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Constructive memory for situated design agents

Published online by Cambridge University Press:  08 April 2005

PAK-SAN LIEW  and
JOHN S. GERO
PAK-SAN LIEW
Affiliation:
Key Centre of Design Computing and Cognition, School of Architecture, Design Science and Planning, University of Sydney, Chippendale, NSW 2006, Australia
JOHN S. GERO
Affiliation:
Key Centre of Design Computing and Cognition, School of Architecture, Design Science and Planning, University of Sydney, Chippendale, NSW 2006, Australia
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Abstract

Design is situated. “Situatedness” in designing entails the explicit consideration of the state of the environment, the knowledge and experiences of the designer, and the interactions between the designer and the environment during designing. Central to the notion of situatedness is the notion of design situation and constructive memory. A design situation models a particular state of interaction between a design agent and the environment at a particular point in time. Memory construction occurs whenever a design agent uses past experiences and knowledge within the current design environment in a situated manner. This paper is concerned with the development of an agent-based computational design tool that takes into consideration the notion of situatedness in designing. A key element of this tool is a constructive memory system that supports the dynamic nature of designing. Memories of past experiences are constructed as required by the current situation, and past experiences are refined for future utility according to the current interactions between the agent and the environment. This latter case of knowledge improvement is illustrated through a series of experiments that demonstrates the effect of grounding on the operating modes and responses of a constructive memory system.

Keywords

Constructive MemorySituated Design Agent
Type
Research Article
Information
AI EDAM , Volume 18 , Issue 2 , May 2004 , pp. 163 - 198
Copyright
© 2004 Cambridge University Press

