{"type":"ScholarlyArticle","title":"Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract)","authors":[{"name":"Eoin M. Kenny","slug":"eoin-m-kenny","given":"Eoin M.","family":"Kenny","affiliation":"University College Dublin , Dublin , Ireland; Insight Centre for Data Analytics , UCD , Dublin , Ireland; VistaMilk SFI Research Centre"},{"name":"Elodie Ruelle","slug":"elodie-ruelle","given":"Elodie","family":"Ruelle","affiliation":"Teagasc , Moorepark. Co. Cork , Ireland; VistaMilk SFI Research Centre"},{"name":"Anne Geoghegan","slug":"anne-geoghegan","given":"Anne","family":"Geoghegan","affiliation":"Teagasc , Moorepark. Co. Cork , Ireland"},{"name":"Laurence Shalloo","slug":"laurence-shalloo","given":"Laurence","family":"Shalloo","affiliation":"Teagasc , Moorepark. Co. Cork , Ireland; VistaMilk SFI Research Centre"},{"name":"Micheál O'Leary","slug":"micheal-o-leary","given":"Micheál","family":"O'Leary","affiliation":"Teagasc , Moorepark. Co. Cork , Ireland"},{"name":"Michael O'Donovan","slug":"michael-o-donovan","given":"Michael","family":"O'Donovan","affiliation":"Teagasc , Moorepark. Co. Cork , Ireland"},{"name":"Mohammed Temraz","slug":"mohammed-temraz","given":"Mohammed","family":"Temraz","affiliation":"University College Dublin , Dublin , Ireland; Insight Centre for Data Analytics , UCD , Dublin , Ireland; VistaMilk SFI Research Centre"},{"name":"Mark T. Keane","slug":"mark-t-keane","given":"Mark T.","family":"Keane","affiliation":"University College Dublin , Dublin , Ireland; Insight Centre for Data Analytics , UCD , Dublin , Ireland; VistaMilk SFI Research Centre"}],"abstract":"Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.","doi":"10.24963/ijcai.2020/657","url":"https://ijcai.org/proceedings/2020/657","pdf":"https://ijcai.org/proceedings/2020/0657.pdf","markdown":"https://ijcai.org/proceedings/2020/657.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/657","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":657,"pages":{"first":4740,"last":4744},"keywords":["Machine Learning: Interpretability","Knowledge Representation and Reasoning: Case-based Reasoning","Machine Learning: Explainable Machine Learning","AI Ethics: Explainability"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24380/file_24380.m3u8","length_sec":273,"provider":"hls"}],"references":[{"position":1,"raw":"[Aha et al., 1991] David W Aha, Dennis Kibler, and Marc K Albert. Instance-based learning algorithms. Machine learning, 6(1):37-66, 1991.","title":"Instance-based learning algorithms","authors":"Dennis David W Aha, Marc K Kibler, Albert","year":"1991","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Branting et al., 2001] Karl Branting, John D Hastings, and Jeffrey A Lockwood. CARMA: A case-based range man- agement advisor. In IAAI, pages 3-10, 2001.","title":"CARMA: A case-based range management advisor","authors":"Karl Branting, John D Hastings, Jeffrey A Lockwood","year":"2001","venue":"IAAI","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cho et al., 2012] Yongyun Cho, Kyoungryong Cho, Chang- sun Shin, Jangwoo Park, and Eun-Ser Lee. A recommend service based on expert knowledge model in agricultural environments. In International Conference on Hybrid In- formation Technology, pages 189-194. Springer, 2012.","title":"A recommend service based on expert knowledge model in agricultural environments","authors":"Cho","year":"2012","venue":"International Conference on Hybrid Information Technology","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gafsi et al., 2006] Mohamed Gafsi, Bruno Legagneux, Genevieve Nguyen, and Patrice Robin. Towards sustain- able farming systems: Effectiveness and deficiency of the french procedure of sustainable agriculture. Agricultural Systems, 90(1-3):226-242, 2006.","title":"Towards sustainable farming systems: Effectiveness and deficiency of the french procedure of sustainable agriculture","authors":"Mohamed Gafsi, Bruno Legagneux, Genevieve Nguyen, Patrice Robin","year":"2006","venue":"Agricultural Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gates, 1972] Geoffrey Gates. The reduced nearest neighbor rule (corresp.). IEEE transactions on information theory, 18(3):431-433, 1972.","title":"The reduced nearest neighbor rule (corresp.)","authors":"Geoffrey Gates","year":"1972","venue":"IEEE transactions on information theory","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Goodland et al., 2009] Robert Goodland, Jeff Anhang, et