A model of three-way decisions for Knowledge Harnessing

Abstract The present work introduces the Knowledge Harnessing, by showing its theoretical foundations as well as a three-way decision model to deal with it. The problem poses how to extract valid information about a specific context from conflicting or uncertain information received by a system (or agent). With this aim, forgetting variable operators are used to both characterize the problem from the logical point of view and provide a theoretical solution as an acceptance-rejection problem. Since the formalization is semantic in nature (it considers the models of the knowledge base that admit the extracted knowledge), general bounds are provided for acceptance-rejection evaluation on boundary region.

[1]  Anna Gomolinska,et al.  Data meaning and knowledge discovery: Semantical aspects of information systems , 2020, Int. J. Approx. Reason..

[2]  Javier Larrosa,et al.  On the Practical use of Variable Elimination in Constraint Optimization Problems: 'Still-life' as a Case Study , 2005, J. Artif. Intell. Res..

[3]  Heiner Stuckenschmidt.  A Semantic Similarity Measure for Ontology-Based Information , 2009, FQAS.

[4]  María-José Hidalgo-Doblado,et al.  A logic-algebraic tool for reasoning with Knowledge-Based Systems , 2018, J. Log. Algebraic Methods Program..

[5]  Nicolas Spyratos,et al.  A unified interaction scheme for information sources , 2006, Journal of Intelligent Information Systems.

[6]  Fan Min,et al.  Cost-sensitive approximate attribute reduction with three-way decisions , 2019, Int. J. Approx. Reason..

[7]  Alessio Ferone,et al.  Feature selection based on composition of rough sets induced by feature granulation , 2018, Int. J. Approx. Reason..

[8]  Binghan Long,et al.  The dynamic update method of attribute-induced three-way granular concept in formal contexts , 2020, Int. J. Approx. Reason..

[9]  Matthias Thimm,et al.  On the complexity of inconsistency measurement , 2019, Artif. Intell..

[10]  Antonio Hernando,et al.  A Gröbner bases-based rule based expert system for fibromyalgia diagnosis , 2012, Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas.

[11]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..

[12]  Abderrahim Ait Wakrime,et al.  On repairing queries in cloud computing , 2016, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

[13]  Sheila A. McIlraith,et al.  Partition-based logical reasoning for first-order and propositional theories , 2005, Artif. Intell..

[14]  Zbigniew W. Ras,et al.  On Reaching Consensus by a Group of Collaborating Agents , 2009, FQAS.

[15]  Deyu Li,et al.  Knowledge structure preserving fuzzy attribute reduction in fuzzy formal context , 2019, Int. J. Approx. Reason..

[16]  Philippe Besnard.  Forgetting-Based Inconsistency Measure , 2016, SUM.

[17]  Joaquín Borrego-Díaz,et al.  Toward Semantic Mobile Web 2.0 through Multiagent Systems , 2009, KES-AMSTA.

[18]  Nils J. Nilsson,et al.  Teleo-Reactive Programs and the Triple-Tower Architecture , 2001, Electron. Trans. Artif. Intell..

[19]  Tong-Jun Li,et al.  Decision Making in Incomplete Information System Based on Decision-Theoretic Rough Sets , 2011, RSKT.

[20]  Huilai Zhi,et al.  Three-way dual concept analysis , 2019, Int. J. Approx. Reason..

[21]  Jianfeng Xu,et al.  A three-way selective ensemble model for multi-label classification , 2018, Int. J. Approx. Reason..

[22]  Joaquín Borrego-Díaz,et al.  Agent-mediated shared conceptualizations in tagging services , 2012, Multimedia Tools and Applications.

[23]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[24]  Yan Zhang,et al.  Variable Forgetting in Reasoning about Knowledge , 2009, J. Artif. Intell. Res..

[25]  Yves Moinard,et al.  Forgetting Literals with Varying Propositional Symbols , 2007, J. Log. Comput..

[26]  Yan Zhang,et al.  Knowledge forgetting: Properties and applications , 2009, Artif. Intell..

[27]  Jingtao Yao,et al.  Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets , 2012, Fundam. Informaticae.

[28]  Katsumi Inoue,et al.  Generalizing Conjunctive Queries for Informative Answers , 2011, FQAS.

[29]  Tao Zhang,et al.  Incremental concept-cognitive learning based on attribute topology , 2020, Int. J. Approx. Reason..

[30]  Li Chen,et al.  User-Involved Preference Elicitation for Product Search and Recommender Systems , 2008, AI Mag..

[31]  Philippe Chatalic,et al.  Reasoning with Inconsistencies in Propositional Peer-to-Peer Inference Systems , 2006, ECAI.

