Now showing items 1-20 of 28

  • Applying genetic algorithms to multi-objective land use planning 

    Matthews, Keith B.; Craw, Susan; Elder, S.; Sibbald, A.; MacKenzie, I. (Morgan Kaufmann (now Elsevier) http://www.elsevier.com, 2000-07)
    MATTHEWS, K., CRAW, S., ELDER, S., SIBBALD, A. and MACKENZIE, I. 2000. Applying genetic algorithms to multi-objective land use planning. In: Proceedings of the Genetic and Evolutionary Computation Conference. 8-12 July 2000. Las Vegas, Nevada: Riviera Hotel and Casino. pp.613-620.
    This paper explores the application of multiobjective Genetic Algorithms (mGAs) to rural land use planning, a spatial allocation problem. Two mGAs are proposed. Both share an underlying structure of: fitness assignment ...
  • Automatically acquiring structured case representations: the SMART way. 

    Asiimwe, Stella; Craw, Susan; Wiratunga, Nirmalie; Taylor, Bruce J. (Springer Verlag. http://dx.doi.org/10.1007/978-1-84800-086-5_4, 2008)
    ASIIMWE, S., CRAW, S., WIRATUNGA, N. and TAYLOR, B., 2008. Automatically acquiring structured case representations: the SMART way. In: R. ELLIS, T. ALLEN and M. PETRIDIS. Applications and Innovations in Intelligent Systems XV: Proceedings of AI- 2007: The Twenty-Seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. London: Springer Verlag. Pp. 45-58.
    Acquiring case representations from textual sources remains an interesting challenge for CBR research. Approaches based on methods in information retrieval require large amounts of data and typically result in ...
  • Case-base maintenance with multi-objective evolutionary algorithms. 

    Lupiani, Eduardo; Massie, Stewart; Craw, Susan; Juarez, Jose M.; Palma, Jose T. (Springer https://dx.doi.org/10.1007/s10844-015-0378-z, 2015-09-21)
    LUPIANI, E., MASSIE, S., CRAW, S., JUAREZ, J.M. and PALMA, J., 2016. Case-base maintenance with multi-objective evolutionary algorithms. Journal of Intelligent Information Systems, Vol 46(2), pp. 259-284.
    Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is ...
  • Case-based reasoning for matching SMARTHOUSE technology to people's needs 

    Wiratunga, Nirmalie; Craw, Susan; Taylor, Bruce J. (Elsevier http://www.elsevier.com/wps/find/journaldescription.cws_home/525448/description#description, 2004)
    WIRATUNGA, N., CRAW, S. and TAYLOR, B., 2004. Case-based reasoning for matching SMARTHOUSE technology to people's needs. Knowledge based systems, 17 (2-4), pp. 139-146
    SMARTHOUSE technology offers devices that help the elderly and people with disabilities to live independently in their homes. This paper presents our experiences from a pilot project applying case-based reasoning techniques ...
  • Cold-start music recommendation using a hybrid representation. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart (dot.rural http://www.dotrural.ac.uk/digitalfutures2012/session5, 2012-10-25)
    HORSBURGH, B., CRAW, S. and MASSIE, S., 2012. Cold-start music recommendation using a hybrid representation. Available from: http://www.dotrural.ac.uk/digitalfutures2012/session5 [Accessed 27 September 2013]
    Digital music systems are a new and exciting way to dis- cover, share, and listen to new music. Their success is so great, that digital downloads are now included alongside tra- ditional record sales in many o cial music ...
  • Complexity modelling for case knowledge maintenance in case-based reasoning. 

    Massie, Stewart (The Robert Gordon University School of Computing, 2006-12)
    Case-based reasoning solves new problems by re-using the solutions of previously solved similar problems and is popular because many of the knowledge engineering demands of conventional knowledge-based systems are removed. ...
  • Consultant-2: Pre- and post-processing of machine learning applications. 

    Sleeman, D.; Rissakis, M.; Craw, Susan; Graner, N.; Sharma, S. (Elsevier http://dx.doi.org/10.1006/ijhc.1995.1035, 1995-07)
    SLEEMAN, D., RISSAKIS, M., CRAW, S., GRANER, N. and SHARMA, S., 1995. Consultant-2: Pre- and post processing of machine learning applications. International Journal of Human Computer Studies, 43 (1), pp. 43-63
    The knowledge acquisition bottleneck in the development of large knowledge-based applications has not yet been resolved. One approach which has been advocated is the systematic use of Machine Learning (ML) techniques. ...
  • Debugging knowledge-based applications with a generic toolkit 

    Craw, Susan; Boswell, Robin (IEEE, 2000-11)
    CRAW, S. and BOSWELL, R. 2000. Debugging knowledge-based applications with a generic toolkit. In: Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence. 2000. Vancouver, Canada. pp182-185.
    Knowledge refinement tools assist in the debugging and maintenance of knowledge based systems (KBSs) by attempting to identify and correct faults in the knowledge that account for incorrect problem-solving. Most ...
  • Design, innovation and case-based reasoning 

    Goel, Ashok K.; Craw, Susan (Cambridge University Press http://dx.doi.org/10.1017/S0269888906000609, 2005)
    GOEL, A. and CRAW, S., 2005. Design, innovation and case-based reasoning. Knowledge Engineering Review, 20 (3), pp. 271-276
    The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning ...
  • Finding the hidden gems: recommending untagged music. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart; Boswell, Robin (AAAI Press/ International Joint Conferences on Artificial Intelligence., 2011)
    HORSBURGH, B., CRAW, S., MASSIE, S. and BOSWELL, R., 2011. Finding the hidden gems: recommending untagged music. In: WALSH, T., ed. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. 16-22 July 2011. Menlo Park, California: AAAI Press/ International Joint Conferences on Artificial Intelligence. Pp. 2256-2261
    We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts ...
  • Integrating content and semantic representations for music recommendation. 

