A granular computing framework for approximate reasoning in situation awareness

Abstract

We present our results on the adoption of a set-theoretic framework for granular computing to situation awareness. The proposed framework guarantees a high degree of flexibility in the process of creation of granules and granular structures allowing to satisfy the wide variety of requirements for perception and comprehension of situations where some elements must be perceived per similarity, others per spatial proximity, some must be fused to improve their comprehension, and so on. A second value is the support for approximate reasoning in situation awareness. A granular structure in particular represents a snapshot of a situation, and is a building block for the development of tools and techniques to reason on situation in order to reduce situation awareness errors and accelerate the process of decision-making. To this purpose, we show a technique to support operators in the analysis of conformity between a recognized situation and an expected one. A third value is the fact that we can support operators in having rapid and indicative measures of how two situations, e.g. a recognized and a projected, may differ. A preliminary evaluation instantiating our approach with self-organizing maps is reported and discussed. The results are encouraging with respect to the capability of improving perception and comprehension of a situation, reducing comprehension errors and supporting projection of situations

Spatial and temporal reasoning with granular computing and three way formal concept analysis

“…The achieved results are promising mainly with respect to the possibility of obtaining explainable results that can be observed and understood at different levels of granularity. We are going to evaluate the method in other applications such as situation awareness (Loia et al 2016;D'Aniello et al 2017), analysis and reasoning on tweet streams (Cuzzocrea et al 2015) and sign prediction in social networks , smart city (D'Aniello et al 2016) and smart museum (Capuano et al 2016), and kinds of events such as terrorism (''Understanding the composition and evolution of terrorist group networks: A rough set approach'', 2019) and security analysis (Fujita et al 2019).…”

Subclass-based semi-random data partitioning for improving sample representativeness

“…In the future, we will investigate clustering techniques [3,4,5,6,13,19,34] towards decomposing big data into multiple contexts, such that the training data obtained within each cluster can be more representative in the corresponding context. It is also worth to investigate granular computing techniques [7,14,31,35,41,44] for the decomposition of each class in more depth [20,22,24,30], towards further improvements of sample representativeness.…”

“…Entropy(Humidity) = 7 14 CS(Humidity = 1) + 7 14 CS( Table 5 Frequency According to Table 5, we can calculate the average entropy Entropy(W indy)…”

A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios

“…The authors of this paper studied a systematic integration of SA and GrC in previous works [ 7 , 30 ]. In this paper, the main contribution refers to the definition of a comprehensive model to represent and reason on situations with the aim of supporting Intelligence analysis activities for decision-making.…”