A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios
Angelo Gaeta1, Vincenzo Loia1, Francesco Orciuoli1
1Dipartimento di Scienze Aziendali - Management, Innovation Systems (DISA-MIS), Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy.
Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
Summary
This study introduces a new model for intelligence analysis, enhancing decision-making through situation awareness and granular computing. It integrates descriptive, relational, and behavioral perspectives, validated with real-world data and structured analytic techniques.
Area of Science:
- Intelligence analysis
- Decision support systems
- Information science
Background:
- Existing intelligence analysis methods lack comprehensive situation representation.
- Previous work by authors explored Situation Awareness and Granular Computing for intelligence support.
- Need for refined models linking theoretical approaches to practical analyst techniques.
Purpose of the Study:
- To present a comprehensive model for representing and reasoning on situations in intelligence analysis.
- To refine and abstract previous findings on Situation Awareness and Granular Computing.
- To link advanced reasoning techniques to established intelligence analysis practices.
Main Methods:
- Developed a model based on Granular Computing principles (fuzzy and rough sets).
- Integrated descriptive, relational, and behavioral perspectives for situation representation.
- Utilized graph structures and four reasoning methods validated with real data.
Main Results:
- A novel, comprehensive model for intelligence situation representation and reasoning.
- Demonstrated applicability through case studies and real-world data validation.
- Successful integration with Structured Analytic Techniques.
Conclusions:
- The proposed model effectively supports intelligence decision-makers.
- Granular Computing provides a robust framework for situation awareness in intelligence.
- The model bridges theoretical advancements with practical intelligence analysis needs.
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