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Models of human behavior are essential for simulating military operations and supporting command and control across a variety of environments. These models must be able to represent the range of judgment and decision making strategies actually used by operators: from the naturalistic heuristics to the prescribed strategies from the operators’ training and commanders. This talk introduces a general linear model (GLM) of judgment and decision making that can be constructed to represent both of these descriptive and prescriptive human behaviors. We conclude by showing how the GLM can be used to describe large variations in human performance in various environments, prescribe new judgment and decision making strategies for better operational performance, and design new decision support methods and tools.
This poster contains a summary of the Congitive Engineering Center, answering the following questions:
This poster contains a summary of the major projects ongoing in the Cognitive Engineering Center as of September 2016. It was generated to present at the 10th Annual AE Expo on Sept. 7, 2016.
Abstract: This poster introduces a general linear model which integrates three major components of judgment and decision making: cue weights (the relative importance of cues), estimates of missing information (what value should an unknown cue be assigned), and utility functions (converting a cue's environmental value to a useful score). From these three components many well-studied decision making strategies, particularly those from the fast-and-frugal heuristics program, can be derived.
Design Knowledge Coordination is a structured approach to integrating design considerations across the different disciplines in engineering design through use of goals, tasks, metrics, and decisions. A key aspect to connecting coordination to aerospace engineering design is the recognition that this process encompasses distinct, yet interdependent aspects of design.
Cognitive Work Analysis (CWA) has the potential to offer a contextually relevant, constraint-based perspective to the extravehicular activity (EVA) work domain, which is a proven critical component of human spaceflight. A work domain analysis (WDA) emphasizing the real-time execution of EVA is presented in this study as a first step towards establishing the EVA work domain within the CWA framework and guiding EVA decision support system design development. The results presented here define the EVA work domain boundary objects and illustrate the physical and operational constraints of the environment, hardware, and life support systems of the domain. Additionally, the WDA is used to guide potential avenues and identify functional needs for future EVA decision support system development.
One of the few convergent themes during the first two United Nations Meeting of Experts on autonomous weapons systems (AWS) was the requirement that there be meaningful human control (MHC) of AWS. What exactly constitutes MHC, however, is still ill-defined.
The engineering design process is a complex, iterative process through which individuals and teams solve ill-defined, multidisciplinary problems by integrating domain-based technical knowledge. Aerospace engineering integrates technical components from many different disciplines, such as aerodynamics, combustion, avionics, materials science, structural analysis, flight mechanics, optimization, and manufacturing.
People often make decisions without gathering or processing all possible information. Time pressure, high information acquisition costs, too much information, and incomplete information in the environment, all contribute to this reality. This thesis is motivated by the need for decision support tools for these heuristic decision makers. The foundational contributions will be a new understanding of heuristic decision making with incomplete information and how to use this knowledge to design a heuristic decision support tool.
Dr. Karen Feigh presented work on behalf of the IML team including Dr. Andrea Thomaz, Dr. Charles Isbell and Dr. Mark Riedl. Four studies were presented:
Cognitive Engineering Center (CEC) Georgia Institute of Technology 270 Ferst Drive Atlanta GA 30332-0150
