Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

Abstract

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

“…This article follows the previous article [1] and the conference article [2]. FVSOOMM is the culmination of many previous works of the authors concerned with temporal modeling [60], ontologies [27,29,50,61], emotion modeling [62,63] and object-oriented models.…”

“…FVSOOMM proposes a new mechanism for analogic reasoning and allows temporal case-based reasoning by computing structural distances on fuzzy vectorial spaces, implemented at each level of a complex object's composition, which is a tree of components. The bottom-up multiple inheritance implementation means that no conflict is represented by the sum of the vectors, which has powerful properties of commutativity, transitivity, symmetry, and a neutral element of a vector space [1]. Moreover, the FVS scalar product allows each attribute and component to be weighted according to its importance, as given by the domain expert, to assess the object and to take appropriate decisions, for example, to perform a diagnosis, predict a suitable treatment, or to assess the prognosis of a clinical state.…”

“…The authors believe that the emotional dimension also has an effect on the decisions of mayors with regard to psychological and emotional factors and the resulting territorial recompositions. The emotion model is the one that is used in medicine and also in management applications with the EPICE model presented in a previous paper [1]. It is based on the OCC model (Ortony, Clore and Collins) [58].…”

“…This requires a temporal comparison of the complex structure's component objects and their states (the value of their attributes), where component objects must be considered at different levels of granularity, according to their composition in the complex structure. This paper uses a fuzzy vector space object-oriented model and Algorithms 2022, 15, 66 2 of 40 method (FVSOOMM), proposed by Colloc [1]. In the ESM'2019 conference paper [2], FVSOOMM was presented and utilized to model the personality, cognitive dissonance, and emotions in a marketing case example.…”

“…respectively the fields of the attributes and objects that are involved in the end-user interface and are respectively linked to the QAD (field idQAD) and to the OTD (field ObjTid) that define their constraints.QADWWeight of the attribute in the object[1,100] Input (Key-entered)QADName Unique descriptive name chosen by the expert Input (Key-entered) QADLastUpdate Date and Hour of Last Update by the Expert Get system date and hour when the last update by the expert QADType Type of value {Boolean, Character, Integer, Float, Double, String, DateHour, Enum} Combo List to choose QADU Unit of the attribute if any (optional) Input (Key-entered) QADVmin Minimum value of the attribute Input (Key-entered) QADVmax Maximum value of the attribute Input (Key-entered) QADConst The value of the attribute is constant Radio Button () Yes () No QADMand The value of the attribute is mandatory (may be 0) Radio Button () Yes () No QADDefault Default value when not provided by the user Input (Key-entered) QADlinlog Linear value or logarithm scale (Log Base ) Input (Key-entered) Default is Linear QADFuzzy [-1,1] Fuzzy characteristic membership function Graph Graphic of the sigmoid function APeriodicity Periodicity update of the attribute specified in time unit: {s,m,h,d,w,m,y} : second, minute, hour, day, week, month, year. When a revaluation will be launched (* if too short cause performance issue) Input (Key-entered) Expert Interface : Fields of the Qualifier Attribute Descriptor (QAD) OTNbQAD Number of Qualifier Attribute Descriptors of the Object Type.…”