentative (machine) learning, resulting from a combination of neural machine learning mechanisms and argumentative (symbolic) reasoning. We include in our overview neuro-symbolic paradigms that integrate reasoners with a natural understanding in argumentative terms, notably those capturing forms of non-monotonic reasoning in logic programming. We also outline avenues and challenges for future work in this spectrum.

关键词： Computational argumentation Artificial neural networks non-monotonic reasoning

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Learning probabilistic logic models from probabilistic examples

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MACHINE LEARNING 2008年第1期73卷 55-85页

作者： Chen, Jianzhong Muggleton, Stephen Santos, Jose Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

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 representing 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 SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

关键词： probabilistic inductive logic programming stochastic logic programs abduction probabilistic examples

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non-monotonic Temporal Goals

Non-monotonic Temporal Goals

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12th international conference on logic programming and nonmonotonic reasoning (LPNMR)

作者： Goncalves, Ricardo Knorr, Matthias Leite, Joao Slota, Martin Univ Nova Lisboa CENTRIA P-1200 Lisbon Portugal

ISBN: (纸本)9783642405648

In this paper we introduce a logic programming based framework which allows the representation of conditional non-monotonic temporal beliefs and goals in a declarative way. We endow it with stable model like semantics that allows us to deal with conflicting goals and generate possible alternatives. We show that our framework satisfies some usual properties on goals and that it allows imposing alternative constraints on the interaction between beliefs and goals. We prove the decidability of the usual reasoning tasks and show how they can be implemented using an ASP solver and an LTL reasoner in a modular way, thus taking advantage of existing LTL reasoners and ASP solvers.

关键词： logic programming

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Ticker: A system for incremental ASP-based stream reasoning

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thEORY AND PRACTICE OF logic programming 2017年第5-6期17卷 744-763页

作者： Beck, Harald Eiter, thomas Folie, Christian Vienna Univ Technol Inst Informat Syst Favoritenstr 9-11 A-1040 Vienna Austria

In complex reasoning tasks, as expressible by Answer Set programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. this paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS, which extends ASP for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: first, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth-maintenance system to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms.

关键词： Stream reasoning Answer set programming non-monotonic reasoning

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