<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments - may achieve an improved understanding of architectures and models - if there is more emphasis on systematic sensitivity and necessity analyses during model development, evaluation, and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge, exploring the complex interactions among architectural mechanisms, knowledge-level strategy variants, and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees, combined with parallelization on high performance computing clusters, to enable large scale, simultaneous search and exploration.</p></abstract>

The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments - may achieve an improved understanding of architectures and models - if there is more emphasis on systematic sensitivity and necessity analyses during model development, evaluation, and comparison. We demonstrate this methodological prescription with two of the models submitted for the Dynamic Stocks and Flows (DSF) Model Comparison Challenge, exploring the complex interactions among architectural mechanisms, knowledge-level strategy variants, and task conditions. To cope with the computational demands of these analyses we use a predictive analytics approach similar to regression trees, combined with parallelization on high performance computing clusters, to enable large scale, simultaneous search and exploration.

Exploration for Understanding in Cognitive Modeling

Air Force Research Laboratory, 711 HPW/RHAC, 2698 G Street, Bldg 190, WPAFB, OH 45433, USA

Lockheed Martin at Air Force Research Laboratory, 6030 S. Kent St Mesa, AZ 85212, USA

Department of Psychology, Carnegie Mellon University, 5000 Forbes Ave Pittsburgh, PA 15213, USA

Journal of Artificial General Intelligence

{"article-title":"Exploration for Understanding in Cognitive Modeling"}

The cognitive modeling and artificial general intelligence research communities may reap greater scientific return on research investments - may achieve an...

Exploration for Understanding in Cognitive Modeling

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 88 - 107

DOI: https://doi.org/10.2478/v10229-011-0011-7

Schlüsselwörter
model comparison, exploration, sensitivity, necessity, efficiency, predictive analytics, high performance computing

This content is open access.

Exploration for Understanding in Cognitive Modeling

Online veröffentlicht: 23. Nov. 2011

Seitenbereich: 88 - 107

DOI: https://doi.org/10.2478/v10229-011-0011-7

Schlüsselwörtermodel comparison, exploration, sensitivity, necessity, efficiency, predictive analytics, high performance computing

This content is open access.

Schlüsselwörter
model comparison, exploration, sensitivity, necessity, efficiency, predictive analytics, high performance computing