This book focuses on the definition and implementation of data-driven computational tools supporting decision-making along heterogeneous intelligence scenarios. Intelligence analysis includes methodologies, activities, and tools aimed at obtaining complex information from a set of isolated data gathered from different sensors. The tools aim at increasing the level of situation awareness of decision-makers through the construction of abstract structures supporting human operators in reasoning and making decisions. This book appeals to students, professionals, and academic researchers in computational intelligence and approximate reasoning applications. It is a comprehensive textbook on the subject, supported with case studies and practical examples in Python. The readers will learn how to define decision support systems for the intelligence analysis through the application of situation awareness and granular computing for information processing.
Author(s): Vincenzo Loia, Francesco Orciuoli, Angelo Gaeta
Publisher: Springer
Year: 2023
Language: English
Pages: 184
City: Cham
Preface
Contents
About the Authors
Part I Foundations
1 Introduction
1.1 Book Content Structure
1.2 Didactic Aspects
1.3 Using Python and Google Colaboratory
1.4 Useful Resources
2 Intelligence Analysis
2.1 Learning Objectives of the Chapter
2.2 Topic Map of the Chapter
2.3 What Is Intelligence Analysis?
2.4 The Intelligence Process
2.5 The Analysis Process and Analytic Tradecraft
2.5.1 Families of SAT
2.5.2 Adoption of SAT in the Intelligence Analysis Process
2.5.3 Cognitive and Perceptual Biases
2.6 Some Challenges of Intelligence Analysis
3 Foundations of the Computational Techniques
3.1 Learning Objectives of the Chapter
3.2 Topic Map of the Chapter
3.3 Situation Awareness
3.3.1 SA Demons
3.3.2 Information Processing for SA
3.3.3 Goal-Directed Task Analysis
3.3.4 Representing Operational Situations
3.4 Granular Computing
3.5 Dealing with Imprecise and Uncertain Information
3.5.1 Rough Set Theory
3.5.2 Fuzzy Set Theory
3.6 Three-Way Decisions
3.6.1 Setting Thresholds in 3WD
3.7 Hands-on Lab
3.7.1 Basic Source Code
3.7.2 Probabilistic and Traditional Rough Sets
3.7.3 Three-Way Decisions
3.7.4 Fuzzy Sets and Fuzzy Relations
3.7.5 Useful Resources
4 Applying Situation Awareness to Intelligence Analysis
4.1 Learning Objectives of this Chapter
4.2 Topic Map of the Chapter
4.3 Information Processing: Overall Framework
4.4 Mapping into Intelligence Cycles
4.5 Selection and Adoption of Concrete Techniques
Part II Computational Techniques
5 Decision Making Through What-If Analysis
5.1 Learning Objectives of the Chapter
5.2 Topic Map of the Chapter
5.3 Case Introduction: Vessel Surveillance
5.4 Methodology
5.5 Computational Techniques
5.5.1 Lattice Derivation with Probabilistic Rough Sets
5.5.2 Measures for Situation Evolution
5.5.3 Reasoning with 3WD on Temporal Structures
5.6 Analytical Value
5.7 Hands-on Lab
5.7.1 Lattice Building
5.7.2 Assessing the Situation Evolution
5.8 Useful Resources
6 System Modelling with Graphs
6.1 Learning Objectives of the Chapter
6.2 Topic Map of the Chapter
6.3 Case Introduction: Evaluation of Critical Nodes in a Critical Infrastructure
6.4 Methodology
6.5 Computational Techniques
6.5.1 3WD Supported by Network Analysis
6.5.2 Reasoning on Graphs with GrC
6.6 Analytical Value
6.7 Hands-on Lab
6.7.1 Dataset
6.7.2 Building the Graph
6.7.3 Calculating T Values
6.7.4 Building the Decision Table
6.7.5 Applying 3WD
6.7.6 Implementing a Resilience Model
6.8 Useful Resources
7 Behaviour Modelling with Fuzzy Signatures
7.1 Learning Objectives of the Chapter
7.2 Topic Map of the Chapter
7.3 Case Introduction: Counter-Terrorism Analysis with the Global Terrorism Database
7.4 Methodology
7.5 Computational Techniques
7.5.1 Fuzzy Signature
7.5.2 Reasoning Based on 3WD and Fuzzy Signature
7.6 Analytical Value
7.7 Hands-on Lab
7.7.1 Building the Activity Matrix
7.7.2 Defining the Fuzzy Sets
7.7.3 Constructing the Fuzzy Signature
7.7.4 Projecting and Reasoning with Fuzzy Signatures
7.8 Useful Resources
8 Concept Drift Analysis with Structures of Opposition
8.1 Learning Objectives of the Chapter
8.2 Topic Map of the Chapter
8.3 Case Introduction: Opinion Changes
8.4 Methodology
8.5 Computational Techniques
8.5.1 Creating and Reasoning on Structures of Opposition Based on Rough Set
8.6 Analytical Value
8.7 Hands-on Lab
8.7.1 Building Hexagons of Opposition
8.7.2 Visualizing Hexagons of Opposition
8.8 Useful Resources
Part III Methodological and Technological Insight
9 Comparing Different Approaches for Implementing Probability-Based Rough Set Operators
9.1 Useful Resources
10 Data Streaming Scenarios
10.1 Useful Resources
11 Dealing with Continuous Variables: Neighborhood and Dominance Based Rough Sets
11.1 Strategies for Implementing Neighborhood Rough Sets
11.2 Useful Resources
Appendix References
