Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

Oliveira, Tiago; Dauphin, Jérémie; Satoh, Ken; Tsumoto, Shusaku; Nováis, Paulo

doi:10.1007/978-3-030-44638-3_11

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…More recently, Oliveira et al (2020) further improved CompGuide to include computational argumentation. This last step is useful when reasoning in cases of multimorbidity.…”

Section: Compguidementioning

confidence: 99%

On the Missing Diagrams in Category Theory

Ochs

2023

Handbook of Abductive Cognition

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Artificial Intelligence and Machine Learning have been widely applied in several areas with the twofold goal of improving people's wellbeing and accelerating computational processes. This may be seen in medical assistance (for example, automatic verification of MRI images), in personal assistants that adapt the content to the user based on his/her preferences or, to optimize query response times in relational databases and accelerate the information retrieval process. Most of Machine Learning algorithms used need a dataset to train on, so that the resulting models can be used, for example, to predict a value or enable user-specific results. Considering predictive methods, when new data arrives a new training of the model may be needed. Speculative Computation is a Machine Learning sub-field that seeks to enable computation to be one step ahead of the user by speculating the value that will be received to be computed. A change in the environment may affect the execution, but the adjustments are rapidly performed. This paper intends to provide an overview of the field of Speculative Computation, describing its main characteristics and advantages, and different scenarios of the medical field in which it is applied. It also provides a critical and comparative analysis with other Machine Learning

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“…More recently, Oliveira et al (2020) further improved CompGuide to include computational argumentation. This last step is useful when reasoning in cases of multimorbidity.…”

Section: Compguidementioning

confidence: 99%

On the Missing Diagrams in Category Theory

Ochs

2023

Handbook of Abductive Cognition

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“…How to best use argumentation theory to generate understandable explanations has become an increasingly important topic in AI regulation and AI design. There have been works [4,7] discussing which standards should an argumentation-based explanation meet and some formal explanations [7,10,18] for different argumentation frameworks are given. For legal systems, users may need explanations with understandable legal information for the acceptance of certain assertions, rather than the acceptance of a set of design elements.…”

Section: Giving Legal Explanationsmentioning

confidence: 99%

An Argumentation-Based Legal Reasoning Approach for DL-Ontology

Yu¹,

Lu²

2022

Preprint

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Ontology is a popular method for knowledge representation in different domains, including the legal domain, and description logics (DL) is commonly used as its description language. To handle reasoning based on inconsistent DL-based legal ontologies, the current paper presents a structured argumentation framework particularly for reasoning in legal contexts on the basis of ASPIC + , and translates the legal ontology into formulas and rules of an argumentation theory. With a particular focus on the design of autonomous vehicles from the perspective of legal AI, we show that using this combined theory of formal argumentation and DL-based legal ontology, acceptable assertions can be obtained based on inconsistent ontologies, and the traditional reasoning tasks of DL ontologies can also be accomplished. In addition, a formal definition of explanations for the result of reasoning is presented.

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“…(2018), Čyras and Oliveira (2019), Oliveira et al . (2018), the authors translate clinical evidence into arguments for an ABA and track patients health state to suggest a treatment in an emergency scenario. Due to the probability distribution which is part of the framework, an ABA which can handle uncertainty can tackle the demands of stochastic framework.…”

Section: Argumentation and Explainable Systemsmentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

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Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

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Goal-Driven Structured Argumentation for Patient Management in a Multimorbidity Setting

Cited by 5 publications

References 23 publications

On the Missing Diagrams in Category Theory

On the Missing Diagrams in Category Theory

An Argumentation-Based Legal Reasoning Approach for DL-Ontology

Argumentation and explainable artificial intelligence: a survey

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