BibSonomyburst/tag/solvingBibSonomy RSS feed for /tag/solving2012-02-15T06:28:18+01:00http://www.bibsonomy.org/bibtex/2353bcdd1e09d48cc89bc265f0ea92eee/lwa_2011lwa_20112012-01-11T13:01:15+01:002011 LWA LWA_2011 filter kalman lifted linear pagerank program solving towards <span class="authorEditorList"><a href="/author/Ahmadi">Babak Ahmadi</a>, <a href="/author/Mladenov">Martin Mladenov</a>, <a href="/author/Kersting">Kristian Kersting</a>, and <a href="/author/Sanner">Scott Sanner</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 11 13:01:15 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationOn Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving20112011 LWA LWA_2011 filter kalman lifted linear pagerank program solving towards http://www.bibsonomy.org/bibtex/23bb460409ac5a54b00cf43ddbd7ecd5c/lwa_2011lwa_20112012-01-11T13:01:15+01:002011 LWA LWA_2011 analytics applications challenges problems solving visual <span class="authorEditorList"><a href="/author/Keim">Daniel Keim</a> </span><em>Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation, </em>(<em>2011</em>)Wed Jan 11 13:01:15 CET 2012Working Notes of the LWA 2011 - Learning, Knowledge, AdaptationSolving Problems with Visual Analytics: Challenges and Applications20112011 LWA LWA_2011 analytics applications challenges problems solving visual http://www.bibsonomy.org/bibtex/241d013af4ea6644fcf2ddcdcc4f60690/georges_pgeorges_p2011-12-22T11:51:38+01:00Basic Computer Conceptual Dialectics; Evolutionary Innovation Invention; Inventive Mathematical Patents Problem Standard TRIZ TRIZ; Trial aided algorithms; analysis; and error; frameworks; idea; inventions, operators; principles, problems; procedures; process; solving <span class="authorEditorList"><a href="/author/D.">Duran-Novoa R. Leon-Rovira N. Aguayo-Tellez H. Said D.</a> </span><em>Computers in Industry</em> <em>62(4):437-445</em> (<em>2011</em>)<em>cited By since 1996 0 . </em>Thu Dec 22 11:51:38 CET 2011Computers in Industrycited By (since 1996) 04437-445Inventive problem solving based on dialectical negation, using evolutionary algorithms and TRIZ heuristics622011Basic Computer Conceptual Dialectics; Evolutionary Innovation Invention; Inventive Mathematical Patents Problem Standard TRIZ TRIZ; Trial aided algorithms; analysis; and error; frameworks; idea; inventions, operators; principles, problems; procedures; process; solving The ability to solve inventive problems is at the core of the innovation process; however, the standard procedure to deal with them is to utilize random trial and error, despite the existence of several theories and methods. TRIZ and evolutionary algorithms (EA) have shown results that support the idea that inventiveness can be understood and developed systematically. This article presents a strategy based on dialectical negation in which both approaches converge, creating a new conceptual framework for enhancing computer-aided problem solving. Two basic ideas presented are the inversion of the traditional EA selection ("survival of the fittest"), and the incorporation of new dialectical negation operators in evolutionary algorithms based on TRIZ principles. Two case studies are the starting point to discuss what kind of results can be expected using this "Dialectical Negation Algorithm" (DNA). Β© 2010 Elsevier B.V. All rights reserved.http://www.bibsonomy.org/bibtex/20fb9eaffae73aedcd98193fca6c7d9ae/georges_pgeorges_p2011-12-22T11:51:38+01:00Abstraction Design Design; Formalisation; Knowledge Level Matching Natural Ontology, Problem TRIZ; Whole based body, detail; languages; level; matching; of ontology problems; process; solving sources; systems; <span class="authorEditorList"><a href="/author/F.b">Yan W.a Zanni-Merk C.b Rousselot F.b</a> </span><em>Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics</em> <em>6884 LNAI(PART 4):445-454</em> (<em>2011</em>)<em>cited By since 1996 0; Conference of 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011; Confer<span class="info">...