Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Kenny, Eoin M.; Ruelle, E.; Geoghegan, A.; Shalloo, L.; O'Leary, M. J.; O’Donovan, Michael; Keane, Mark T.

doi:10.1007/978-3-030-29249-2_12

Cited by 14 publications

(4 citation statements)

References 26 publications

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“…We also want to integrate RustOnt as part of a case-based reasoning (CBR) system widely used in agriculture for crop management [3], traceability [73], yield estimation [74], and pest and disease protection [75][76][77]. RustOnt can improve the retrieval process of a CBR by making the input data less heterogeneous and thus more accurate, so that the search for the most similar case can be more precise according to the system requirements.…”

Section: Discussionmentioning

confidence: 99%

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Suárez

Griol

Figueroa

et al. 2022

Sensors

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Crop disease management in smart agriculture involves applying and using new technologies to reduce the impact of diseases on the quality of products. Coffee rust is a disease that factors such as poor agronomic management activities and climate conditions may favor. Therefore, it is crucial to identify the relationships between these factors and this disease to learn how to face its consequences and build intelligent systems to provide appropriate management or help farmers and experts make decisions accordingly. Nevertheless, there are no studies in the literature that propose ontologies to model these factors and coffee rust. This paper presents a new ontology called RustOnt to help experts more accurately model data, expressions, and samples related to coffee rust and apply it whilst taking into account the geographical location where the ontology is adopted. Consequently, this ontology is crucial for coffee rust monitoring and management by means of smart agriculture systems. RustOnt was successfully evaluated considering quality criteria such as clarity, consistency, modularity, and competence against a set of initial requirements for which it was built.

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Section: Discussionmentioning

confidence: 99%

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Suárez

Griol

Figueroa

et al. 2022

Sensors

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“…Complementing DSS functionality with explanations is a relevant research topic with a broad spectrum of applications, ranging from agriculture [28] to medical systems [29] and information security [30]. As each application scenario has specific explanation requirements and processing strategies, the works closest to our proposal are presented next.…”

Section: Main Influencing Concepts and Approachesmentioning

confidence: 99%

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Oliveira

Neto

2023

IEEE Access

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The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use of intelligent black-box models can hinder the decision support system's potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers. Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility. Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e. decide with more efficiency as well as efficacy). The new approach is accompanied by two proofs of concept in the health and public security areas. Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented. The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.

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“…Explanations generated from CBR systems are important so the end users are encouraged to use and adopt them. As Kenny et al mention [9], adoption barriers can be addressed by the explanation capabilities designed to improve adoption, such as adequate predictions and providing "personalised explanation-byexample". They identify three main challenges that systems have.…”

Section: Related Workmentioning

confidence: 99%

Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study

Marín-Veites

Bach

2022

Case-Based Reasoning Research and Development

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Explainability in AI is becoming increasingly important as we delegate more safety-critical tasks to intelligent decision support systems. Case-Based Reasoning (CBR) systems are one way to build such systems. Understanding how results are created by a CBR system has become an important task in their development process. In this work, we present how visualizations can help developers and domain experts to evaluate the CBR systems behavior and provide insights to further develop CBR systems in their application scenarios. This paper presents an overview of SupportPrim, a CBR system for the management of musculoskeletal pain complaints, and presents methods that explain its retrieval and similarity measures through visualizations that help to evaluate the system's performance. In the case study, we conduct experiments within the SupportPrim CBR system using differently weighted global similarity measures to compare their effect on the retrieval. This work shows that providing suitable explanations for the CBR system's stakeholders increases the likelihood of its adoption, and visualizations allow the creation of different explanations for the different users throughout the development phase, thus allowing for better modeling and usage of the system.

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Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Cited by 14 publications

References 26 publications

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study

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