Now showing items 1-14 of 14

  • 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 situation awareness. 

    Nwiabu, Nuka D.; Allison, Ian K.; Holt, Patrik; Lowit, Peter; Oyeneyin, Babs (IEEE http://dx.doi.org/10.1109/CogSIMA.2012.6188388, 2012)
    NWIABU, N., ALLISON, I., HOLT, P., LOWIT, P. and OYENEYIN, B., 2012. Case-based situation awareness. In: IEEE Multi-disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. 6-8 March 2012. Piscataway, New Jersey: IEEE. Pp. 22-29.
    Situation-aware case-based decision support (SACBDS) systems comprise two distinct parts: situation awareness (SA) and case-based reasoning (CBR). The SA part keeps a finite history of the time space information of the ...
  • 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. ...
  • A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. 

    Martin, Kyle; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Clos, Jérémie (ICCBR (Organisers) http://ceur-ws.org/Vol-2028, 2017-06-26)
    MARTIN, K., WIRATUNGA, N., SANI, S., MASSIE, S. and CLOS, J. 2017. A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Proceedings of the 25th International case-based reasoning conference (ICCBR 2017): case-based reasoning and deep learning workshop (CBRDL 2017), 26-28 June 2017, Trondheim, Norway. Trondheim: ICCBR [online], pages 85-94. Available from: http://ceur-ws.org/Vol-2028/paper8.pdf
    The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to ...
  • Informed pair selection for self-paced metric learning in Siamese neural networks. 

    Martin, Kyle; Wiratunga, Nirmalie; Massie, Stewart; Clos, Jérémie (Springer https://doi.org/10.1007/978-3-030-04191-5_3, 2018-11-16)
    MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3
    Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning ...
  • 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 ...
  • 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, ...
  • 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 ...
  • Maintenance of case bases: current algorithms after fifty years. 

    Juarez, Jose M.; Craw, Susan; Lopez-Delgado, J. Ricardo; Campos, Manuel (International Joint Conferences on Artificial Intelligence https://doi.org/10.24963/ijcai.2018/770 http://www.ijcai.org, 2018-07-13)
    JUAREZ, J.M., CRAW, S., LOPEZ-DELGADO, J.R. and CAMPOS, M. 2018. Maintenance of case bases: current algorithms after fifty years. In Lang, J. (ed.) Proceedings of the 27th International joint conference on artificial intelligence (IJCAI-18), 13-19 July 2018, Stockholm, Sweden. Freiburg: IJCAI [online], pages 5457-5463. Available from: https://doi.org/10.24963/ijcai.2018/770
    Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from modelbased systems since it can learn incrementally as new data is available, storing new ...
  • Personalised human activity recognition using matching networks. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-030-01081-2_23, 2018-10-09)
    SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Personalised human activity recognition using matching networks. In Cox, M.T., Funk, P. and Begum, S. (eds.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], pages 339-353. Available from: https://doi.org/10.1007/978-3-030-01081-2_23
    Human Activity Recognition (HAR) is typically modelled as a classification task where sensor data associated with activity labels are used to train a classifier to recognise future occurrences of these activities. An ...
  • Plan recommendation for well engineering. 

    Thomson, Richard; Massie, Stewart; Craw, Susan; Ahriz, Hatem; Mills, Ian (Springer http://dx.doi.org/10.1007/978-3-642-21827-9_45, 2011-07)
    THOMSON, R., MASSIE, S., CRAW, S., AHRIZ, H. and MILLS, I., 2011. Plan recommendation for well engineering. In: K. G. MEHOTRA, C. K. MOHAN, J. C. OH, P. K. VARSHNEY and M. ALI, eds. Modern Approaches in Applied Intelligence: Proceedings of the 24th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Part II. 28 June – 1 July 2011. Berlin: Springer. Pp. 436-445.
    Good project planning provides the basis for successful offshore well drilling projects. In this domain, planning occurs in two phases: an onshore phase develops a project plan; and an offshore phase implements the plan ...
  • Risk information recommendation for engineering workers. 

    Martin, Kyle; Liret, Anne; Wiratunga, Nirmalie; Owusu, Gilbert; Kern, Mathias (Springer https://doi.org/10.1007/978-3-030-04191-5_27, 2018-11-16)
    MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Risk information recommendation for engineering workers. In Bramer, M. and PETRIDIS, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's specialist group on artificial intelligence (SGAI) annual international artificial intelligence conference (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 311-325. Available from: https://doi.org/10.1007/978-3-030-04191-5_27
    Within any sufficiently expertise-reliant and work-driven domain there is a requirement to understand the similarities between specific work tasks. Though mechanisms to develop similarity models for these areas do exist, ...
  • Self-optimising CBR retrieval 

    Jarmulak, Jacek; Craw, Susan; Rowe, Ray (IEEE, 2000-11)
    JARMULAK, J., CRAW, S. and ROWE, R. 2000. Self-optimising CBR retrieval. In: Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence. 13-15 November 2000. Vancouver, Canada. pp.376-383.
    One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effortmay be demanding. In this paper we ...
  • Situation awareness in context-aware case-based decision support. 

    Nwiabu, Nuka D.; Allison, Ian K.; Holt, Patrik; Lowit, Peter; Oyeneyin, Babs (IEEE http://dx.doi.org/10.1109/COGSIMA.2011.5753761, 2011-02)
    NWIABU, N., ALLISON, I., HOLT, P., LOWIT, P. and OYENEYIN, B., 2011. Situation awareness in context-aware case-based decision support. In: IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2011). 22-24 February 2011. Piscataway, New Jersey: IEEE. Pp. 9-16
    Humans naturally reuse recalled knowledge to solve problems and this includes understanding the information that identify or characterize these problems (context), and the situation. Context-aware case-based reasoning ...