<div>cited By since 1996 0; Conference of 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011; Conference Date: 12 September 2011 through 14 September 2011; Conference Code: 86591</div></span> . </em>Thu Dec 22 11:51:38 CET 2011KaiserslauternLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)cited By (since 1996) 0; Conference of 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2011; Conference Date: 12 September 2011 through 14 September 2011; Conference Code: 86591PART 4445-454Matching of different abstraction level knowledge sources: The case of inventive design6884 LNAI2011Abstraction Design Design; Formalisation; Knowledge Level Matching Natural Ontology, Problem TRIZ; Whole based body, detail; languages; level; matching; of ontology problems; process; solving sources; systems; The formalization of the inventive design process has become an important problem to be solved. Formalizing the inventive design process based on ontologies is a good approach, and using the matching approaches available to solve the specific matching problems in inventive design ontologies is worth to be examined. In fact, there are different knowledge sources used for solving inventive design problems. These sources are about similar notions, but the level of detail of their description is very different. We are interested in the formalisation of these knowledge sources (for the moment described in natural language) and to make the whole body complete and coherent. It is needed, therefore, to make the links among them explicit. It is a typical problem of ontology matching. This article presents a survey of the possible ontology matching approaches for the specific characteristics of this problem. Β© 2011 Springer-Verlag.http://www.bibsonomy.org/bibtex/23f7d98c2e51f544ed27154ab70df6b46/georges_pgeorges_p2011-12-20T14:36:06+01:00Inventive Pipelines, Problem Structured Technical Theory inventive obstacles; of problem problems; solving solving, <span class="authorEditorList"><a href="/author/Runhua">T. Runhua</a> </span><em>Computers in Industry</em> <em>62(4):414-422</em> (<em>2011</em>)<em>cited By since 1996 0 . </em>Tue Dec 20 14:36:06 CET 2011Computers in Industrycited By (since 1996) 04414-422Eliminating technical obstacles in innovation pipelines using CAIs622011Inventive Pipelines, Problem Structured Technical Theory inventive obstacles; of problem problems; solving solving, Ill-structured problems are difficult types to solve. When this type of problem is faced in an innovation pipeline, a technical obstacle emerges. Comparison between the definitions of ill-structured and inventive problems in theory of inventive problem solving (TRIZ) shows that the latter is a sub-set of the former in engineering. As a result, computer-aided innovation (CAI) systems (CAIs) based on TRIZ can be applied to solve some ill-structured problems that appear in an innovation pipeline. A model including two technical obstacles is developed for an innovation pipeline, and a case study is carried out to show how to eliminate the technical obstacles using the model. Β© 2010 Elsevier B.V. All rights reserved.http://www.bibsonomy.org/bibtex/2bd7f613219d32091b0fe5a5507f12722/enricostanoenricostano2011-10-25T18:10:28+02:00autism children enhancing problem social solving <span class="authorEditorList"><a href="/author/Bernard-Opitz">Vera Bernard-Opitz</a>, <a href="/author/Sriram">N. Sriram</a>, and <a href="/author/Nakhoda-Sapuan">Sharul Nakhoda-Sapuan</a> </span><em>Journal of Autism and Developmental Disorders</em> (<em>2001</em>)<em>10.1023/A:1010660502130 . </em>Tue Oct 25 18:10:28 CEST 2011Journal of Autism and Developmental Disorders10.1023/A:1010660502130377-384Enhancing Social Problem Solving in Children with Autism and Normal Children Through Computer-Assisted Instruction312001autism children enhancing problem social solving Children with autism have difficulty in solving social problems and in generating multiple solutions to problems. They are, however, relatively skilled in responding to visual cues such as pictures and animations. Eight distinct social problems were presented on a computer, along with a choice of possible solutions, and an option to produce alternative solutions. Eight preschool children with autism and eight matched normal children went through 10 training sessions interleaved with 6 probe sessions. Children were asked