Combining innovation and efficiency is ideal in many organizational settings. Adaptive expertise represents a cognitive explanation of how individuals and teams can learn to achieve simultaneous innovation and efficiency. In 2004, scientists led twin rovers on Mars in the search for historical water. The science team experienced a remarkable increase in efficiency, adapting with flexibility to unexpected events and dynamic, dwindling resources. After discussing the conceptual differences between adaptive expertise and related team learning and innovation concepts, we examine longitudinal behavioral data on novelty, routine and adaptive expertise. Sequential time series ARIMA analyses reveal that novelty fluctuated randomly, but both routine and adaptive expertise significantly increased over time. In addition, novelty, routine expertise, and adaptive expertise did not significantly predict each other directly or at a lag, suggesting that these are indeed three distinct constructs. Implications for theory and research on efficiency and innovation are discussed.

This paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model development environment for the CORE constraint-based cognitive modeling engine [7,13,21]. X-PRT comprises a novel feature set: (a) it supports the automatic generation of predictive models at multiple skill levels from a single taskspecification, (b) it supports a comprehensive set of modeling activities, and (c) it supports compositional reuse of existing cognitive/perceptual/motor skills by transforming high-level, hierarchical task descriptions into detailed performance predictions. Task hierarchies play a central role in X-PRT, serving as the organizing construct for task knowledge, the locus for compositionality, and the cognitive structures over which the learning theory is predicated. Empirical evidence supports the role of task hierarchies in routine skill acquisition.

It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.

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This paper presents a case study of the NASA Ames Research Center HCI Group's design and development of a problem reporting system for NASA's next generation vehicle (to replace the shuttle) based on the adaptation of an open source software application. We focus on the criteria used for selecting a specific system (Bugzilla) and discuss the outcomes of our project including eventual extensibility and maintainability. Finally, we address whether our experience may generalize considering where Bugzilla lies in the larger quantitative picture of current open source software projects.