Browse

Introducing Clood CBR: a cloud based CBR framework. (2022)
Conference Proceeding
PALIHAWADANA, C., NKISI-ORJI, I., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Introducing Clood CBR: a cloud based CBR framework. In Reuss, P. and Schönborn, J. (eds.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, pages 233-234. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_108.pdf

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of applications have been built using monolithi... Read More about Introducing Clood CBR: a cloud based CBR framework..

iSee: intelligent sharing of explanation experiences. (2022)
Conference Proceeding
MARTIN, K., WIJEKOON, A., WIRATUNGA, N., PALIHAWADANA, C., NKISI-ORJIC, I., CORSAR, D., DÍAZ-AGUDO, B., RECIO-GARCÍA, J.A., CARO-MARTÍNEZ, M., BRIDGE, D., PRADEEP, P., LIRET, A. and FLEISCH, B. 2022. iSee: intelligent sharing of explanation experiences. In Reuss, P. and Schönborn, J. (eds.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, pages 231-232. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_83.pdf

The right to an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. However, different system stakeholders may have different background knowledge, competencies and goals, thus requiring different kinds of ex... Read More about iSee: intelligent sharing of explanation experiences..

Conceptual modelling of explanation experiences through the iSeeonto ontology. (2022)
Conference Proceeding
CARO-MARTÍNEZ, M., WIJEKOON, A., RECIO-GARCÍA, J.A., CORSAR, D. and NKISI-ORJI, I. 2022. Conceptual modelling of explanation experiences through the iSeeonto ontology. In Reuss, P. and Schönborn, J. (eds.) ICCBR-WS 2022: proceedings of the 30th International conference on Case-based reasoning workshops 2022 (ICCBR-WS 2022) co-located with the 30th International conference on Case-based reasoning 2022 (ICCBR 2022), 12-15 September 2022, Nancy, France. Aachen: CEUR workshop proceedings [online], 3389, pages 117-128. Available from: https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_86.pdf

Explainable Artificial Intelligence is a big research field required in many situations where we need to understand Artificial Intelligence behaviour. However, each explanation need is unique which makes it difficult to apply explanation techniques a... Read More about Conceptual modelling of explanation experiences through the iSeeonto ontology..

Adapting semantic similarity methods for case-based reasoning in the Cloud. (2022)
Conference Proceeding
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Adapting semantic similarity methods for case-based reasoning in the Cloud. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 125-139. Available from: https://doi.org/10.1007/978-3-031-14923-8_9

CLOOD is a cloud-based CBR framework based on a microservices architecture, which facilitates the design and deployment of case-based reasoning applications of various sizes. This paper presents advances to the similarity module of CLOOD through the... Read More about Adapting semantic similarity methods for case-based reasoning in the Cloud..

How close is too close? Role of feature attributions in discovering counterfactual explanations. (2022)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A cas... Read More about How close is too close? Role of feature attributions in discovering counterfactual explanations..