    Horsburgh, Ben (Robert Gordon University School of Computing Science, 2013-07)
    Music recommender systems are used by millions of people every day to discover new and exciting music. Central to making recommendations is the representation of each track, which may be used to calculate similarity. ...
  • A knowledge acquisition tool to assist case authoring from texts. 

    Asiimwe, Stella Maris (The Robert Gordon University School of Computing, 2009-03)
    Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without ...
  • Knowledge modelling for a generic refinement framework 

    Boswell, Robin; Craw, Susan (Elsevier http://dx.doi.org/10.1016/S0950-7051(99)00018-0, 1999-10)
    BOSWELL, R. and CRAW, S., 1999. Knowledge modelling for a generic refinement framework. Knowledge Based Systems, 12 (5-6), pp. 317-325
    Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools ...
  • Learning adaptation knowledge to improve case-based reasoning 

    Craw, Susan; Wiratunga, Nirmalie; Rowe, Ray (Elsevier http://dx.doi.org/10.1016/j.artint.2006.09.001, 2006-11)
    CRAW, S., WIRATUNGA, N. and ROWE, R., 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170 (16-17), pp. 1175-1192.
    Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, ...
  • Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart (Elsevier http://dx.doi.org/10.1016/j.artint.2014.11.004, 2015-02)
    HORSBURGH, B., CRAW, S. and MASSIE, S., 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial Intelligence, 219, pp. 25-39.
    Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems however suffer from tag sparsity, whereby tracks ...
  • Maintaining retrieval knowledge in a case-based reasoning system. 

    Craw, Susan; Jarmulak, Jacek; Rowe, Ray (Blackwell http://www.blackwellpublishing.com/journal.asp?ref=0824-7935&site=1, 2001-05)
    CRAW, S., JARMULAK, J. and ROWE, R., 2001. Maintaining retrieval knowledge in a case-based reasoning system. Computational Intelligence, 17 (2), pp. 346-363.
    The knowledge stored in a case-base is central to the problem-solving of a Case-Based Reasoning (CBR) system. Therefore, case-base main- tenance is a key component of maintaining a CBR system. However, other knowledge ...
  • A multi-objective evolutionary algorithm fitness function for case-base maintenance. 

    Lupiani, Eduardo; Craw, Susan; Massie, Stewart; Juarez, Jose M.; Palma, Jose T. (Springer. http://dx.doi.org/10.1007/978-3-642-39056-2_16, 2013-07)
    LUPIANI, E., CRAW, S., MASSIE, S., JUAREZ, J. M. and PALMA, J. T., 2013. A multi-objective evolutionary algorithm fitness function for case-base maintenance. In: S. J. DELANY and S. ONTANON, eds. Case-Based Reasoning Research and Development: Proceedings of the 21st International Conference, ICCBR 2013. 8-11 July 2013. Berlin: Springer. Pp. 218-232.
    Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a ...
  • Music recommendation: audio neighbourhoods to discover music in the long tail. 

    Craw, Susan; Horsburgh, Ben; Massie, Stewart (Springer https://dx.doi.org/10.1007/978-3-319-24586-7_6, 2015-11-26)
    CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommendation: audio neighbourhoods to discover music in the long tail. Lecture notes in computer science [online], 9343, Proceedings of the 23rd international conference on case-based reasoning (ICCBR 2015), pages 73-87. Available from: https://dx.doi.org/10.1007/978-3-319-24586-7_6
    Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists ...
  • Music recommenders: user evaluation without real users? 

    Craw, Susan; Horsburgh, Ben; Massie, Stewart (AAAI/International Joint Conferences on Artificial Intelligence (IJCAI) http://ijcai.org/papers15/Papers/IJCAI15-249.pdf, 2015-07)
    CRAW, S., HORSBURGH, B. and MASSIE, S., 2015. Music recommenders: user evaluation without real users? In: Q. YANG and M. WOOLDRIDGE, eds. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. 25-31 July 2015. [online] Palo Alto: AAAI/IJCAI pp. 1749-1755. Available from: http://ijcai.org/papers15/contents.php [Accessed 10 August 2015]
    Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can ...
  • Music-inspired texture representation. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart (AAAI Press. http://www.aaai.org/Press/Proceedings/aaai12.php, 2012-07)
    HORSBURGH, B., CRAW, S. and MASSIE, S., 2012. Music-inspired texture representation. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12). 22-26 July 2012. Palo Alto, CA: AAAI Press. Pp. 52-58.
    Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most ...