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Studies on design, show that problem formulation plays a major role in creative design. We plan to construct an interactive computer system that aids problem formulation. In the current stage, to improve our understanding of problem formulation, we have conducted exploratory protocol studies of novice designers and collected data from an expert designer in the form of a depositional interview. A formal representation of the design problem is needed to improve our empirical investigation. We propose a preliminary framework for such a model and we call it a problem map. It provides a basis for comparing how different designers perceive a problem. Our study is based on the design of a model aircraft for the AIAA student design competition. This preliminary analysis shows the evolution of the problem and the solution spaces in the elaboration of the problem maps through time. The problem maps also show a richer representation of attended attributes and relations for the expert and more attributes left in vacuum for the novices.

Laban movement analysis (LMA) is a systematic framework for describing all forms of human movement and has been widely applied across animation, biomedicine, dance, and kinesiology. LMA (especially Effort/Shape) emphasizes how internal feelings and intentions govern the patterning of movement throughout the whole body. As we argue, a complex understanding of intention via LMA is necessary for human-computer interaction to becomeembodiedin ways that resemble interaction in the physical world. We thus introduce a novel, flexible Bayesian fusion approach for identifying LMA Shape qualities from raw motion capture data in real time. The method uses a dynamic Bayesian network (DBN) to fuse movement features across the body and across time and as we discuss can be readily adapted for low-cost video. It has delivered excellent performance in preliminary studies comprising improvisatory movements. Our approach has been incorporated inResponse, a mixed-reality environment where users interact via natural, full-body human movement and enhance their bodily-kinesthetic awareness through immersive sound and light feedback, with applications to kinesiology training, Parkinson's patient rehabilitation, interactive dance, and many other areas.

The generation of referring expressions is a central topic in computational linguistics. Natural referring expressions – both definite references like ‘the baseball cap’ and pronouns like ‘it’ – are dependent on discourse context. We examine the practical implications of context-dependent referring expression generation for the design of spoken systems. Currently, not all spoken systems have the goal of generating natural referring expressions. Many researchers believe that the context-dependency of natural referring expressions actually makes systems less usable. Using the dual-task paradigm, we demonstrate that generating natural referring expressions that are dependent on discourse context reduces cognitive load. Somewhat surprisingly, we also demonstrate that practice does not improve cognitive load in systems that generate consistent (context-independent) referring expressions. We discuss practical implications for spoken systems as well as other areas of referring expression generation.

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