The mathematical modelling and representation of Tanino’s multiplicative transitivity property to the case of intuitionistic reciprocal preference relations (IRPRs) is derived via Zadeh’s extension principle and the representation theorem of fuzzy sets. This result guarantees the correct generalisation of the multiplicative transitivity property of reciprocal preference relations (RPRs), and it allows the multiplicative consistency (MC) property of IRPRs to be dened. The MC property used in decision making problems is threefold: (1) to develop a consistency based procedure to estimate missing values in IRPRs using an indirect chain of alternatives; (2) to quantify the consistency index (CI) of preferences provided by experts; and (3) to build a novel consistency based induced ordered weighted averaging (MC-IOWA) operator that associates a higher contribution in the aggregated value to the more consistent information. These three uses are implemented in developing a consensus model for GDM problems with incomplete IRPRs in which the level of agreement between the experts’ individual IRPRs and the collective IRPR, which is referred here as the proximity index (PI), is combined with the CI to design a feedback mechanism to support experts to change some of their preference values using simple advice rules that aim at increasing the level of agreement while, at the same time, keeping a high degree of consistency. In the presence of missing information, the feedback mechanism implements the consistency based procedure to produce appropriate estimate values of the missing ones based on the given information provided by the experts. Under the assumption of constant CI values, the feedback mechanism is proved to converge to unanimous consensus when all experts are provided with recommendations and these are fully implemented. Additionally, visual representation of experts’ consensus position within the group before and after implementing their feedback advice is also provided, which help an expert to revisit his evaluations and make changes if considered appropriate to achieve a higher consensus level. Finally, an IRPR fuzzy majority based quantier-guided nondominance degree based prioritisation method using the associated score reciprocal preference relation is proposed to obtain the nal solution of consensus.

http://www.cse.dmu.ac.uk/~chiclana/publications/KNOSYS-D-14-00278R2-Paco2.pdf

Multiplicative consistency of intuitionistic reciprocal preference relations and its application to missing values estimation and consensus building

Understanding the impact of personality traits on mobile app adoption - Insights from a large-scale field study

Intuitionistic preference relations constitute a flexible and simple representation format of experts' preference on a set of alternative options, while at the same time allowing to accommodate degrees of hesitation inherent to all decision making processes. In comparison with fuzzy preference relations, the use of intuitionistic fuzzy preference relations in decision making is limited, which is mainly due to the computational complexity associated to using membership degree, non-membership degree and hesitation degree to model experts' subjective preferences. In this paper, the set of reciprocal intuitionistic fuzzy preference relations and the set of asymmetric fuzzy preference relations are proved to be mathematically isomorphic. This result can be exploited to use methodologies developed for fuzzy preference relations to the case of intuitionistic fuzzy preference relations and, ultimately, to overcome the computation complexity mentioned above and to extend the use of reciprocal intuitionistic fuzzy preference relations in decision making. In particular, in this paper, this isomorphic equivalence is used to address the presence of incomplete reciprocal intuitionistic fuzzy preference relations in decision making by developing a consistency driven estimation procedure via the corresponding equivalent incomplete asymmetric fuzzy preference relation procedure. Additionally, the hesitancy degree of the reciprocal intuitionistic fuzzy preference relation is used to introduce the concept of expert's confidence from which a group decision making procedure, based on a new aggregation operator that takes into account not only the experts' consistency but also their confidence degree towards the opinion provided, is proposed.

Confidence-consistency driven group decision making approach with incomplete reciprocal intuitionistic preference relations

Recently, ontology learning is emerging as a new hotspot of research in computer science. In this paper the issue of ontology learning is divided into nine sub-issues according to the structured degree (structured, semi-structured, non-structured) of source data and learning objects (concept, relation, axiom) of ontology. The characteristics, major approaches and the latest research progress of the nine sub-issues are summarized. Based on the analysis framework proposed in the paper, existing ontology learning tools are introduced and compared. The problems of current research are discussed, and finally the future directions are pointed out.

