The framework provided in this article provides a tool for organizing and informing past, present, and future research and development efforts in adaptive systems.

In this article, the authors empirically assess the costs and benefits of designing an adaptive system to follow social conventions regarding the appropriateness of interruptions. Interruption management is one area within the larger topic of automation etiquette. The authors tested these concepts in an outdoor environment using the Communications Scheduler, a wearable adaptive system that classifies users' cognitive state via brain and heart sensors and adapts its interactions. Designed to help dismounted soldiers, it manages communications in much the same way as a good administrative assistant. Depending on a combination of message priority, user workload, and system state, it decides whether to interrupt the user's current tasks. The system supports decision makers in two innovative ways: It reliably measures a mobile user's cognitive workload to adapt its behavior, and it implements rules of etiquette adapted from human-human interactions to improve humancomputer interactions. Results indicate costs and benefits to both interrupting and refraining from interrupting. When users were overloaded, primary task performance was improved by managing interruptions. However, overall situation awareness on secondary tasks suffered. This work empirically quantifies costs and benefits of "appropriate" interruption behaviors, demonstrating the value of designing adaptive agents that follow social conventions for interactions with humans.

This research investigated how machine operator expertise, strategies, and decision-making can be integrated into operator models that simulate authentic human behavior in construction machine operations. Physical prototype tests of construction machines require significant time and cost. However, computer-based simulation is often limited by the fidelity in which human operators are modeled. A greater understanding of how highly skilled operators obtain high machine performance and productivity can inform machine development and advance construction automation technology. Operator interviews were conducted to build a framework of tasks, strategies, and cues commonly used while controlling an excavator through repeating work cycles. A closed loop simulation demonstrated that an operator model could simulate the trenching work cycle with multiple operator strategies, and adapt to different vehicle and work site settings. A Virtual Operator Model that captures human expert behaviors can be used to assess vehicle characteristics and efficiency, and inform the design of automation systems.

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The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. Although this presents a difficult technical challenge in any context, these concerns are particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically derived cognitive state classification approach designed for ambulatory task contexts. We highlight signal processing and classification components that render the electroencephalogram (EEG) -based cognitive state estimation system robust to noise. Field assessments show classification performance that exceeds 70% for all participants in a context that many have regarded as intractable for cognitive state classification using EEG. ADDRESS CORRESPONDENCE TO: Michael C. Dorneich, Honeywell Laboratories, Human-Centered Systems, 3660 Technology Dr., Minneapolis, MN 55418, 612/951-7488, michael.dorneich@honeywell.com.

With the movement in education towards collaborative learning, it is becoming more important that learners be able to work together in groups and teams. Intelligent tutoring systems (ITSs) have been used successfully to teach individuals, but so far only a few ITSs have been used for the purpose of training teams. This is due to the difficulty of creating such systems. An ITS for teams must be able to assess complex interactions between team members (team skills) as well as the way they interact with the system itself (task skills). Assessing team skills can be difficult because they contain social components such as communication and coordination that are not readily quantifiable. This article addresses these difficulties by developing a framework to guide the authoring process for team tutors. The framework is demonstrated using a case study about a particular team tutor that was developed using a military surveillance scenario for teams of two. The Generalized Intelligent Framework for Tutoring (GIFT) software provided the team tutoring infrastructure for this task. A new software architecture required to support the team tutor is described. This theoretical framework and the lessons learned from its implementation offer conceptual scaffolding for future authors of ITSs.

The package planning (chip layout and compaction) problem can be stated in terms of an optimization problem. The goal is to find the relative placement and shapes of the chips in a way that minimizes the total chip area subject to linear and nonlinear constraints. The constraints arise from geometric design rules, distance and connectivity requirements between various components, area and communication costs and other designer-specified requirements. The problem has been addressed in various settings. It is of unusual computational difficulty due to the nonconvexities involved. This paper presents a new mixed-integer nonlinear programming formulation for simultaneous chip layout and two-dimensional compaction. Global optimization algorithms are developed for this model as well as for an existing formulation lor the chip compaction problem. These algorithms are implemented with the global optimization software BARON and illustrated by solving several example problems.