LEADER 13565nam 22008775 450 001 996465683203316 005 20230321171936.0 010 $a3-642-25324-5 024 7 $a10.1007/978-3-642-25324-9 035 $a(CKB)3400000000024082 035 $a(SSID)ssj0000609053 035 $a(PQKBManifestationID)11367871 035 $a(PQKBTitleCode)TC0000609053 035 $a(PQKBWorkID)10607800 035 $a(PQKB)10102178 035 $a(DE-He213)978-3-642-25324-9 035 $a(MiAaPQ)EBC6283823 035 $a(MiAaPQ)EBC5585618 035 $a(Au-PeEL)EBL5585618 035 $a(OCoLC)765949002 035 $a(PPN)15751255X 035 $a(EXLCZ)993400000000024082 100 $a20111121d2011 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in Artificial Intelligence$b[electronic resource] $e10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part I /$fedited by Ildar Batyrshin, Grigori Sidorov 205 $a1st ed. 2011. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2011. 215 $a1 online resource (XXV, 596 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v7094 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-25323-7 320 $aIncludes bibliographical references and index. 327 $aIntro -- Title -- Preface -- Organization -- Table of Contents -- Automated Reasoning and Multi-Agent Systems -- Case Studies on Invariant Generation Using a Saturation Theorem Prover -- Introduction -- Preliminaries -- Symbol Elimination and Invariant Generation in Vampire -- Program Analysis in Vampire -- Theory Reasoning in Vampire -- Symbol Elimination in Vampire -- Pruning Generated Invariants -- Proving Invariants, Postconditions, and Assertions -- Experimental Results -- Challenging Benchmarks -- Industrial Examples -- Analysis of Experiments -- Related Work -- Conclusions -- References -- Characterization of Argumentation Semantics in Terms of the MMr Semantics -- Introduction -- Background -- Syntax and Some Operations -- The MMr Semantics -- Argumentation Theory -- Relation between CF2 and MMr -- Preferred Extension and MMr Semantics -- Conclusions -- References -- Learning Probabilistic Description Logics: A Framework and Algorithms -- Introduction -- Basics -- Description Logics -- Probabilistic Description Logics and crALC -- Learning Description Logics -- Learning with the PDL crALC -- The Probabilistic Score Function -- The Algorithm to Learn Probabilistic Terminologies -- Experiments -- Experiments on Description Logic Learning -- Experiments on Learning Probabilistic Terminologies -- Conclusion -- References -- Belief Merging Using Normal Forms -- Introduction -- Preliminaries -- Normal Partial Satisfiability -- Comparing Results -- Postulates -- Algorithm PS-Merge -- Prime Implicant-Based Merging -- Conclusion -- References -- Toward Justifying Actions with Logically and Socially Acceptable Reasons -- Introduction -- Background and Objectives -- Related Work -- Motivating Example -- Logical Preliminaries -- Structuring Arguments and Defeat Functions -- Practical and Theoretical Arguments. 327 $aTheoretical, Preference-Based and Welfare-Based Defeats -- Justifying Logically and Socially Acceptable Reasons -- Analyzing Structured Practical Argumentation Frameworks -- Analyzing Layered Practical Argumentation Frameworks -- Illustrative Example -- Conclusions and Future Work -- References -- A Complex Social System Simulation Using Type-2 Fuzzy Logic and Multiagent System -- Introduction -- Representation of Uncertainty in a MAS -- Simulation of a Social Complex System -- Interactions between Agents -- Use of Type-2 Fuzzy Logic -- Simulation Results -- Conclusions and Future Work -- References -- Computing Mobile Agent Routes with Node-Wise Constraints in Distributed Communication Systems -- Introduction -- Problem Formulation -- Algorithm Description -- Dynamic Programming (DP) Algorithm -- General Description of the FPTAS -- Stage A: Finding Preliminary Lower and Upper Bounds for PN1 -- Stage B: Finding Improved Bounds for PN1 -- Stage C: The \epsilon-Approximation Algorithm -- Discussion and Concluding Remarks -- References -- Collaborative Redundant Agents: Modeling the Dependences in the Diversity of the Agents' Errors -- Introduction -- Related Work -- A Model of Dependences for Collaborative Agents -- Experiments and Analyses -- Conclusions and Future Work -- Modeling and Using Dependences in Trust and Reputation Networks -- References -- Strategy Patterns Prediction Model (SPPM) -- Introduction -- RoboCup 2D Soccer Simulator -- Knowledge Base Creation -- In-Game Features -- Results Obtained -- Conclusions and Further Work -- References -- Fuzzy Case-Based Reasoning for Managing Strategic and Tactical Reasoning in StarCraft -- Introduction -- Related Work -- Strategic Reasoning -- Strategy Selection -- Case Representation for Strategies -- Methodology to Build the Case Base -- Matching Treat -- Tactical Reasoning. 327 $aAbstraction of the Space -- Fuzzy Case Representation -- Fuzzy Sets and Case Base -- Fuzzy Matching Method -- Experiments and Results -- Conclusions and Future Work -- References -- Problem Solving and Machine Learning -- Variable and Value Ordering Decision Matrix Hyper-heuristics: A Local Improvement Approach -- Introduction -- Solution Approach -- Variable and Value Ordering -- CSP Instances and the Problem State Representation -- The Decision Matrix Hyper-heuristic and the Local Improvement Approach -- Experiments and Results -- Experiment I -- Experiment II -- Experiment III -- Conclusions -- References -- Improving the Performance of Heuristic Algorithms Based on Causal Inference -- Introduction -- Learning Explanations through Causal Inference -- Causal Models -- Learning Causal Models from Data -- Causal Analysis of Parameters in WABP Algorithm -- Weight Annealing Algorithm for the Bin Packing Problem -- Experimental Design -- Learning a Causal Model from the Experimental Data -- Conclusions and Future Work -- References -- Fuzzified Tree Search in Real Domain Games -- Introduction -- Current Situation in Game Tree Search -- The Fuzzy Approach -- The Fuzzified Search Algorithm -- Game Setup -- Experimental Results -- Conclusions and Future Work -- References -- On Generating Templates for Hypothesis in Inductive Logic Programming -- Introduction -- Background -- Incremental Template Generation -- History-Driven Tabu Template Generation -- Stochastic Extension of History-Driven Tabu Template Generation -- Experimental Results -- Conclusions -- References -- Towards Building a Masquerade Detection Method Based on User File System Navigation -- Introduction -- Masquerade Detection -- The SEA Dataset -- Masquerade Detection Mechanisms: An Overview -- Navigation Structures -- Access Graph -- Directory Graph -- Some Preliminary Results. 