Representing and Indexing Building Refurbishment
Cases for Multiple Retrieval of Adaptable Pieces of
Cases

Farhi Marir and Ian Watson
Department of Surveying, University of Salford, MS 4 WT. UK.


Abstract. CBRefurb is a case-based reasoning (CBR) system for the strategic cost
estimation for building refurbishment. This domain is characterised by many uncertainties
and variation. Its cost estimation involves large amount of interrelated factors whose
impact is difficult to assess. This paper report on the problems faced by the building cost
information Services (BCIS) databases and several rule-based expert systems to tackle
this complex cost estimation problem and, the design and evaluation of CBRefurb system
implemented using ReMind Shell. CBRefurb imitates the domain expert in its approach
of breaking down the whole building work into smaller work (building items) by
organising the refurbishment cases as a hierarchical structure composed of cases and
subcases. The process of estimation imitate the expert by considering only these pieces of
previous cases of similar situation (or context). For this purpose, CBRefurb defines some
of the building and its component (or items) features as a global context and local context
information used to classify cases and subcases into context cases and subcases, and to
decompose the cost estimation problem into adaptable subproblems. This is followed by a
two indexing schemes to suit the hierarchical structure of the case and the problem
decomposition and to allow classfication and retrieval of contextual cases. CBRefurb
features consolidate the aim of the project that is allowing multiple retrieval of
appropriate pieces of the refurbishment which are easier to adapt, reflecting the expert
method of estimating cost for complex refurbishment work.
7.	References

1.	Schank, R. (1982). Dynamic memory: a theory of reminding and learning in
computers and people. Cambridge University Press, Cambridge, UK
2.	Kolodner, J.L. (1993). Case-Based Reasoning. Morgan Kaufmann.
3.	Aamodt, A. & Plaza, E. (1994). Case-Based Reasoning:
Foundational Issues, Methodological Variations, and System Approaches. AI
Communications, 7(i): pp 39-59.
4.	Watson, I. & Marir, F. 1994. Case-Based Reasoning: A Review. The
Knowledge Engineering Review, Vol. 9, No. 4: pp.327-354
5.	Douglas J.F. and Peter S.B. (1991). Cost Planning of Buildings (6th Edition).
BSP Professional Books
6	Marir, F., & Watson, I.D. ,(1995a). CBRefurb: Case-Based Cost Estimation.
Colloquium on Case Based Reasoning: Prospects for applications. Organised by
Professional Group C4 (Artificial Intelligence), 7, March, 1995.
7.	Watson, I.D., & Brandon, P.s. (1992). Strategic Maintenance Prediction: An
Expert System for Facilities Managers. In Facilities Management: Research
Direction, Proc. 2nd. IFMA International Symposium. (Ed. Barett P.).
8.	Pearce, M., Ashok K.G., Kolodner, J.L., Zimring, C. & Billignton, R. (1992)
Case-Based Design Support- A case study in Architectural Design IEEE Expert
Oct. 1992
9.	Hennessy, D. et al.[1992]. Applying Case-Based Reasoning to autoclave
Loading., IEEE Expert,.Vol 17, ppl4-20,pp2l-25.
10.	Redmond, M. A. 1992. Learning by observing and understanding expert
problem solving. Georgia Institute of Technology College of Computing
Technical Report no. GIT-CC-92/43. Atlanta
11.	Sycara, K. [1987] . Resolving Adversial Conflicts: An approach Integrating
cases-Based Reasoning and Analytic Methods. PhD thesis , School of
Information and Computer Science, Georgia Institute of Technology.
12	Hammond, K.J. (1986). CHEF: A Model of Case-Based Planning. In Proc.
American Association for Artificial Intelligence, AAAI-86, August 1986.
Philadelphia, PA, US.
13.	Barletta, R. & Mark, W.,(1988). Explanation-Based Indexing of Cases.In
DARPA88 Proceeding, see Kolodner J.L.,(Ed.) 1988.
14	Marir, F., & Watson, I.D. (1995b). Can CBR imitate human intelligence and
are such systems easy to design and maintain? A critique. Proceedings of the
First UK CBR Workshop, 12th January at the University of Salford. (Ed.
Watson I.D., F. Marir & Perera S.)
15.	Lebowitz, M., (1987). Experimental with incremental concept formation:
UNIMEM. Machine Learning, 2(ii): pp 1 03-38.
16	Navichandra, D. (1991). Exploration and innovation in design:towards a
computational model. Springer Verlag, New York. NY, US.
17	Maher, M.L. & Zhang, D.M. (1991): CADSYN: using case and
decomposition knowledge for design synthesis. In Artificial Intelligence in
Design, Gero, J.S. (ed.), Butterworth-Heinmann. Oxford. UK
18	Domeshek, E., (1993). A case study of case indexing: Designing index feature
sets to suit task demands and support parallelism. In, Advances in
connectionnist and neural computation theory, Vol.2: Analogical connections,
eds. J. Barenden and K. Holyoak, Norwood, NJ. US.
19	Smyth, B., & Keane M.T., [1993]. Retyrieving Adaptable Knowledge in Case
Retrieval. EWCBR-93, 1st Worksop on CBR, Vol2, 1-5Nov. 93, Germany
