Weighted Compensative Logic With Adjustable Threshold Andness and Orness

The threshold andness is a border between soft and hard partial conjunction (similarly, the threshold orness is a border between soft and hard partial disjunction). This paper presents a weighted compensative logic based on aggregators that use adjustable threshold andness and orness. The adjustability of threshold andness/orness is a new degree of freedom in weighted logics, which is suitable for increasing the precision of evaluation criteria developed by evaluation experts. We investigate the distribution of threshold andness and present a new form of interpolative aggregators that provide adjustable threshold andness and orness. Our goal is to use a new verbalized approach, based on decomposing the perception of overall importance, for designing the generalized conjunction/disjunction (GCD) aggregators. The verbalized approach helps specify parameters that affect both the formal logic properties and the semantic properties of GCD, facilitating the use of soft computing evaluation logic and corresponding evaluation methods.

[1]  Jozo J. Dujmovic,et al.  LSP method and its use for evaluation of Java IDEs , 2006, Int. J. Approx. Reason..

[2]  P. Bullen Handbook of means and their inequalities , 1987 .

[3]  I. Levin BASIC CONCEPTS OF CONTINUOUS LOGIC , 2007 .

[4]  Marc Roubens,et al.  Fuzzy Preference Modelling and Multicriteria Decision Support , 1994, Theory and Decision Library.

[5]  Jozo J. Dujmovic,et al.  Continuous Preference Logic for System Evaluation , 2007, IEEE Transactions on Fuzzy Systems.

[6]  Henrik Legind Larsen,et al.  Importance weighting and andness control in De Morgan dual power means and OWA operators , 2012, Fuzzy Sets Syst..

[7]  Franco P. Preparata,et al.  Continuously Valued Logic , 1972, J. Comput. Syst. Sci..

[8]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[9]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[11]  Henrik Legind Larsen,et al.  Generalized conjunction/disjunction , 2007, Int. J. Approx. Reason..

[12]  Ronald R. Yager Weighted triangular norms using generating functions , 2004 .

[13]  Emin,et al.  Extended Continuous Logic and the Theory of Complex Criteria * , 2005 .

[14]  George J. Klir,et al.  Fuzzy sets and fuzzy logic , 1995 .

[15]  James Rumrill Miller The assessment of worth: a systematic procedure and its experimental validation. , 1966 .

[16]  Jozo J. Dujmovic Characteristic forms of generalized conjunction/disjunction , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[17]  Antoon Bronselaer,et al.  Enhancing Flexible Querying Using Criterion Trees , 2013, FQAS.

[18]  Jozo J. Dujmovic Aggregation Operators and Observable Properties of Human Reasoning , 2013, AGOP.

[19]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[20]  Theodor J. Stewart,et al.  Multiple criteria decision analysis - an integrated approach , 2001 .

[21]  Henrik Legind Larsen,et al.  Efficient Andness-Directed Importance Weighted Averaging Operators , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[22]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[23]  Mariano Eriz Aggregation Functions: A Guide for Practitioners , 2010 .

[24]  R. Mesiar,et al.  ”Aggregation Functions”, Cambridge University Press , 2008, 2008 6th International Symposium on Intelligent Systems and Informatics.

[25]  Guy De Tré,et al.  Soft Computing Models in Online Real Estate , 2013, WCSC.

[26]  Jozo J. Dujmovic,et al.  An Empirical Analysis of Assessment Errors for Weights and Andness in LSP Criteria , 2004, MDAI.

[27]  Ronald R. Yager,et al.  Generalized OWA Aggregation Operators , 2004, Fuzzy Optim. Decis. Mak..

[28]  Ronald R. Yager,et al.  Computing with Words Using Weighted Power Mean Aggregation Operators , 2013, Soft Computing: State of the Art Theory and Novel Applications.

[29]  Piero Risoluti Fuzzy Sets, Decision Making, and Expert Systems , 2004 .

[30]  Theodor J. Stewart,et al.  Multiple Criteria Decision Analysis , 2001 .

[31]  H. Larsen,et al.  Importance weighted OWA aggregation of multicriteria queries , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[32]  Siegfried Gottwald,et al.  Fuzzy Sets and Fuzzy Logic , 1993 .

[33]  Ralph L. Keeney,et al.  Decisions with multiple objectives: preferences and value tradeoffs , 1976 .

[34]  Jozo Dujmović,et al.  Evaluation of Disease Severity and Patient Disability Using the LSP Method , 2008 .

[35]  Jozo J. Dujmovic Nine forms of andness/orness , 2006, Computational Intelligence.

[36]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[37]  Guy De Tré,et al.  Multicriteria methods and logic aggregation in suitability maps , 2011, Int. J. Intell. Syst..

[38]  Antoon Bronselaer,et al.  On the Applicability of Multi-criteria Decision Making Techniques in Fuzzy Querying , 2012, IPMU.