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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects:
In the developed world we are surrounded by man-made objects, but most people give little thought to the complex processes needed for their design. The design of hand knitting is complex because much of the domain knowledge is tacit. The objective of this thesis is to devise a methodology to help designers to work within design constraints, whilst facilitating creativity. A hybrid solution including computer aided design (CAD) and case based reasoning (CBR) is proposed. The CAD system creates designs using domain-specific rules and these designs are employed for initial seeding of the case base and the management of constraints. CBR reuses the designer's previous experience. The key aspects in the CBR system are measuring the similarity of cases and adapting past solutions to the current problem. Similarity is measured by asking the user to rank the importance of features; the ranks are then used to calculate weights for an algorithm which compares the specifications of designs. A novel adaptation operator called rule difference replay (RDR) is created. When the specifications to a new design is presented, the CAD program uses it to construct a design constituting an approximate solution. The most similar design from the case-base is then retrieved and RDR replays the changes previously made to the retrieved design on the new solution. A measure of solution similarity that can validate subjective success scores is created. Specification similarity can be used as a guide whether to invoke CBR, in a hybrid CAD-CBR system. If the newly resulted design is suffciently similar to a previous design, then CBR is invoked; otherwise CAD is used. The application of RDR to knitwear design has demonstrated the flexibility to overcome deficiencies in rules that try to automate creativity, and has the potential to be applied to other domains such as interior design.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [13] Gavin Finnie and Zhaohao Sun. R 5 model for case-based reasoning. Knowledge-Based Systems, 16(1):5965, 2003.
    • [14] Paul Richards and A EkÆrt. Hierarchical case based reasoning to support knitwear design. CIRP Journal of Manufacturing Science and Technology , 2(4):299309, 2010.
    • [15] David Wilson. CBR Noir. In AIII08, 2008. Retrieved from http://www.aivideo.org/2008/acceptedvideos.html, 6 February 2011.
    • [16] L Wittgenstein. Philosophical investigations . Blackwell, 1953.
    • [17] Janet. L. Kolodner and Robert L. Simpson. The mediator: Analysis of an early case-based problem solver. Cogni, 13(4):507549, 1989.
    • [18] William M. Bain. A case-based reasoning system for subjective assessment. In Proceedings of AIII08, pages 523527, 1986.
    • [19] Kristian J. Hammond. Chef: A model of case-based planning. In Proceedings of the Fifth National Conference on Articial Intelligence (AAAI-86) , 1986.
    • [20] Phyllis Koton. Using Experience in Learning and Problem Solving . PhD thesis, Massachusetts Institute of Technology, 1988.
    • [21] Phyllis Koton. A medical reasoning program that improves with experience. Computer Methods and Programs in Biomedicine , 30(2-3):177184, 1989.
    • [22] E. Ray Bareiss, Bruce W Porter, and Craig C. Wier. Protos: an exemplar-based learning apprentice. International Journal of Man-Machine Studies , 29(5):549561, 1988.
    • [23] David B. Leake. Creativity by case-based reasoning (CBR): Swale project home page. Accessed from http://www.cs.indiana.edu/ leake/projects/swale/ on 6 February 2011.
    • [24] Roger C. Schank and David B. Leake. Creativity and learning in a case-based explainer. Articial Intelligence, 40(1-3):353385, 1989.
    • [26] Bradley P. Allen and S. Daniel Lee. A knowledge-based environment for the development of software parts composition systems. In Proc. 11th ICSE, pages 104112, 1989.
    • [27] S.Daniel Lee, Trung D Nguyen, and Mary P. Czerwinski. Integration of case-based search engine into help database (Inference Corporation patent 5701399), 1997.
    • [28] Problem-solving software. Technical Report 20020083260, NASA, 1992.
    • [29] William Cheetham and John Graf. Case-based reasoning in color matching. In ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development , pages 112, 1997.
    • [30] William Cheetham. Tenth anniversary of the plastics color formulation tool. AI Magazine, 26(3):51 61, 2005.
    • [31] Xijun Wang. A web-based case-based reasoning tool. Master's thesis, University of Wyoming, 2000.
    • [32] Cynthia Marling, Mohammed Sqalli, Edwina Rissland, Hector Muaeoz-Avila, and David Aha. Casebased reasoning integrations. AI Magazine, 23(1):6986, 2002.
    • [33] Andrew M. Dearden and Derek G. Bridge. Choosing a knowledge based system to support a help desk. Knowledge Engineering Review , 8(3):201222, 1993.
    • [34] R.E. Fikes and N.J. Nilsson. STRIPS: A new approach to the application of theorem proving to problem solving. In 2nd International Joint Conference on Articial Intelligence , pages 608620, 1971.
    • [36] H Munoz-Avila and M.T. Cox. Case-based plan adaptation: An analysis and review. Intelligent Systems, 23(4):75 81, 2008.