to provide solutions to animated problem scenes in all the sessions. Unlike the probe sessions, in the training sessions problem solutions were first explained thoroughly by the trainer. Subsequently these explanations were illustrated using dynamic animations of the solutions. Although children with autism produced significantly fewer alternative solutions compared to their normal peers, a steady increase across probe sessions was observed for the autistic group. The frequency of new ideas was directly predicted by the diagnostic category of autism. Results suggest young children with autism and their normal peers can be taught problem-solving strategies with the aid of computer interfaces. More research is required to establish whether such computer-assisted instruction will generalize to nontrained problem situations in real-life contexts.http://www.bibsonomy.org/bibtex/26d4b047fbf6ddf90b97db77a0115ac75/tonitoni2010-11-12T10:45:16+01:00constraint geometric myown pt root selection solving <span class="authorEditorList"><a href="/author/Joan-Arinyo">Robert Joan-Arinyo</a>, <a href="/author/Luzón">M. V. Luzón</a>, and <a href="/author/Soto-Riera">Antoni Soto-Riera</a> </span><em>PPSN, </em><em>volume 2439 of Lecture Notes in Computer Science, </em><em>page 759-768. </em><em>Springer, </em>(<em>2002</em>)Fri Nov 12 10:45:16 CET 2010PPSNDBLP:conf/ppsn/2002759-768Lecture Notes in Computer ScienceConstructive Geometric Constraint Solving: A New Application of Genetic Algorithms24392002constraint geometric myown pt root selection solving http://www.bibsonomy.org/bibtex/28ce3aacf6847d584228d3bb98d78c3fc/tonitoni2010-11-12T10:34:09+01:002d arithmetic constraint geometric interval myown solving <span class="authorEditorList"><a href="/author/Joan-Arinyo">Robert Joan-Arinyo</a>, <a href="/author/Mata">Nuria Mata</a>, and <a href="/author/Soto-Riera">Antoni Soto-Riera</a> </span><em>J. Comput. Inf. Sci. Eng.</em> <em>1(4):341-346</em> (<em>2001</em>)Fri Nov 12 10:34:09 CET 2010J. Comput. Inf. Sci. Eng.4341-346A Constraint Solving-Based Approach to Analyze 2D Geometric Problems With Interval Parameters.120012d arithmetic constraint geometric interval myown solving dblphttp://www.bibsonomy.org/bibtex/2c195a51e1a952be1f7abbf38d75f6e2a/paulotrezentospaulotrezentos2010-09-26T11:46:55+02:00PBO dependency linux mancoosi solving <span class="authorEditorList"><a href="/author/Trezentos">Paulo Trezentos</a>, <a href="/author/Lynce">Inês Lynce</a>, and <a href="/author/Oliveira">Arlindo L. Oliveira</a> </span><em>ASE &#039;10: Proceedings of the IEEE/ACM international conference on Automated software engineering, </em><em>page 427--436. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Sun Sep 26 11:46:55 CEST 2010New York, NY, USAASE '10: Proceedings of the IEEE/ACM international conference on Automated software engineering427--436Apt-pbo: solving the software dependency problem using pseudo-boolean optimization2010PBO dependency linux mancoosi solving http://www.bibsonomy.org/bibtex/2258eb88f201dca7a07a853f8cd256955/yishyish2010-01-15T15:45:41+01:00problem schank schemas solving <span class="authorEditorList"><a href="/author/Marshall">Sandra P. Marshall</a> </span><em>Cambridge Univ Press, </em>(<em>1995</em>)Fri Jan 15 15:45:41 CET 2010Schemas in problem solving1995problem schank schemas solving http://www.bibsonomy.org/bibtex/2d457e9827b32afdc126e77bcfd9c05a6/tonitoni2009-11-27T11:23:14+01:00constraint geometric solving <span class="authorEditorList"><a href="/author/der Meiden">H.A. Van der Meiden</a> </span><em>Delft University of Technology, </em>(<em>2008</em>)Fri Nov 27 11:23:14 CET 2009Semantics of families of objects2008constraint geometric solving In current Computer-Aided Design (CAD) systems for mechanical and industrial design, a model is built from parametric features. Such a parametric model can be interpreted as representing a family of objects, where the members of the family are all possible objects that can be obtained by varying the parameters. A model of a family of objects is useful for manufacturing series of products, for customisation of products, and for reuse of part models. However, there are several major