al. Livestock and climate change: What if the key actors in climate change are... cows, pigs, and chickens? World Watch Magazine, 2009.","title":"Livestock and climate change: What if the key actors in climate change are","authors":"Robert Goodland, Jeff Anhang","year":"2009","venue":"cows, pigs, and chickens? World Watch Magazine","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Guan et al., 2009] Donghai Guan, Weiwei Yuan, Young- Koo Lee, and Sungyoung Lee. Nearest neighbor edit- ing aided by unlabeled data. Information Sciences, 179(13):2273-2282, 2009.","title":"Nearest neighbor editing aided by unlabeled data","authors":"Weiwei Donghai Guan, Young-Koo Yuan, Sungyoung Lee, Lee","year":"2009","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hart, 1968] Peter Hart. The condensed nearest neighbor rule (corresp.). IEEE transactions on information theory, 14(3):515-516, 1968.","title":"The condensed nearest neighbor rule (corresp.)","authors":"Peter Hart","year":"1968","venue":"IEEE transactions on information theory","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hurtado-Uria et al., 2013] Cristina Hurtado-Uria, Deirdre Hennessy, Laurence Shalloo, Declan O'Connor, and Luc Delaby. Relationships between meteorological data and grass growth over time in the south of ireland. Irish Geog- raphy, 46(3):175-201, 2013.","title":"Relationships between meteorological data and grass growth over time in the south of ireland","authors":"Cristina Hurtado-Uria, Deirdre Hennessy, Laurence Shalloo, O' Declan, Luc Connor, Delaby","year":"2013","venue":"Irish Geography","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Keane and Kenny, 2019] Mark T Keane and Eoin M Kenny. How case-based reasoning explains neural networks: A theoretical analysis of XAI using post-hoc explanation-by- example from a survey of ANN-CBR twin-systems. In In- ternational Conference on Case-Based Reasoning, pages 155-171. Springer, 2019.","title":"How case-based reasoning explains neural networks: A theoretical analysis of XAI using post-hoc explanation-byexample from a survey of ANN-CBR twin-systems","authors":"T Mark, Eoin M Keane, Kenny","year":"2019","venue":"International Conference on Case-Based Reasoning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kenny and Keane, 2019] Eoin M Kenny and Mark T Keane. Twin-systems to explain artificial neural networks us- ing case-based reasoning: Comparative tests of feature- weighting methods in ANN-CBR twins for XAI. In Twenty-Eighth International Joint Conferences on Artif- ical Intelligence (IJCAI), Macao, 10-16 August 2019, pages 2708-2715, 2019.","title":"Twin-systems to explain artificial neural networks using case-based reasoning: Comparative tests of featureweighting methods in ANN-CBR twins for XAI","authors":"M Eoin, Mark T Kenny, Keane","year":"2019","venue":"Twenty-Eighth International Joint Conferences on Artifical Intelligence (IJCAI)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kenny et al., 2019] Eoin M Kenny, Elodie Ruelle, Anne Geoghegan, Laurence Shalloo, Micheál O'Leary, Michael O'Donovan, and Mark T Keane. Predicting grass growth for sustainable dairy farming: A CBR system us- ing bayesian case-exclusion and post-hoc, personalized explanation-by-example (XAI). In International Confer- ence on Case-Based Reasoning, pages 172-187. Springer, 2019.","title":"Predicting grass growth for sustainable dairy farming: A CBR system using bayesian case-exclusion and post-hoc, personalized explanation-by-example (XAI)","authors":"M Eoin, Elodie Kenny, Anne Ruelle, Laurence Geoghegan, Shalloo, O' Micheál, Leary, O' Michael, Mark T Donovan, Keane","year":"2019","venue":"International Conference on Case-Based Reasoning","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lindblom et al., 2017] Jessica Lindblom, Christina Lund- ström, Magnus Ljung, and Anders Jonsson. Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precision Agriculture, 18(3):309-331, 2017.","title":"Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies","authors":"Lindblom","year":"2017","venue":"Precision Agriculture","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Markovitch and Scott, 1988] Shaul Markovitch and Paul D Scott. The role of forgetting in learning. In Machine Learning Proceedings 1988, pages 459-465. Elsevier, 1988.","title":"The role of forgetting in learning","authors":"Shaul Markovitch, Paul D Scott","year":"1988","venue":"Machine Learning Proceedings 1988","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Murphy, 2007] Kevin P Murphy. Conjugate bayesian anal- ysis of the gaussian distribution. def, 1(2σ2):16, 2007.","title":"Conjugate bayesian analysis of the gaussian distribution","authors":"Kevin P Murphy","year":"2007","venue":"def","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Poux and Aubert, 2018] Xavier Poux and Pierre-Marie Aubert. An agroecological europe in 2050: multifunc- tional agriculture for healthy eating. 