This tutorial is intended to provide a detailed account of both the cutting-edge and the most commonly used computational approaches to situation assessment (a.k.a. level 2 fusion) and the associated generation of appropriate response recommendations for decision making under uncertainty. The tutorial materials are based on the two recently published two books High-Level Data Fusion and Foundations of Decision Making Agents.
A diverse range of level 2 fusion techniques, including Bayesian belief networks, fuzzy logic, and the theory of belief function, will be covered in the tutorial. These techniques are suitable for modeling uncertain knowledge under different situations, and their suitability will be discussed in each case. A number of temporal modeling techniques, such as dynamic belief networks and hidden Markov models will also be presented. The tutorial will discuss interactions between level 1 and level 2 fusion processes. Special emphasis will be given to a discussion on particle filtering techniques as unifying methods for both filtering under level 1 fusion and inferencing in dynamic Bayesian networks for Level 2 fusion. Finally, distributed level 2 fusion techniques, as appropriate within network centric warfare environments, will be discussed.
For the response recommendations part of the tutorial, traditional expected utility theory, rule-based expert systems, and influence diagram based decision-making processes will be described. Then a symbolic argumentation technique using first-order and non-classical modal logics will be presented. Various techniques for aggregating arguments including probability, possibility, and Dempster-Shafer theories will be covered. The argumentation technique and probabilistic aggregation are the major focus of the speaker’s recent book on symbolic decision-making.
As for software tools, an in-house 5th generation application development platform (Prolog and Lisp) and argumentation building engine, and a belief network engine will be used for illustrating response recommendations and situation assessment respectively. The commercial-off-the-shelf tools Matlab and Hugin will be used for illustrating Kalman/particle filtering for level 1 fusion, fuzzy inferencing, and influence diagrams for decision-making. Examples and prototype demos involving tasks of determining relationships among entities and events, target classification, and target identification will be provided throughout the tutorial.
Attendees receive comprehensive slides, texts, CDs and software tools to take away for future references. The content of these tutorials are drawn heavily from books by in-house experts, especially two recent ones, namely, “High-Level Data Fusion” and “Foundations of Decision Making Agent: Logic, Modality and Probability”. Each Attendee will receive a complimentary copy of the “High-Level Data Fusion” book.
- Information fusion practitioners from both academia and industry
- Developers of C4I systems who work within military organizations around the world
- Developers of systems for the diagnosis of status and health, collision avoidance, syndromic surveillance, cyber security, etc.
Lesson 1: Architectures – JDL Model and other architectures
Lesson 2: Application Scenarios – Conventional, MOUT, OOTW, Bioterrorism
Lesson 3: Background – Uncertainty, Probability and Statistics, First-Order and Modal Logics, INTs
Lesson 4: Brief Introduction to Level 1 Fusion – Data Association, Single and Multi-target Tracking, Kalman Filtering and Extensions, Particle Filtering, Rao-Blackwellised Filtering
Lesson 5: Situation Assessment – Bayesian Belief Networks, Message Passing and Junction Tree Algorithms, Theory of Belief Function, Fuzzy Logic, Hidden Markov Model, Dynamic Belief Networks, Approximate Inferencing via Particle Filtering
Lesson 6: Decision Making – Expected Utility Theory, Rule-based Expert Systems, Influence Diagrams, Dempster-Shafer Theory, Certainty Factor, Symbolic Argumentation
Lesson 7: Foundational Tools – Bayesian Belief Network Engine, 5th Generation Application Development Environment (Prolog and Lisp), Argumentation Building Engine
Lesson 8: Future – Network Centric Warfare and Distributed Fusion
Lesson 9: Selected References