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A Survey on Ontology Learning Research

The main research question within this thesis is introduced: how can an intelligent agent-based system be designed, that gives support to teams, based on their social interactions and internal processes? The contribution of this thesis is the provision and extension of knowledge on how to analyse and design new, innovative computational models for human processes integrating social and individual internal functioning and involving both cognitive and affective elements. This research is innovative, in that it models and validates agent-based models that integrate internal and social dynamics plus affective and cognitive aspects. Usually, computational models do not focus on integrating all of these four aspects. The multiple research objectives are explained, as well as the methodology used to analyse, design and evaluate the agent-based models in this book. Moreover, the contents of each of the six parts of this thesis are outlined.

Social Agents: Agent-Based Modelling of Integrated Internal and Social Dynamics of Cognitive and Affective Processes

Empirical Based Techniques for Human Cognitive Interaction Analysis: Universal Template Design.

This paper introduces a concept of invariant to estimate the mental states of a person who is interacting with the artifact that has a cloned mentality of the particular person. The invariant in the paper is movements of the facial features that are always observed in the developmental process of the facial expressions expressing the identical mental states. The invariant acquired as the result of cloning the way to judge the mental state of the others by the target of cloning. The interpretation of the facial expression in the paper is done in a subjective manner. An extraction method of the invariants and the mental state estimation with the invariants are described in detail. A brief preliminary experiment shows that the proposed method has a possibility to clone the way by the target person to estimate the mental state from the facial expressions with the invariants.

Facial Expression Invariants for Estimating Mental States of Person

In this paper, we describe how to exploit a machine-readable dictionary (MRD) and domain-specific text corpus in supporting the construction of domain ontologies that specify taxonomic and non-taxonomic relationships among given domain concepts. A) In building taxonomic relationships (hierarchically structure) of domain concepts, some hierarchically structure can be extracted from a MRD with marked sub-trees that may be modified by a domain expert, using matching result analysis and trimmed result analysis. B) In building non-taxonomic relationships (specification templates) of domain concepts, we construct concept specification templates that come from pairs of concepts extracted from text corpus, using WordSpace and an association rule algorithm. A domain expert modifies taxonomic and non-taxonomic relationships later. Through the case study with CISG, we make sure that our system can work to support the process of constructing domain ontologies with a MRD and text corpus.

DODDLE II: A Domain Ontology Development Environment Using a MRD and Text Corpus

Human computer Interaction based on emotional modelling and physical views, collectively; has been investigated and reported in this paper. Two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical collected exhibited consequences of such disorder. These two types of ontology have been mapped and aligned using OWL-S and SWRL for reasoning purposes. We have constructed an integrated computerized model which reflects a human diagnostician as computer model and through it, an integrated interaction between that model and the real human user (patient) is utilized for 1st stage diagnosis purposes. The diagnostician knowledge has been utilized through UMLS for testing, and the integrated mapping of the two views been represented through OWLS framework. The reasoning instantiation is done using SWRL and RACER integrated on Protege 4.

Virtual doctor system (VDS): medical decision reasoning based on physical and mental ontologies

The paper discusses reasoning application for decision making in medical diagnosis. This is to reason on medical concepts that are viewed on two type ontologies; namely physical and mental. We highlighted in this position paper issues on fuzzy reasoning by aggregating two types of ontologies that are used to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. Similarity matching is used to find the similarity between fuzzy set reflected to mental fuzzy ontology, and physical fuzzy ontology. The alignment is projected on medical ontology to rank attributes for decision making. We apply aggregate function for ranking attributes related to physical object. In the same time, we apply harmonic power average aggregate function fuzzy for ranking attributes related to mental objects. The alignment of these two aggregate function produce weighted ranking order fuzzy set for medical decision making for diagnosis. The paper highlights these issues as new challenges extending intelligence reasoning of VDS.

Masaki Kurematsu

Papers

Empirical Based Techniques for Human Cognitive Interaction Analysis: Universal Template Design.

Facial Expression Invariants for Estimating Mental States of Person

DODDLE II: A Domain Ontology Development Environment Using a MRD and Text Corpus

Virtual doctor system (VDS): medical decision reasoning based on physical and mental ontologies

Fuzzy reasoning for medical diagnosis-based aggregation on different ontologies