Enforcing situation awareness with granular computing: a systematic overview and new perspectives

Abstract

Situation Awareness is defined by Endsley as ''the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future'' and it deals with the continuous extraction of environmental information and its integration with prior knowledge for directing further perception and anticipating future events. To realize systems for Situation Awareness, individual pieces of raw information (e.g. sensor data) should be interpreted into a higher, domain-relevant concept called ''situation'', which is an abstract state of affairs interesting to specific applications. The power of using ''situations'' lies in their ability to provide a simple, human-understandable representation of, for instance, sensor data. The aim of this work is to propose an overview of the applications of Computational Intelligence and Granular Computing for the implementation of systems supporting Situation Awareness. In this scenario, several and heterogeneous Computational Intelligence models and techniques (e.g. Fuzzy Cognitive Maps, Fuzzy Formal Concept Analysis, Dempster-Shafer Theory of Evidence, Ontologies, Knowledge Reasoning, Evolutionary Computing, Intelligent Agents) can be employed to implement such systems. Moreover, in a Situation Identification process, huge volumes of heterogeneous data need processing (e.g. fusion). With respect to this issue, Granular Computing is an information processing theory for using ''granules'' (e.g. subsets, intervals, fuzzy sets) effectively to build an efficient computational model for dealing with the above-mentioned data. The overview is proposed coherently to both methodological and architectural viewpoints for Situation Awareness.

Linking Group Theory to Social Science Game Theory: Interaction Grammars, Group Subcultures and Games for Comparative Analysis

“…Data granulation and granular computing are some of the methods for modelling and processing imprecise and uncertain data. In general, granular computing can be viewed as: (i) a method of structured problem solving; (ii) a paradigm of information processing; and (iii) a way of structured thinking Loia et al 2016). 2 In the social science literature a standard distinction is made between formal and informal rules.…”

Multi-attribute decision-making based on the SPIFGIA operators

“…MADM problems and granular computing have got more attentions from the literatures (Beliakov et al 2011;Beliakov et al 2010;Wei and Zhao 2012;Chen 2014;Bedregal et al 2014;Livi and Sadeghian 2015;Pedrycz and Chen 2015;Chen and Chang 2015;Rodríguez et al 2012Rodríguez et al , 2013Rodríguez et al , 2014He et al 2015;Chen et al 2016;Apolloni et al 2016;Antonelli et al 2016;Ciucci 2016;Lingras et al 2016;Loia et al 2016;Maciel et al 2016;Min and Xu 2016;Peters and Weber 2016;Skowron et al 2016;Wilke and Portmann 2016;Xu and Wang 2016;Yao 2016). Considering the different backgrounds of experts, Xu and Wang (2016) gave an overview on managing multigranularity linguistic term sets for MADM problems.…”

Granular computing: from granularity optimization to multi-granularity joint problem solving

“…GrC is a label of the family of any theories, methodologies, techniques and tools. It is an information processing theory for using ''granules'' to build an efficient computational model for dealing with problems (Loia et al 2016). Especially, when problems are with uncertainty, Grc can solve them approximately (Bazan 2008).…”