[32]  Weihua Xu,et al.  Generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system , 2019, Int. J. Approx. Reason..

[33]  Christine Froidevaux,et al.  Repairing Queries in a Mediator Approach , 2000, ECAI.

[34]  Nouman Azam,et al.  A three-way clustering approach for handling missing data using GTRS , 2018, Int. J. Approx. Reason..

[35]  Joaquín Borrego-Díaz,et al.  Conceptual-based Reasoning in Mobile Web 2.0 by Means Multiagent Systems - Knowledge Engineering Notes , 2012, ICAART.

[36]  Jiaqi Wang,et al.  A cost-sensitive three-way combination technique for ensemble learning in sentiment classification , 2019, Int. J. Approx. Reason..

[37]  Michael R. Genesereth,et al.  Logical foundations of artificial intelligence , 1987 .

[38]  Yiyu Yao,et al.  Three-way decision and granular computing , 2018, Int. J. Approx. Reason..

[39]  Badran Raddaoui,et al.  Handling Disagreement in Ontologies-Based Reasoning via Argumentation , 2019, WISE.

[40]  Giampiero Chiaselotti,et al.  Notions from rough set theory in a generalized dependency relation context , 2018, Int. J. Approx. Reason..

[41]  Nils J. Nilsson,et al.  Teleo-Reactive Programs for Agent Control , 1993, J. Artif. Intell. Res..

[42]  François Goasdoué,et al.  Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases , 2019, J. Artif. Intell. Res..

[43]  Qing Wan,et al.  Optimal granule level selection: A granule description accuracy viewpoint , 2020, Int. J. Approx. Reason..

[44]  Alexander Felfernig,et al.  ReAction: Personalized Minimal Repair Adaptations for Customer Requests , 2011, FQAS.

[45]  Yuhua Qian,et al.  Accelerator for supervised neighborhood based attribute reduction , 2020, Int. J. Approx. Reason..

[46]  Joaquín Borrego-Díaz,et al.  On Experimental Efficiency for Retraction Operator to Stem Basis , 2019 .

[47]  Jinkun Chen,et al.  A fast attribute reduction method for large formal decision contexts , 2019, Int. J. Approx. Reason..

[48]  Hector J. Levesque,et al.  GOLOG: A Logic Programming Language for Dynamic Domains , 1997, J. Log. Program..

[49]  Hector J. Levesque,et al.  IndiGolog: A High-Level Programming Language for Embedded Reasoning Agents , 2009, Multi-Agent Programming, Languages, Tools and Applications.

[50]  W. W. Bledsoe,et al.  Variable Elimination and Chaining in a Resolution-based Prover for Inequalities , 1980, CADE.

[51]  Usman Qamar,et al.  Feature selection using rough set-based direct dependency calculation by avoiding the positive region , 2018, Int. J. Approx. Reason..

[52]  René Witte,et al.  Flexible Ontology Population from Text: The OwlExporter , 2010, LREC.

[53]  Kewen Wang,et al.  Semantic forgetting in answer set programming , 2008, Artif. Intell..

[54]  Leopoldo E. Bertossi,et al.  Consistent query answering in databases , 2006, SGMD.

[55]  Baoli Wang,et al.  Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions , 2020, Int. J. Approx. Reason..

[56]  Andrzej Skowron,et al.  Local rough set: A solution to rough data analysis in big data , 2018, Int. J. Approx. Reason..

[57]  Shu Zhao,et al.  A three-way decision ensemble method for imbalanced data oversampling , 2019, Int. J. Approx. Reason..

[58]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[59]  PETER GÄRDENFORS,et al.  Belief Revision: Belief revision: An introduction , 2003 .

[60]  Xiaonan Li,et al.  Three-way decisions approach to multiple attribute group decision making with linguistic information-based decision-theoretic rough fuzzy set , 2018, Int. J. Approx. Reason..

[61]  Xin Yang,et al.  A sequential three-way approach to multi-class decision , 2019, Int. J. Approx. Reason..

[62]  V. S. Subrahmanian,et al.  A General Framework for Reasoning On Inconsistency , 2013, SpringerBriefs in Computer Science.

[63]  Fan Min,et al.  Three-way decisions based feature fusion for Chinese irony detection , 2019, Int. J. Approx. Reason..

[64]  Yiyu Yao,et al.  Three-Way Decisions and Cognitive Computing , 2016, Cognitive Computation.

[65]  Antonio Hernando,et al.  An algebraic model for implementing expert systems based on the knowledge of different experts , 2015, Math. Comput. Simul..

[66]  Yiyu Yao,et al.  Three-way granular computing, rough sets, and formal concept analysis , 2020, Int. J. Approx. Reason..