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...
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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.
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 gi...
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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is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information.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.
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 gi...
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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.
We present the latest, substantially improved, version of NoHR, a reasoner designed to answer queries over hybrid theories composed of an OWL ontology in Description logics and a set of non-monotonic rules in logic Pr...
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ISBN:
(纸本)9783319616605;9783319616599
We present the latest, substantially improved, version of NoHR, a reasoner designed to answer queries over hybrid theories composed of an OWL ontology in Description logics and a set of non-monotonic rules in logicprogramming. Whereas the need to combine the distinctive features of these two knowledge representation and reasoning approaches stems from real world applications, their integration is nevertheless theoretically challenging due to their substantial semantical differences. NoHR has been developed as a plug-in for the widely used ontology editor Protege - in fact, the first hybrid reasoner of its kind for Protege, building on a combination of reasoners dedicated to OWL and rules - but it is also available as a library, allowing for its integration within other environments and applications. Compared to previous versions of NoHR, this is the first that supports all polynomial OWL profiles, and even beyond, allowing for its usage with real-world ontologies that do not fit within a single profile. In addition, NoHR has now an enhanced integration with its rule engine, which provides support for a vast number of standard built-in Prolog predicates that considerably extend its usability.
Entity resolution in settings with rich relational structure often introduces complex dependencies between co-references. Exploiting these dependencies is challenging - it requires seamlessly combining statistical, re...
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ISBN:
(纸本)9781538638354
Entity resolution in settings with rich relational structure often introduces complex dependencies between co-references. Exploiting these dependencies is challenging - it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple partial representations of a family tree are provided, from the perspective of different family members, and the challenge is to reconstruct a family tree from these multiple, noisy, partial views. this reconstruction is crucial for applications such as understanding genetic inheritance, tracking disease contagion, and performing census surveys. Here, we design a model that incorporates statistical signals, such as name similarity, relational information, such as sibling overlap, and logical constraints, such as transitivity and bijective matching, in a collective model. We show how to integrate these features using probabilistic soft logic, a scalable probabilistic programming framework. In experiments on real-world data, our model significantly outperforms state-of-the-art classifiers that use relational features but are incapable of collective reasoning.
Temporal logic formalizes reasoning about the possible behaviours of a system over time. For example, a temporal formula may stipulate that an occurrence of event A may be, or must be, followed by an occurrence of eve...
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Temporal logic formalizes reasoning about the possible behaviours of a system over time. For example, a temporal formula may stipulate that an occurrence of event A may be, or must be, followed by an occurrence of event B. Traditional temporal logics, however, are insufficient for reasoning about multi-agent systems. In order to stipulate, for example, that a specific agent can ensure that A is followed by B, references to agents, their capabilities, and their intentions must be added to the logic, yielding Alternating-time Temporal logic (ATL). ATL is a temporal logicthat is interpreted over multi-player games, whose players correspond to agents that pursue temporal objectives. the hardness of the model-checking problem -- whether a given formula is true for a given multi-agent system -- depends on how the players decide on the next move in the game (for example, whether they take turns, or make independent concurrent decisions, or bid for the next move), how the outcome of a move is computed (deterministically or stochastically), how much memory the players have available for making decisions, and which kind of objectives they pursue (qualitative or quantitative). the expressiveness of ATL is still insufficient for reasoning about equilibria and related phenomena in non-zero-sum games, where the players may have interfering but not necessarily complementary objectives. For this purpose, the behavioural strategies (a.k.a. policies) of individual players can been added to the logic as quantifiable first-order entities, yielding Strategy logic. We survey several known results about these multi-player game logics and point out some open problems.
In recent years, foundations have been laid for a turn in logicprogramming paradigms in continuous domains. Fuzzy answer set programming (FASP) has emerged as a combination of a tool for non-monotonicreasoning and s...
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the paper deals with base revision for Answer Set programming (ASP). Base revision in classical logic is done by the removal of formulas. Exploiting the non-monotonicity of ASP allows one to propose other revision str...
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ISBN:
(纸本)9783319675824;9783319675817
the paper deals with base revision for Answer Set programming (ASP). Base revision in classical logic is done by the removal of formulas. Exploiting the non-monotonicity of ASP allows one to propose other revision strategies, namely addition strategy or removal and/or addition strategy. these strategies allow one to define families of rule-based revision operators. the paper presents a semantic characterization of these families of revision operators in terms of answer sets. this characterization allows one to equivalently consider the evolution of syntactic logic programs and the evolution of their semantic content.
In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a constrained optimization problem with...
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In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a constrained optimization problem with elastic net regularization in kernel feature space and solve it. We consider outlier detection (where KPCA is employed) as an application for SKPCA, using the RBF kernel. We test it on 5 real world datasets and show that by using just 4% (or even less) of the principal components (PCs), where each PC has on average less than 12% non-zero elements in the worst case among all 5 datasets, we are able to nearly match and in 3 datasets even outperform KPCA. We also compare the performance of our method with a recently proposed method for SKPCA and show that our method performs better in terms of both accuracy and sparsity. We also provide a novel probabilistic proof to justify the existence of sparse solutions for KPCA using the RBF kernel. To the best of our knowledge, this is the first attempt at theoretically analyzing the validity of SKPCA.
Modern software is often developed with advanced mechanisms for code reuse. A software module may build on other software modules or libraries where the source code is not available. And even if the source code is kno...
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