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Many cognitive engineering methodologies for user-centered design involve modeling procedural knowledge; others deal with domain semantics or conceptual models. COGNET (COGnitive NEwork of Tasks) is a framework for modeling human cognition and decision-making which provides an integrated representation of the knowledge, behavioral actions, strategies and problem solving skills used in a domain or task situation, yielding a powerful cognitive engineering tool. A case study of the design of the user interface for a new telephone operator workstation is presented to illustrate the derivation of the design from the components of the COGNET model. The model does not directly convey any specific feature of the interface design, but rather a formal representation of the what the user must do with the resulting interface. This information is then evolved through a set of transformations which systematically move toward design features, in a fully traceable manner.

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Cognitive model-based synthetic teammates allow individuals to engage in guided mission rehearsal when other teammates and/or instructors may not be available. Synthetic teammates employed in a training simulation environment need to behave in a believable manner in order to interact with human teammates effectively. Synthetic teammate deve!opers must consider which types of exhibited behavior are required to provide instruction. Classes of behavior that may be required of a synthetic include individual taskwork, teamwork, and instruction. In addition, the level of human performance emulation, Le., the degree of competency of these behaviors, needs to be considered. This paper discusses these classes of behavior as well as the appropriateness of performance versus competence modeling. Two examples of synthetic teammate development efforts are presented that illustrate these behavioral characteristics.

PDQ and CANCERLIT are databases that represent potentially valuable resources for oncologists, but there are significant obstacles to their effective use in the clinical context. The clinician must learn where each databasecan be found, what it contains, and how to query it. More importantly, the clinician must also determine when and how each information resource is appropriate to the (clinical) task at hand, and integrate their general information with patient-specific data contained in patient records and clinical information systems. This research sought to remove these barriers to information use by developing a software agent in the human-computer interface that automates the information retrieval and integration process. The agent, developed from a cognitive model of how human experts perform these tasks, provides both a specific solution to the cancer information access problem, and a template for solving similar problems that occur with increasing frequency in many other domains.