2024 IEEE Conference on Cognitive and Computational Aspects
of Situation Management (CogSIMA)

Tutorial 1: Situation Awareness in the Design of Future Complex Systems

Corentin Conan: PhD candidate at McGill University in the department of Electrical and Computer Engineering. With a background in aeronautical engineering and HCI, he specializes in airport navigation for commercial airliners. His research interests include situation awareness, automation transparency, and the future of aviation.

Tutorial 2: High-Level Information Exploitation

Abstract: This tutorial presents important new developments in the use of situational and scenario information in managing complex operations under uncertainty. These concepts enable a structured systematic approach both a) for human-centered practitioners to represent and evaluate biological methods in acquiring and applying knowledge and b) for systems and AI engineers to build effective systems for the same.

Improved context-sensitive exploitation of diverse information is achievable by deeper understanding of the concepts involved in representing, recognizing, and predicting relationships, situations, contexts, courses of actions, interactions, and outcomes.

Current concepts involved in lower and higher-level data fusion and resource management are presented, as applicable both in systems engineering and in modeling biological information acquisition and use. Methods are discussed for achieving synergy across the levels through a common ontology and architecture for uncertainty management. A unified model framework is defined for representing one's own courses of action (CoA) for purposes of planning and performance assessment as well as CoAs of external entities, for use in scenario and outcome prediction or forensic analysis. Finite state CoA models are discussed, with necessary and sufficient conditions for state transitions modeled in terms of capability, opportunity, and intent for such transitions over time, employing a utility/probability/cost calculus. We discuss a reference architecture for closed-loop situation/scenario management under uncertainty; whereby level 3 mission management and level 3 fusion processes iteratively plan, evaluate, and execute courses of action using machine learning and game-theoretic methods. Application examples include traditional and asymmetric warfare, involving both machine and human intelligence.

Intended Audience: Information system architects, systems engineers, and software developers will find this tutorial to be very useful in designing, developing, testing, and evaluating context-sensitive information exploitation systems.

This tutorial is recommended for practitioners in Cognitive Sciences and Artificial Intelligence/ Machine Learning to gain insight into current concepts in Multi-Level Data Fusion as a structured approach for characterizing, designing, and evaluating processes for closed loop planning and response under uncertainty.

Instructor's biography: Alan Steinberg is recognized internationally as one of the leading experts in sensor data fusion and information exploitation, with over 35 years' experience as a designer, developer and operational user of major targeting, electronic combat, and intelligence systems. He is well-known for revisions to the JDL data fusion model and advances in high-level data fusion. He is the recipient of the prestigious Mignona Award for outstanding achievement in data fusion. He has served on blue-ribbon panels for the U.S. Government to evaluate and recommend technology developments and the restructuring of the Intelligence Enterprise.

His recent work focuses on the theoretical foundations and development of systems for situation and threat assessment, including the adaptive exploitation of contextual information. He developed a technology assessment and forecast methodology; assessing diverse emerging technologies as potential threats and opportunities to support technology investment. He is the lead engineer of a system for counter-drone data exploitation and response, in which he defined an architecture and methods for high-level data fusion and response.

He provides seminars and technical support to fusion and sensor system developments in the U.S. and internationally.

Tutorial 3: Decision Making Under Risk, Uncertainty, and Ignorance

Abstract: Decision theory, the study of rational choices under risk, has long been dominated by the principle of Maximization of Expected Utility (MEU). Despite its long and successful use in fields such as insurance, resource management, and engineering design, classical MEU Decision Theory has significant limitations since it is not consistently descriptive of human decision-making. The limitations of MEU as a descriptive theory suggest that human decision-makers may distrust decision support systems predicated on it, limiting its real-world usefulness. MEU assumes that all uncertainties in the problem domain be aleatoric rather than epistemic and that both the numerical probabilities and utilities for all decision consequences be specified. Probabilities are estimated based on human opinion and/or data available. Human opinions suffer various biases while available data can be of insufficient quality, unreliable, ambiguous, or irrelevant. Utilization of probability theory for decisions under risk requires taking into account problems with probability estimation.

