Data modeling for predictive behavior hypothesis formation and testing
Holger M. Jaenisch, James W. Handley, Marvin H. Barnett, Richard Esslinger, David A. Grover, Jeffrey P. Faucheux, Kenneth Lamkin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
This paper presents a novel hypothesis analysis tool building on QUEST and DANCER. Unique is the ability to convert cause/effect relationships into analytical equation transfer functions for exploitation. In this the third phase of our work, we derive Data Models for each unique word and its ontological associated unique words. We form a classical control theory transfer function using the associated words as the input vector and the assigned unique word as the output vector. Each transfer function model can be tested against new evidence to yield new output. Additionally, conjectured output can be passed through the inverse model to predict the requisite case observations required to yield the conjectured output. Hypotheses are tested using circumstantial evidence, notional similarity, evidential strength, and plausibility to determine if they are supported or rejected. Examples of solving for evidence links are provided from tool execution.