Explainable Argumentation for Wellness Consultation

Sassoon, Isabel; Kökciyan, Nadin; Sklar, Elizabeth; Parsons, Simon

doi:10.1007/978-3-030-30391-4_11

Cited by 20 publications

(19 citation statements)

References 27 publications

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“…Argumentation is indeed well-suited for explainable reasoning [6,40,70] with argumentative explanations proposed in various settings, see e.g. [1][2][3]5,14,16,18,[21][22][23][24]26,28,31,32,[37][38][39]42,43,51,57,58,62,64,66,69,70,77,79,81,[84][85][86][87][88]90,93,101,103,104,[110][111][112]. We hope to exploit the well-established as well as novel ABA + mechanisms to our advantage of providing various explanations to accompany the decisions supported by ABA + G. In addition to several other future work directions mentioned in Sections 6 and 7, we will aim…”

Section: Discussionmentioning

confidence: 99%

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

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A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.

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Section: Discussionmentioning

confidence: 99%

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

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“…(2018), Sassoon et al . (2019), we can find a decision-making AF for patients that suffer from chronic diseases to help them decide how they can prevent worsening their health. More specifically, the framework involves sensors that record the health state.…”

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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“…In addition, we believe that whilst arguments can encapsulate premises and claims, even in the case where these are instantiated through the use of argument schemes, simply putting forward extensions or collections of arguments falls short of constituting an explanation [20]. We believe that our approach of using explanation templates to translate the contents of an argument and attack into a structured form of natural language is a promising step towards creating good explanations [27].…”

Section: Related Workmentioning

confidence: 99%

“…Now that we have the sets of acceptable arguments and attacks, we can use argument and attack schemes to give rationales behind the existence of arguments and attacks within the argumentation framework. The basic idea is to map the acceptable arguments and attacks into the explanation templates that we introduced in [27]. Definition 12 captures the idea of an explanation template for an argument scheme.…”

Section: Definition 8 (Dung Argumentation Framework Dung Af)mentioning

confidence: 99%

An Argumentation-Based Approach to Generate Domain-Specific Explanations

Kökciyan

Parsons

Sassoon

et al. 2020

Multi-Agent Systems and Agreement Technologies

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In argumentation theory, argument schemes are constructs to generalise common patterns of reasoning; whereas critical questions (CQs) capture the reasons why argument schemes might not generate arguments. Argument schemes together with CQs are widely used to instantiate arguments; however when it comes to making decisions, much less attention has been paid to the attacks among arguments. This paper provides a high-level description of the key elements necessary for the formalisation of argumentation frameworks such as argument schemes and CQs. Attack schemes are then introduced to represent attacks among arguments, which enable the definition of domain-specific attacks. One algorithm is articulated to operationalise the use of schemes to generate an argumentation framework, and another algorithm to support decision making by generating domain-specific explanations. Such algorithms can then be used by agents to make recommendations and to provide explanations for humans. The applicability of this approach is demonstrated within the context of a medical case study.

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Explainable Argumentation for Wellness Consultation

Cited by 20 publications

References 27 publications

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Argumentation and explainable artificial intelligence: a survey

An Argumentation-Based Approach to Generate Domain-Specific Explanations

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