COGENT: cognitive agent to amplify human perception and cognition

In this paper, we demonstrate the feasibility of a cognitive agent, COGENT, to amplify human perceptual and cognitive abilities in complex high-value operational environments such as are found in spacecraft ground-based telemetry monitoring systems. The architecture of COGENT is based on Rasmussen's integrated theory of human information processing, and supports decisionmaking at multiple levels of abstraction and complexity. The decision-aiding process ranges from situation feature extraction and summarization to support perceptual processing, through situation assessment and alert generation to support enhanced situation awareness, to situation-specific response recommendation to support complex decision-making. COGENT displays information in a format that is easily understood, and matches the user's own mental model of the situation, thus requiring minimal amounts of translation and cognitive processing for assimilation. The robust performance of COGENT is assured through the use of complementary AI techniques such as probabilistic belief networks and argumentation. We developed and demonstrated a prototype for COGENT, and tested it on real telemetry data sets. We are specifically addressing our effort for enhancing operator situation awareness and decision-aiding capability at ground stations.

[1]  John Fox,et al.  Qualitative frameworks for decision support: lessons from medicine , 1992, The Knowledge Engineering Review.

[2]  Ann E. Nicholson,et al.  Dynamic Belief Networks for Discrete Monitoring , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[3]  Oscar H. IBARm Information and Control , 1957, Nature.

[4]  Paul Krause,et al.  Representing Uncertain Knowledge , 1993, Springer Netherlands.

[5]  Dominic A. Clark,et al.  Representing uncertain knowledge - an artificial intelligence approach , 1993 .

[6]  Paul E. Lehner,et al.  Expert decision-making in evolving situations , 1989, IEEE Trans. Syst. Man Cybern..

[7]  Judea Pearl,et al.  On Evidential Reasoning in a Hierarchy of Hypotheses , 1990, Artif. Intell..

[8]  J. Rassmusen,et al.  Information Processing and Human - Machine Interaction: An Approach to Cognitive Engineering , 1986 .

[9]  Gary Klein,et al.  Decision Making in Armored Platoon Command , 1990 .

[10]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  John Fox,et al.  Decision making and plan management by autonomous agents: theory, implementation and applications , 1997, AGENTS '97.

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  Chris Higgins,et al.  SAM—A Tool to Support the Construction, Review and Evolution of Safety Arguments , 1993 .