Patterns of Ruliness in HCI

Bibliographical data, abstract, contents links and bibliography

In: Beale, R. and Finlay, J. (eds), Neural Networks and Pattern Recognition in Human-Computer Interaction, ch 15. Ellis Horwood, Chichester, England, 1992.

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Abstract

To model users, or learn about them, we need to have good representations of situations and actions from a human, cognitive point of view, which involves the difficult but important question of what information people are using. Here, a method of using machine learning to evaluate and compare different representations is described, with examples of the results from applying this method to human control data from a simulation game. This method was used to show the significance of contextuality in the analysis of the human data, which opens up great possibilities for further work based around the idea of contexts. If problems can be overcome, concerning uncaptured data and analogue variables, one can look forward to potential applications in interface design and redesign, and in the study of human learning of cognitive skills.

Links to sections of the paper

Introduction

A fundamental problem in complex HCI

Representation

Contextuality

Ruliness

Experiments on ruliness and representation

Background

Outline of analytical methods

Results

Discussion

Contextuality in human control

Outlook for further developments

General problems

Possible applications

Conclusion

Bibliography

Card, S. K., Moran, T. P., and Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, Hillsdale, NJ.

Carroll, J. M., Kellogg, W. A., and Rosson, M. B. (1991). The task-artifact cycle. In: Carroll, J. M. (ed.), Designing Interaction: Psychology at the Human Computer Interface. Cambridge University Press, Cambridge.

Clark, P. and Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3(4): 261--283.

Davenport, C. and Weir, G. (1986). Plan recognition for intelligent advice and monitoring. In: Harrison, M. D. and Monk, A. F. (eds), People and Computers: Designing for Usability, pp. 296--315. Cambridge University Press.

Gams, M. and Lavrac, N. (1987). Review of five empirical learning systems within a proposed schemata. In: Bratko, I. and Lavrac, N. (eds), Progress in Machine Learning: Proceedings of EWSL-87, Bled, Yugoslavia, pp. 46--66, Wilmslow. Sigma Press.

Grant, A. S. (1990). Modelling Cognitive Aspects of Complex Control Tasks. PhD thesis, Department of Computer Science, University of Strathclyde, Glasgow.

Laird, J. E., Newell, A., and Rosenbloom, P. S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, 33: 1--64.

Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man and Cybernetics, SMC-13: 257--266.

Woods, D. D. (1991). The cognitive engineering of problem representations. In: Weir, G. R. S. and Alty, J. L. (eds), Human-Computer Interaction and Complex Systems. Academic Press, London.