2018.","title":"An agroecological europe in 2050: multifunctional agriculture for healthy eating","authors":"Aubert ; Poux, Xavier Poux, Pierre-Marie Aubert","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Redmond and Highley, 2010] Michael A Redmond and Timothy Highley. Empirical analysis of case-editing ap- proaches for numeric prediction. In Innovations in Com- puting Sciences and Software Engineering, pages 79-84. Springer, 2010.","title":"Empirical analysis of case-editing approaches for numeric prediction","authors":"Redmond, Highley ; Michael, A Redmond, Timothy Highley","year":"2010","venue":"Innovations in Computing Sciences and Software Engineering","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ritter et al., 1975] G Ritter, H Woodruff, S Lowry, and T Isenhour. An algorithm for a selective nearest neighbor decision rule (corresp.). IEEE Transactions on Informa- tion Theory, 21(6):665-669, 1975.","title":"An algorithm for a selective nearest neighbor decision rule (corresp.)","authors":"H Ritter, S Woodruff, Lowry, Isenhour","year":"1975","venue":"IEEE Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ruelle et al., 2018] E Ruelle, D Hennessy, and L Delaby. Development of the moorepark st gilles grass growth model (most gg model): A predictive model for grass growth for pasture based systems. European journal of agronomy, 99:80-91, 2018.","title":"Development of the moorepark st gilles grass growth model (most gg model): A predictive model for grass growth for pasture based systems","authors":"Ruelle, L Hennessy, Delaby","year":"2018","venue":"European journal of agronomy","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Smiti and Elouedi, 2011] Abir Smiti and Zied Elouedi. Overview ofmaintenance for case based reasoning sys- tems. International Journal of Computer Applications, 975:8887, 2011.","title":"Overview ofmaintenance for case based reasoning systems","authors":"Abir Smiti, Zied Elouedi","year":"2011","venue":"International Journal of Computer Applications","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Steinfeld et al., 2006] Henning Steinfeld, Pierre Gerber, TD Wassenaar, Vincent Castel, Mauricio Rosales, Mauri- cio Rosales, and Cees de Haan. Livestock's long shadow: environmental issues and options. Food & Agriculture Org., 2006.","title":"Livestock's long shadow: environmental issues and options","authors":"Henning Steinfeld, Pierre Gerber, Vincent Td Wassenaar, Mauricio Castel, Mauricio Rosales, Cees Rosales, De Haan","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sørmo et al., 2005] Frode Sørmo, Jörg Cassens, and Ag- nar Aamodt. Explanation in Case-Based Reason- ing:Perspectives and goals. Artificial Intelligence Review, 24(2):109-143, October 2005.","title":"Explanation in Case-Based Reasoning:Perspectives and goals","authors":"Frode Sørmo, Jörg Cassens, Agnar Aamodt","year":"2005","venue":"Artificial Intelligence Review","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Temraz et al., 2020] Mohammed Temraz, Eoin M. Kenny, Elodie Ruelle, Laurence Shalloo, and Mark T. Keane. Pre- cision agriculture in a time of climate change: Using CBR to predict grass growth for a sustainable agriculture facing disruptive climate events. Insight Tech. Report., 2020.","title":"Precision agriculture in a time of climate change: Using CBR to predict grass growth for a sustainable agriculture facing disruptive climate events","authors":"Mohammed Temraz, Eoin M Kenny, Elodie Ruelle, Laurence Shalloo, Mark T Keane","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wilson, 1972] Dennis L Wilson. Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans- actions on Systems, Man, and Cybernetics, (3):408-421, 1972.","title":"Asymptotic properties of nearest neighbor rules using edited data","authors":"Dennis L Wilson","year":"1972","venue":"IEEE Transactions on Systems, Man, and Cybernetics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}