8,231 research outputs found
ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11558-0_40Computing generalizers is relevant in a wide spectrum of automated
reasoning areas where analogical reasoning and inductive inference
are needed. The ACUOS system computes a complete and minimal
set of semantic generalizers (also called âanti-unifiersâ) of two structures
in a typed language modulo a set of equational axioms. By supporting
types and any (modular) combination of associativity (A), commutativity
(C), and unity (U) algebraic axioms for function symbols,
ACUOS allows reasoning about typed data structures, e.g. lists, trees,
and (multi-)sets, and typical hierarchical/structural relations such as is a
and part of. This paper discusses the modular ACU generalization tool
ACUOS and illustrates its use in a classical artificial intelligence problem.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2010-21062-C02-02 and TIN 2013-45732-C4-1-P, by Generalitat Valenciana PROMETEO2011/052, and by NSF Grant CNS 13-10109. J. Espert has also been supported by the Spanish FPU grant FPU12/06223.Alpuente Frasnedo, M.; Escobar RomĂĄn, S.; Espert Real, J.; Meseguer, J. (2014). ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance. En Logics in Artificial Intelligence. Springer. 573-581. https://doi.org/10.1007/978-3-319-11558-0_40S573581Alpuente, M., Escobar, S., Espert, J., Meseguer, J.: A Modular Order-sorted Equational Generalization Algorithm. Information and Computation 235, 98â136 (2014)Alpuente, M., Escobar, S., Meseguer, J., Ojeda, P.: A Modular Equational Generalization Algorithm. In: Hanus, M. (ed.) LOPSTR 2008. LNCS, vol. 5438, pp. 24â39. Springer, Heidelberg (2009)Alpuente, M., Escobar, S., Meseguer, J., Ojeda, P.: OrderâSorted Generalization. ENTCS 246, 27â38 (2009)Alpuente, M., Espert, J., Escobar, S., Meseguer, J.: ACUOS: A System for Modular ACU Generalization with Subtyping and Inheritance. Tech. rep., DSIC-UPV (2013), http://www.dsic.upv.es/users/elp/papers.htmlArmengol, E.: Usages of Generalization in Case-Based Reasoning. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 31â45. Springer, Heidelberg (2007)Clavel, M., DurĂĄn, F., Eker, S., Lincoln, P., MartĂ-Oliet, N., Meseguer, J., Talcott, C. (eds.): All About Maude - A High-Performance Logical Framework. LNCS, vol. 4350. Springer, Heidelberg (2007)Clavel, M., DurĂĄn, F., Eker, S., Lincoln, P., MartĂ-Oliet, N., Meseguer, J., Talcott, C.L.: Reflection, metalevel computation, and strategies. In: All About Maude [6], pp. 419â458Gentner, D.: Structure-Mapping: A Theoretical Framework for Analogy*. Cognitive Science 7(2), 155â170 (1983)Krumnack, U., Schwering, A., Gust, H., KĂŒhnberger, K.-U.: Restricted higher order anti unification for analogy making. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 273â282. Springer, Heidelberg (2007)Kutsia, T., Levy, J., Villaret, M.: Anti-Unification for Unranked Terms and Hedges. Journal of Automated Reasoning 520, 155â190 (2014)Meseguer, J.: Conditioned rewriting logic as a united model of concurrency. Theor. Comput. Sci. 96(1), 73â155 (1992)Muggleton, S.: Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic. Artif. Intell. 114(1-2), 283â296 (1999)Ontañón, S., Plaza, E.: Similarity measures over refinement graphs. Machine Learning 87(1), 57â92 (2012)Plotkin, G.: A note on inductive generalization. In: Machine Intelligence, vol. 5, pp. 153â163. Edinburgh University Press (1970)Pottier, L.: Generalisation de termes en theorie equationelle: Cas associatif-commutatif. Tech. Rep. INRIA 1056, Norwegian Computing Center (1989)Schmid, U., Hofmann, M., Bader, F., HĂ€berle, T., Schneider, T.: Incident Mining using Structural Prototypes. In: GarcĂa-Pedrajas, N., Herrera, F., Fyfe, C., BenĂtez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part II. LNCS, vol. 6097, pp. 327â336. Springer, Heidelberg (2010
Service-oriented Context-aware Framework
Location- and context-aware services are emerging technologies in mobile and
desktop environments, however, most of them are difficult to use and do not
seem to be beneficial enough. Our research focuses on designing and creating a
service-oriented framework that helps location- and context-aware,
client-service type application development and use. Location information is
combined with other contexts such as the users' history, preferences and
disabilities. The framework also handles the spatial model of the environment
(e.g. map of a room or a building) as a context. The framework is built on a
semantic backend where the ontologies are represented using the OWL description
language. The use of ontologies enables the framework to run inference tasks
and to easily adapt to new context types. The framework contains a
compatibility layer for positioning devices, which hides the technical
differences of positioning technologies and enables the combination of location
data of various sources
Metaphoric coherence: Distinguishing verbal metaphor from `anomaly\u27
Theories and computational models of metaphor comprehension generally circumvent the question of metaphor versus âanomalyâ in favor of a treatment of metaphor versus literal language. Making the distinction between metaphoric and âanomalousâ expressions is subject to wide variation in judgment, yet humans agree that some potentially metaphoric expressions are much more comprehensible than others. In the context of a program which interprets simple isolated sentences that are potential instances of crossâmodal and other verbal metaphor, I consider some possible coherence criteria which must be satisfied for an expression to be âconceivableâ metaphorically. Metaphoric constraints on object nominals are represented as abstracted or extended along with the invariant structural components of the verb meaning in a metaphor. This approach distinguishes what is preserved in metaphoric extension from that which is âviolatedâ, thus referring to both âsimilarityâ and âdissimilarityâ views of metaphor. The role and potential limits of represented abstracted properties and constraints is discussed as they relate to the recognition of incoherent semantic combinations and the rejection or adjustment of metaphoric interpretations
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
Owning Heller
Recent historical research using big-data techniques casts doubt on whether District of Columbia v. Heller was rightly decided according to originalist methods. These new discoveries put originalists in a bind. Do they embrace âfaint heartedâ originalism: the idea that as between the need for stability in prior decision making, settled expectations, and the coherence of the law, some adulterated decisions must remain enforced for the greater good? Or do they follow Justice Thomasâs reasoning in Gamble v. United States, remain stout-hearted, and reject any prior decision that cannot be supported by the common linguistic usage of the founding era â even if that means rejecting Heller? One thing this new research makes abundantly clear: the Second Amendment is in the Courtâs hands. How it developsâfor good or illâwill be a function solely of the wisdom with which the Court articulates its mandates
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
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