obstacles when using current CAD systems for modelling families. Firstly, the semantics of features and families cannot be adequately specified in and maintained by current CAD systems. As a result, a modelled family may contain undesirable members or may not contain all desirable members. Therefore, we present a new Declarative Family of Objects Model, in which semantics are specified by geometric and topological constraints. Members of the model are determined by solving the those constraints, for which we have developed new methods. Secondly, current CAD systems do not provide sufficient tools for creating and using families of objects. We present a method to determine the range of allowable values for any parameter of a model, which can help users with instantiating family members. We also present a method to determine the critical parameter values for which topological changes occur in the model. These critical values can help users inspect the semantics of families.http://www.bibsonomy.org/bibtex/26c35f7536794672441badf325e752073/achimpassenachimpassen2009-11-23T13:25:47+01:00algorithms, auctions, choice, communication, competition, cooperation, design, distributed game learning, logic, mechanism problem social solving theory, <span class="authorEditorList"><a href="/author/Shoham">Yoav Shoham</a>, and <a href="/author/Leyton-Brown">Kevin Leyton-Brown</a> </span> (<em>February 2009</em>)Mon Nov 23 13:25:47 CET 2009Feb1--532Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations2009algorithms, auctions, choice, communication, competition, cooperation, design, distributed game learning, logic, mechanism problem social solving theory, Multiagent systems consist of multiple autonomous entities having different information and/or diverging interests. This comprehensive introduction to the field offers a computer science perspective, but also draws on ideas from game theory, economics, operations research, logic, philosophy and linguistics. It will serve as a reference for researchers in each of these fields, and be used as a text for advanced undergraduate and graduate courses. Emphasizing foundations, the authors offer a broad and rigorous treatment of their subject, with thorough presentations of distributed problem solving, non-cooperative game theory, multiagent communication and learning, social choice, mechanism design, auctions, coalitional game theory, and logical theories of knowledge, belief, and other aspects of rational agency. For each topic, basic concepts are introduced, examples are given, proofs of key results are offered, and algorithmic considerations are examined. An appendix covers background material in probability theory, classical logic, Markov decision processes, and mathematical programming.http://www.bibsonomy.org/bibtex/2eb97c96b80eab2ed79c5ed5990efc76f/tonitoni2009-10-08T12:13:17+02:00constraint geometric interrogation solving <span class="authorEditorList"><a href="/author/Michelucci">Dominique Michelucci</a>, and <a href="/author/Foufou">Sebti Foufou</a> </span><em>SPM &#039;09: 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling, </em><em>page 343--348. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Thu Oct 08 12:13:17 CEST 2009New York, NY, USASPM '09: 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling343--348Interrogating witnesses for geometric constraint solving2009constraint geometric interrogation solving Classically, geometric constraint solvers use graph-based methods to analyze systems of geometric constraints. These methods have intrinsic limitations, which the witness method overcomes. This paper details the computation of a basis of the vector space of the free infinitesimal motions of a typical witness, and explains how to use this basis to interrogate the witness for detecting all dependencies between constraints: structural dependencies already detectable by graph-based methods, and also non-structural dependencies, due to known or unknown geometric theorems, which are undetectable with graph-based methods. The paper also discusses how to decide about the rigidity of a witness.SM: SPM '09, Interrogating witnesses for geometric ...http://www.bibsonomy.org/bibtex/2d5cd36ae34638a2fbd9df2a6f616a522/mandelmandel2009-06-29T18:33:40+02:00control dx&#039;08 monitoring pompds solving <span class="authorEditorList"><a href="/author/Benazera">Emmanuel Benazera</a>, and <a href="/author/Chanthery">Elodie Chanthery</a> </span><em>Procedings of the 19th International Workshop on Principles of Diagnosis DX&#039;08, </em><em>page 15-22. </em><em>Blue Mountains, NSW, Australia, </em>(<em>September 2008</em>)Mon Jun 29 18:33:40 CEST 2009Blue Mountains, NSW, AustraliaProcedings of the 19th International Workshop on Principles of Diagnosis (DX'08)September15-22The Challenge of Solving POMPDs for Control, Monitoring and Repair of Complex Systems2008control dx'08 monitoring pompds solving http://www.bibsonomy.org/bibtex/29b7452915df51c8637704406f08d7e3e/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00(Psychology); Adult; Attention; Concept Decision Female; Formation; Generalization Humans; Making; Male; Mental Practice Problem Recall; Solving Theory; <span class="authorEditorList"><a href="/author/Neal">A. Neal</a>, <a href="/author/Hesketh">B. Hesketh</a>, and <a href="/author/Andrews">S. Andrews</a> </span><em>Mem Cognit</em> <em>23(2):227--242</em> (<em>March 1995</em>)Tue Sep 16 23:39:07 CEST 2008Mem CognitMar2227--242Instance-based categorization: automatic versus intentional forms of retrieval.231995(Psychology); Adult; Attention; Concept Decision Female; Formation; Generalization Humans; Making; Male; Mental Practice Problem Recall; Solving Theory; CCNLab BibTeXhttp://www.bibsonomy.org/bibtex/28bd002fb1069e26a614d67a82bb4386a/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00cogn, problem solving <span class="authorEditorList"><a href="/author/Larkin">Jill H. Larkin</a> </span><em>Complex Information Processing, </em><em>chapter 3, </em><em>Lawrence Erlbaum Associates, </em><em>Cambridge, MA, </em>(<em>1989</em>)Tue Sep 16 23:39:07 CEST 2008Cambridge, MAComplex Information Processing369-108Display Based Problem Solving1989cogn, problem solving CCNLab BibTeXhttp://www.bibsonomy.org/bibtex/22ce291271ccef91d0a2af6fc914b06a3/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00cogn, problem solving <span class="authorEditorList"><a href="/author/Kotovsky">K. Kotovsky</a>, <a href="/author/Hayes">J. R. Hayes</a>, and <a href="/author/Simon">H. A. Simon</a> </span><em>Cognitive Psychology</em> (<em>1985</em>)Tue Sep 16 23:39:07 CEST 2008Cognitive Psychology248-294Why are some problems hard? Evidence from Tower of Hanoi.171985cogn, problem solving CCNLab BibTeXhttp://www.bibsonomy.org/bibtex/2562e309a4c7f02b704f37eb2a8ab1406/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00analogy, cogn, problem solving <span class="authorEditorList"><a href="/author/Kotovsky">Kenneth Kotovsky</a>, and <a href="/author/Fallside">David Fallside</a> </span><em>Complex Information Processing, </em><em>chapter 3, </em><em>Lawrence Erlbaum Associates, </em><em>Cambridge, MA, </em>(<em>1989</em>)Tue Sep 16 23:39:07 CEST 2008Cambridge, MAComplex Information Processing369-108Representation and Transfer in Problem Solving1989analogy, cogn, problem solving CCNLab BibTeXhttp://www.bibsonomy.org/bibtex/21abfc99da41b789f6120cf411d0f4756/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00cogn, problem solving <span class="authorEditorList"><a href="/author/Karat">John Karat</a> </span><em>Cognitive Psychology</em> (<em>1982</em>)Tue Sep 16 23:39:07 CEST 2008Cognitive Psychology538-559A model of problem solving with incomplete constraint knowledge.141982cogn, problem solving CCNLab BibTeXhttp://www.bibsonomy.org/bibtex/2e10e17c54a79a06dc2b9895fadd1a121/brian.mingusbrian.mingus2008-09-16T23:39:07+02:00nnets, pfc, problem solving <span class="authorEditorList"><a href="/author/Cardoso"> Cardoso</a>, and <a href="/author/Parks">R. W. Parks</a> </span><em>Fundamentals of Neural Network Modeling, </em><em>chapter 9, </em><em>MIT Press, </em><em>Cambridge, MA, </em>(<em>1998</em>)Tue Sep 16 23:39:07 CEST 2008Cambridge, MAFundamentals of Neural Network Modeling9209-231Neural Network Modeling of Executive Functioning with Tower of Hanoi Test in Frontal Lobe-Lesioned Patients1998nnets, pfc, problem solving CCNLab BibTeX