    • [37] Santi Ontaaen, Kane Bonnette, Praful Mahindrakar, Marco Gmez-Martin, Katie Long, Jai Radhakrishnan, Rushabh Shah, and Ashwin Ram. Learning from human demonstrations for realtime case-based planning. In Proceedings of the IJCAI-09 Workshop on Learning Structural Knowledge from Observations , 2009.
    • [38] Bernhard Nebel and Jana Koehler. Plan reuse versus plan generation: A theoretical and empirical analysis. Articial Intelligence , 76:427454, 1995.
    • [39] Claudia Eckert. The communication bottleneck in knitwear design: Analysis and computing solutions. Computer Supported Cooperative Work , 10:2974, 2001.
    • [40] Janet L. Kolodner. Improving human decision making through case-based decision aiding. Magazine, 12(2):5268, 1991.
    • [54] Manuela M. Veloso and Jaime G. Carbonell. Derivational analogy in prodigy: Automating case acquisition, storage and utilization. Machine Learning, 10:249278, 1993.
    • [55] Mykola Galushka and David Patterson. Intelligent index selection for case-based reasoning. Knowledge-Based Systems, 19:625638, 2006.
    • [56] Barry Smyth and PÆdraig Cunningham. The utility problem analysed a case-based reasoning perspective. In Proceedings of the Third European Workshop on Case-Based Reasoning , pages 392 399, 1996.
    • [57] Janet Kolodner. Case-Based Reasoning. Morgan Kaufmann Publishers Inc, 1993.
    • [58] J.R. Quinlan. Induction of decision trees. Machine Learning, 1:81106, 1986.
    • [59] PÆdraig Cunningham. A taxonomy of similarity mechanisms for case-based reasoning. Technical report, University College Dublin, 2008.
    • [74] Leo Breiman. Random forests. Machine Learning, 45:532, 2001.
    • [75] Tor Gunnar Houeland. An ecient random decision tree algorithm for case-based reasoning systems. In Twenty-Fourth International FLAIRS Conference , pages 401406, 2011.
    • [76] Brian Sheppard. World-championship-caliber scrabble. Articial Intelligence , 134(1-2):241275, 2002.
    • [77] Alina Beygelzimer, Sham Kakade, and John Langford. Cover trees for nearest neighbor. In Proceedings of the 23rd International Conference on Machine Learning , 2006.
    • [78] Barry Smyth and Elizabeth McKenna. Footprint-based retrieval. In Proceedings of the Third International Conference on Case-Based Reasoning and Development (ICCBR '99) , pages 343 357, 1999.
    • [79] Mario Lenz, Hans-Dieter Burkhard, and Sven Brckner. Applying case retrieval nets to diagnostic tasks in technical domains. In In Proceedings of the Third European Workshop on Case-Based Reasoning, pages 219233, 1996.
    • [80] Evangelos Simoudis and James Miller. Validated retrieval in case-based reasoning. In Proceedings of the Eighth National Conference on Articial Intelligence , pages 310315, 1990.
    • [81] Katy Brner. Structural similarity as guidance in case-based design. In European Workshop on Case-Based Reasoning , pages 197208, 1993.
    • [90] Mary Lou Maher and Dong Mei Zhang. CADSYN: A case-based design process model. Articial Intelligence for Engineering, Design, Analysis and Manufacturing , 7:97110, 1993.
    • [91] Mary Lou Maher. Engineering design synthesis: A domain independent representation. Articial Intelligence for Engineering, Design, Analysis and Manufacturing (AI EDAM) , 1:207213, 1987.
    • [92] Edmund K. Burke, Bart L. MacCarthy, Sanja Petrovic, and Rong Qu. Multiple-retrieval case-based reasoning for course timetabling problems. Journal of Operations Research Society , 57(2):148162, 2005.
    • [107] Juan JosØ Bello-TomÆs, Pedro A. GonzÆlez-Calero, and BelØn Daz-Agudo. JColibri: An objectoriented framework for building CBR systems. In P.A. GonzÆlez-Calero and P. Funk, editors, ECCBR 2004, pages 3246, 2004.
    • [124] Bruce W. Porter, Ray Bareiss, and Robert Holte. Concept learning and heuristic classication in weak-theory domains. Articial Intelligence , 45(1-2):229263, 1990.
    • [125] John Stillwell. Mathematics and Its History . Springer, 2010.
    • [126] Martin Mller. Computer Go. Articial Intelligence , 134:145179, 2002.
    • [160] Jung-Eun Lee, Rong Jin, Anil K. Jain, and Wei Tong. Image retrieval in forensics: Tattoo image database application. IEEE Multimedia in Forensics, Security, and Intelligence , 19(1):4049, 2012.
    • [162] Facundo MØmoli and Guillermo Sapiro. Comparing point clouds. In SGP '04 Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing , pages 3240, 2004.
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