Decision making under uncertainty and its extreme form, ignorance, is in an opposite epistemic state characterized by the lack of reliable information about the situation of interest. Uncertainty is described in the literature as a state of knowledge when the situation characteristics are known but the likelihood of consequences is not precisely described while ignorance is the state of knowledge when not all the situational characteristics are defined. A particularly important instance in which likelihood of consequences is not reliably known, or may even not be reasonably defined, is the category of decision problems in which, among the universe of discourse, lie events, which are quite rare but highly consequential (grey swan events). To deal with the problems of uncertainty and ignorance as well as various human decision making biases, multiple non-Bayesian decision making models based on non-additive beliefs have been introduced. Among them are Choquet Expected Utility, Cumulative Prospect Theories, and Multiple Quantile Theories, models utilizing fuzzy, possibility, and evidential theories. Consideration of the models of decision making under uncertainty and ignorance is especially important for risk assessment and management of dynamic uncertain and poorly characterized situations.

The tutorial will survey the ideas and methods of decision making under risk, uncertainty, and ignorance, with a focus on recent advances in non-Bayesian models. Examples of decision making and action selection in dynamic uncertain situations will be considered.

Intended Audience: This tutorial is intended for both researchers and practitioners from a wide variety of fields such as communication, intelligence, business processes, health care, and finance, who are interested in understanding of problems of decision making in highly uncertain dynamic environments and building methods for dealing with these problems.

Instructor's biography: Dr. Rogova is a research professor at the State University at Buffalo as well as an independent consultant (DBA Encompass Consulting). She is a recognized expert in information fusion, decision making, machine learning and information quality. Her other research expertise includes reasoning under uncertainty, and image understanding. She has worked on a wide range of defense and non-defense problems such as decision making involving low probability high consequence events, situation and threat assessment, information quality in information fusion, computer-aided diagnosis, and understanding of volcanic eruption patterns, among others. Dr. Rogova published numerous papers and lectured internationally on these subjects. She co-edited 7 books including "Meeting Security Challenges Through Data Analytics and Decision Support", "Fusion Methodologies in Crisis Management: Higher Level Fusion and Decision Making", and "Information Quality in Information Fusion and Decision Making." Dr. Rogova has been a tutorial lecturer and a member of organizing committees of multiple conferences on information fusion and decision support.

Tutorial 4: FEEDBACK: Its role in Technology, Nature and Society

Abstract: The use of feedback control in technology goes back to the prehistoric Polynesian outrigger canoe followed by the Hellenistic period when a water clock was constructed. Feedback was the central concept of Norbert Wiener's half-forgotten Cybernetics. Roughly speaking, negative feedback reduces the error or deviation from a goal state, therefore, has stabilizing effects. Conversely, positive feedback, which increases the deviation from an initial state, has destabilizing effects. It may amplify minor initial differences leading to such dramatic catastrophes as epileptic seizures, massive earthquakes and tsunamis, climate catastrophes, and social unrest.

There is a narrow border between destruction and prosperity: to ensure reasonable growth but avoid existential risk, we need to find the fine-tuned balance between positive and negative feedback. The tutorial offers an exciting integrative, non-technical introduction around the application of feedback control.

Intended Audience: General interest in crisis and disaster management, systems dynamics behavior, prediction and control.

Instructor's biography: Péter Érdi serves as the Henry R. Luce Professor of Complex Systems Studies at Kalamazoo College. He is also a research professor in his home town, in Budapest, at the Wigner Research Centre of Physics. In addition, he is the founding co-director of the Budapest Semester in Cognitive science, a study abroad program.

Péter is a member of the Advisory Board of the Cognitive Situation Management. He served for many years as a Member of the Board of Governors of the International Neural Network Society, for two years as a VP of Membership, of the IEEE Computational Intelligence Society Curriculum Subcommitte, and served for five years as others as the Editor-in-Chief of Cognitive Systems Research. His books on mathematical modeling of chemical, biological and other complex systems have been published by Princeton University Press, MIT Press, Springer Publishing House. His recent non-fiction books RANKING: The Unwritten Rules of the Social Game We All Play, Oxford University Press, 2019 and Repair: When and How to Improve Broken Objects, Ourselves, and Our Society was particularly successful in the East Asian countries.