327 $aConstruction- and Validation-Masquerade Datasets -- Experimentation Results -- Discussion -- Conclusions -- References -- A Fast SVM Training Algorithm Based on a Decision Tree Data Filter -- Introduction -- Preliminaries -- Support Vector Machines -- Decision Trees -- Implementing a Data Filter Algorithm -- Algorithm Description -- SVM Training Based on Data Filter -- Experimental Results and Comparison -- Data Sets -- Classification Accuracy and Training Time -- Conclusions and Future Work -- References -- Optimal Shortening of Covering Arrays -- Introduction -- Problem Definition -- Related Work -- The OSCAR Problem Is NP-Complete -- Algorithms to Solve the OSCAR Problem -- Greedy Algorithm to Solve the OSCAR Problem -- Exact Algorithm to Solve OSCAR Problem -- Experimentation and Results -- Conclusions -- References -- An Exact Approach to Maximize the Number of Wild Cards in a Covering Array -- Introduction -- Background -- Exact Approach for Wild Card Detection -- Symbol Fixing Process -- Exact Algorithm for Wild Card Identification: wcBBCA -- Experimental Results -- Experiment 1: Identification of Wild Cards in a CA Benchmark -- Experiment 2: Wild Card Profile Detection for Larger CAs -- Experiment 3: Diversity in the Wild Card Profiles -- Conclusions -- References -- Intelligent Learning System Based on SCORM Learning Objects -- Introduction -- SCORM Sequencing -- Intelligent Tutoring Systems -- Sequencing and Navigation in ITS -- Using Probabilistic Graphical Models for Adaptive Sequence -- Using Decision Networks in the Sequencing and Navigation of Los -- Intelligent Learning System Based on Learning Objects -- General Architecture of the System -- Domain Knowledge Model -- Tutor Module -- Case Study -- Description of the Evaluation Process -- Results and Discussion -- Model Validation in Electrical Sector -- Conclusions -- References. 327 $aNatural Language Processing -- A Weighted Profile Intersection Measure for Profile-Based Authorship Attribution -- Introduction -- Related Work -- Proposed Method -- Experimental Evaluation -- Authorship Attribution Data Sets -- CNG-SPI vs CNG-WPI -- CNG-SPI vs Classification-based methods -- Conclusions -- References -- A New General Grammar Formalism for Parsing -- Introduction -- Grammatical Framework -- ConstrainedW-Grammars -- Probabilistic CW-Grammars -- Learning CW-Grammars from Tree-Banks -- Some Further Technical Notions -- Capturing State of the Art Parsers -- Bilexical Grammars -- Markov Rules -- Stochastic Tree Substitution Grammars -- References -- Contextual Semantic Processing for a Spanish Dialogue System Using Markov Logic -- Introduction -- Dialogue Models -- Semantic Processing -- Word Enhanced Spotting -- Markov Logic -- Corpus -- Experiments -- Exploratory and Baseline Experiments -- Context Experiments -- Results -- Conclusions -- References -- A Statistics-Based Semantic Textual Entailment System -- Introduction -- Related Work -- Universal Networking Language -- System Description -- UNL En-Conversion Module -- Pre-processing Module -- Scoring Module -- Decision Module -- Experimental Results -- Error Analysis -- Conclusions -- References -- Semantic Model for Improving the Performance of Natural Language Interfaces to Databases -- Introduction -- Background -- Problems in Queries -- Semantically Enriched Database Modeling -- Grammatical Descriptors -- Entities -- Attributes -- Relationships -- Specialization and Generalization -- SEM Representation -- Case Study -- Final Remarks and Future Work -- References -- Modular Natural Language Processing Using Declarative Attribute Grammars -- Introduction -- General Notation -- Underlying Foundation -- Grammars, Top-Down Parser and Combinators. 327 $aDeclarative and Executable Attribute Grammars. 330 $aThe two-volume set LNAI 7094 and LNAI 7095 constitutes the refereed proceedings of the 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The 96 revised papers presented were carefully reviewed and selected from numerous submissions. The first volume includes 50 papers representing the current main topics of interest for the AI community and their applications. The papers are organized in the following topical sections: automated reasoning and multi-agent systems; problem solving and machine learning; natural language processing; robotics, planning and scheduling; and medical applications of artificial intelligence. 410 0$aLecture Notes in Artificial Intelligence ;$v7094 606 $aArtificial intelligence 606 $aOptical data processing 606 $aApplication software 606 $aPattern recognition 606 $aData mining 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aApplication software. 615 0$aPattern recognition. 615 0$aData mining. 615 14$aArtificial Intelligence. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aImage Processing and Computer Vision. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aPattern Recognition. 615 24$aData Mining and Knowledge Discovery. 676 $a006.3 702 $aBatyrshin$b Ildar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSidorov$b Grigori$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465683203316 996 $aAdvances in Artificial Intelligence$9772131 997 $aUNISA