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References

REFERENCES

Atkinson, R.C. & Shiffrin, R.M. (1968). Human memory: A proposed system and its control processes. In The Psychology of Learning and Motivation: Advances in Research and Theory (Spence, K.W., Ed.), pp. 89–195. New York: Academic.
Ausubel, D.P. (1968). Educational Psychology: A Cognitive View. New York: Holt, Rinehart, & Winston.
Baddeley, A.D. (1999). Memory. In The MIT Encyclopedia of the Cognitive Sciences (Wilson, R.A. & Keil, F.C., Eds.), pp. 514–517. Cambridge, MA: MIT Press.
Boothroyd, G. (1994). Product design for manufacture and assembly. Computer-Aided Design 26(7), 505–519.Google Scholar
Boothroyd, G. & Dewhurst, P. (1989). Product Design for Assembly. Wakefield, RI: Boothroyd Dewhurst, Inc.
Boothroyd, G., Dewhurst, P., & Knight, W. (1994). Product Design for Manufacture and Assembly. New York: Marcel Dekker.
Boothroyd, G., Poli, C., & Murch, L.E. (1982). Automatic Assembly. New York: Marcel Dekker.
Bruner, J.S. (1966). Toward a Theory of Instruction. New York: Norton.
Clancey, W.J. (1997). Situated Cognition: On Human Knowledge and Computer Representations. New York: Cambridge University Press.
Dewey, J. (1896). The reflex arc concept in psychology. Psychological Review 3, 357–370.Google Scholar
Fiesler, E. & Beale, R. (Eds.). (1997). Handbook of Neural Computation. New York: Oxford University Press.
Gero, J.S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Gero, J.S. (1998a). Design tools that learn. Advances in Engineering Software 29(10), 755–761.Google Scholar
Gero, J.S. (1998b). Towards a model of designing which includes its situatedness. In Universal Design Theory (Grabowski, H., Rude, S. & Green, G., Eds.), pp. 47–56. Aachen, Germany: Shaker Verlag.
Gero, J.S. & Fujii, H. (2000). A computational framework for concept formation in a situated design agent. Knowledge-Based Systems 13(6), 361–368.Google Scholar
Gero, J.S. & Kannengiesser, U. (2000). Towards a situated function–behaviour–structure framework as the basis for a theory of designing. In Workshop on Development and Application of Design Theories in AI in Design Research, Artificial Intelligence in Design '00 (Smithers, T., Ed.), pp. 1–5, Worcester, MA.
Gero, J.S. & Kannengiesser, U. (2002). The situated function–behaviour–structure framework. In Artificial Intelligence in Design '02 (Gero, J.S., Ed.), pp. 89–104. Dordrecht: Kluwer Academic.
Gero, J.S., Tham, K.W., & Lee, H.S. (1992). Behaviour: A link between function and structure in design. In Intelligent Computer Aided Design (Brown, D.C., Waldron, M.B. & Yoshikawa, H., Eds.), pp. 193–225. Amsterdam: North–Holland.
Goker, M.H. (1997). The effects of experience during design problem solving. Design Studies 18, 405–426.Google Scholar
Goldschmidt, G. (1997). Capturing indeterminism: Representation in the design problem space. Design Studies 18, 441–455.Google Scholar
Haykin, S.S. (1998). Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice–Hall.
Hoehfeld, M. & Fahlman, S.E. (1992). Learning with limited numerical precision using the cascadecorrelation learning algorithm. IEEE Transactions on Neural Networks 3(4), 602–611.Google Scholar
Jonassen, D.H. (1991). Objectivism versus constructivism: Do we need a new philosophical paradigm? Educational Technology Research and Development 40(3), 5–14.Google Scholar
Kumar, B. & Raphael, B. (2001). Derivational Analogy Based Structural Design. Stirling: Saxe–Coburg Publications.
Logie, R.H. (1995). Visuo-Spatial Working Memory. Hove: Erlbaum.
Lueg, C. & Pfeifer, R. (1997). Cognition, situatedness, and situated design. Proc. Second Int. Conf. Cognitive Technology (CT 97), pp. 124–135, Aizu, Japan, August 25–28.
Maher, M.L., Balachandran, M.B., & Zhang, D.M. (1995). Case-Based Reasoning in Design. Mahwah, NJ: Erlbaum.
Mandic, D. & Chambers, J. (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Singapore: Wiley.
McClelland, J.L. (1981). Retrieving general and specific information from stored knowledge of specifics. Proc. Third Annual Meeting of the Cognitive Science Society, pp. 170–172, Hillsdale, NJ: Erlbaum.
McClelland, J.L. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Cambridge, MA: MIT Press.
McClelland, J.L. (1995). Constructive memory and memory distortion: A parallel-distributed processing approach. In Memory Distortion: How Minds, Brains, and Societies Reconstruct the Past (Schacter, D.L., Ed.), pp. 69–90. Cambridge, MA: Harvard University Press.
Medler, D.A. (1998). A brief history of connectionism. Neural Computing Surveys 1(1), 61–101.Google Scholar
Nehaniv, C. & Dautenhahn, K. (1998). Embodiment and memories—Algebras of time and history for autobiographic agents. Proc. 14th European Meeting on Cybernetics and Systems Research Symposium on Embodied Cognition and Artificial Intelligence—Cybernetics and Systems '98, Vol. 2, pp. 651–656, Vienna, Austria.
Nehaniv, C.L. (1999). Meaning for observers and agents. Proc. IEEE Int. Symp. Intelligent Control/Intelligent Systems and Semiotics, ISIC/ISAS '99, pp. 435–440, Cambridge, MA.
O'Reilly, R.C. & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, MA: MIT Press.
Piaget, J. (1971). Psychology and Epistemology: Towards a Theory of Knowledge. New York: Harcourt, Brace.
Qian, L. (1994). Creative design by analogy. PhD Thesis. University of Sydney.
Reed, R. (1993). Pruning algorithms: A survey. IEEE Transactions on Neural Networks 4, 740–747.Google Scholar
Rosenfield, I. (1988). The Invention of Memory: A New View of the Brain. New York: Basic Books.
Rosenman, M.A. & Gero, J.S. (1993). Creativity in design using a design prototype approach. In Modeling Creativity and Knowledge-Based Creative Design (Gero, J.S. & Maher, M.L., Eds.), pp. 119–148. Hillsdale, NJ: Erlbaum.
Rumelhart, D.E. & McClelland, J.L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press.
Russell, S. & Norvig, P. (1995). Artificial Intelligence. Englewood Cliffs, NJ: Prentice–Hall.
Rylatt, R.M. & Czarnecki, C.A. (1998). Beyond physical grounding and naive time: Investigations into short term memory for autonomous agents. In From Animals to Animats, Proc. of the Fifth International Conference of the Society for Adaptive Behaviour, SAB 5 (Pfeifer, R., Ed.), pp. 17–21. Cambridge, MA: MIT Press.
Seifert, C.M., Patalano, A.L., Hammond, K.J., & Converse, T.M. (1997). Experience and expertise: The role of memory in planning for opportunities. In Expertise in Context (Feltovich, P.J., Ford, K.M. & Hoffman, R.R., Eds.), pp. 101–123. Cambridge, MA: MIT Press.
Szykman, S., Sriram, R., Bochenek, C., & Racz, J. (1998). The NIST design repository project. In Advances in Soft Computing: Engineering Design and Manufacturing (Roy, R., Furuhashi, T., Chawdhry, P.K. & Ruggieri, M., Eds.), pp. 5–19. London: Springer–Verlag.
Tait, B. (1997). Constructive internet based learning. Active Learning 7, 3–8.Google Scholar
Williams, P. (1995). Dynamic memory for design. PhD Thesis. University of Sydney.
Ziemke, T. (1999). Rethinking grounding. In Understanding Representation in the Cognitive Sciences (Riegler, A., Peschl, M. & Stein, A., Eds.), pp. 177–190. New York: Plenum.