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BenEliyahuZohary, Rachel; Francez, Nissim; Kaminski, Michael
3 Citations
The paper identifies a problem in default reasoning in Reiter’s Default Logic and related systems: elements which are similar given the axioms only, become distinguishable in extensions. We explain why, sometimes, this is considered undesirable. Two approaches are presented for guaranteeing similarity preservation: One approach formalizes a way of uniformly applying the defaults to all similar elements by introducing generic extensions, which depend only on similarity types of objects. According to the second approach, for a restricted class of default theories, a default theory is viewed as a “shorthand notation” to what is “really meant” by its formulation. In this approach we propose a rewriting of defaults in a form that guarantees similarity preservation of the modified theory. It turns out that the above two approaches yield the same result.
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d’Amato, Claudia; Fanizzi, Nicola; Fazzinga, Bettina; Gottlob, Georg; Lukasiewicz, Thomas
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10 Citations
Semantic Web search is currently one of the hottest research topics in both Web search and the Semantic Web. In previous work, we have presented a novel approach to Semantic Web search, which allows for evaluating ontologybased complex queries that involve reasoning over the Web relative to an underlying background ontology. We have developed the formal model behind this approach, and provided a technique for processing Semantic Web search queries, which consists of an offline ontological inference step and an online reduction to standard Web search. In this paper, we continue this line of research. We further enhance the above approach by the use of inductive rather than deductive reasoning in the offline inference step. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. The inductive variant also allows to infer new (not logically deducible) knowledge (from training individuals). We report on a prototype implementation of (both the deductive and) the inductive variant of our approach in desktop search, and we provide extensive new experimental results, especially on the running time and the precision and the recall of our new approach.
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By
Li, Dandan ; SummersStay, Douglas
Word embedding models excel in measuring word similarity and completing analogies. Word embeddings based on different notions of context trade off strengths in one area for weaknesses in another. Linear bagofwords contexts, such as in word2vec, can capture topical similarity better, while dependencybased word embeddings better encode functional similarity. By combining these two word embeddings using different metrics, we show how the best aspects of both approaches can be captured. We show stateoftheart performance on standard word and relational similarity benchmarks.
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By
ArziGonczarowski, Z.; Lehmann, D.
4 Citations
Perception is the recognition of elements and events in the environment, usually through integration of sensory impressions. It is considered here as a broad, highlevel, concept (different from the sense in which computer vision/audio research takes the concept of perception). We propose and develop premises for a formal approach to a fundamental phenomenon in AI: the diversity of artificial perceptions. A mathematical substratum is proposed as a basis for a rigorous theory of artificial perceptions. A basic mathematical category is defined. Its objects are perceptions, consisting of world elements, connotations, and a threevalued (true, false, undefined) predicative correspondence between them. Morphisms describe paths between perceptions. This structure serves as a basis for a mathematical theory. This theory provides a way of extending and systematizing certain intuitive pretheoretical conceptions about perception, about improving and/or completing an agent's perceptual grasp, about transition between various perceptions, etc. Some example applications of the theory are analyzed.
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Hazan, Hananel ; Saunders, Daniel J.; Sanghavi, Darpan T.; Siegelmann, Hava; Kozma, Robert
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Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and selforganized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a selforganized grid of filters via cooperative and competitive excitatoryinhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network training, and (ii) switching the inhibition level from low to high (twolevel) after an initial training segment. During the labeling phase, the spiking activity generated by data with known labels is used to assign neurons to categories of data, which are then used to evaluate the network’s classification ability on a heldout set of test data. Several biologically plausible evaluation rules are proposed and compared, including a populationlevel confidence rating, and an ngram inspired method. The effectiveness of the proposed selforganized learning mechanism is tested using the MNIST benchmark dataset, as well as using images produced by playing the Atari Breakout game.
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By
Veale, Tony
Text is an especially malleable medium for human and machine creativity. When guided by the appropriate symbolic and/or statistical models, even a small and seemingly superficial change at the formal level can result in a predictable yet profound change at the semantic and pragmatic level. Text is also a virtually unlimited resource on the web, which offers abundant, freeflowing channels of topical texts for almost every genre and register. In this paper we consider diverse approaches to transforming these input channels into new and creative streams of machinegenerated outputs. We focus on the specific kind of linguistic creativity associated with metaphor, yet also demonstrate that divergent approaches to metaphor generation can, in turn, enable divergent uses and applications for machine creativity.
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By
Rajasekar, Arcot; Minker, Jack
6 Citations
We address the problem of a consistent fixpoint semantics for general disjunctive programs restricted to stratifiable programs which do not recurse through negative literals. We apply the nonmonotonic fixpoint theory developed by Apt, Blair and Walker to a closure operatorT^{c} and develop a fixpoint semantics for stratified disjunctive programs. We also provide an iterative definition for negation, called the Generalized Closed World Assumption for Stratified programs (GCWAS), and show that our semantics captures this definition. We develop a modeltheoretic semantics for stratified disjunctive programs and show that the least state characterized by the fixpoint semantics corresponds to a stablestate defined in a manner similar to the stablemodels of Gelfond and Lifschitz. We also discuss a weaker stratification semantics for general disjunctive programs based on the Weak Generalized Closed World Assumption.
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By
Giannotti, Fosca; Greco, Sergio; Saccà, Domenico; Zaniolo, Carlo
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19 Citations
While nondeterminism has long been established as a key concept in logic programming, its importance in the context of deductive databases was recognized only recently. This paper provides an overview of recent results on this topic with the aim of providing an introduction to the theory and practice of nondeterminism in deductive databases. In particular we (i) recall the main results linking nondeterministic constructs in database languages to the theory of data complexity and the expressibility hierarchy of query languages; (ii) provide a reasoned introduction to effective programming with nondeterministic constructs; (iii) compare the usage of nondeterministic constructs in languages such as LDL++ to that of traditional logic programming languages; (iv) discuss the link between the semantics of logic programs with nondeterministic constructs and the stablemodel semantics of logic programs with negation.
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By
Cuzzolin, Fabio
4 Citations
The study of the interplay between belief and probability can be posed in a geometric framework, in which belief and plausibility functions are represented as points of simplices in a Cartesian space. Probability approximations of belief functions form two homogeneous groups, which we call “affine” and “epistemic” families. In this paper we focus on relative plausibility, belief, and uncertainty of probabilities of singletons, the “epistemic” family. They form a coherent collection of probability transformations in terms of their behavior with respect to Dempster’s rule of combination. We investigate here their geometry in both the space of all pseudo belief functions and the probability simplex, and compare it with that of the affine family. We provide sufficient conditions under which probabilities of both families coincide.
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By
Vantaggi, Barbara
17 Citations
A definition of stochastic independence which avoids the inconsistencies (related to events of probability 0 or 1) of the classic one has been proposed by Coletti and Scozzafava for two events. We extend it to conditional independence among finite sets of events. In particular, the case of (finite) discrete random variables is studied. We check which of the relevant properties connected with graphical structures hold. Hence, an axiomatic characterization of these independence models is given and it is compared to the classic ones.
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