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Isabel Sassoon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

JAIR Journal 2024 Journal Article

Computational Argumentation-based Chatbots: A Survey

Federico Castagna
Nadin Kökciyan
Isabel Sassoon
Simon Parsons
Elizabeth Sklar

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

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IS Journal 2021 Journal Article

Applying Metalevel Argumentation Frameworks to Support Medical Decision Making

Nadin Kokciyan
Isabel Sassoon
Elizabeth Sklar
Sanjay Modgil
Simon Parsons

People are increasingly employing artificial intelligence as the basis for decision-support systems (DSSs) to assist them in making well-informed decisions. Adoption of DSS is challenging when such systems lack support, or evidence, for justifying their recommendations. DSSs are widely applied in the medical domain, due to the complexity of the domain and the sheer volume of data that render manual processing difficult. This article proposes a metalevel argumentation-based decision-support system that can reason with heterogeneous data (e. g. , body measurements, electronic health records, clinical guidelines), while incorporating the preferences of the human beneficiaries of those decisions. The system constructs template-based explanations for the recommendations that it makes. The proposed framework has been implemented in a system to support stroke patients and its functionality has been tested in a pilot study. User feedback shows that the system can run effectively over an extended period.

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EUMAS Conference 2020 Conference Paper

An Argumentation-Based Approach to Generate Domain-Specific Explanations

Nadin Kökciyan
Simon Parsons
Isabel Sassoon
Elizabeth Sklar
Sanjay Modgil

Abstract 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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AAMAS Conference 2019 Conference Paper

Computational Argumentation-based Clinical Decision Support

Martin Chapman
Panagiotis Balatsoukas
Mark Ashworth
Vasa Curcin
Nadin Kökciyan
Kai Essers
Isabel Sassoon
Sanjay Modgil

This demonstration highlights the design of the Consult system, a modular decision-support system (DSS) intended to help patients suffering from chronic conditions self-manage their treatments. The system takes input from multiple sources, including commercial wellness sensors and a patient’s electronic health record, to inform a computational argumentation engine that constructs weighted opinions using these inputs and knowledge about their sources, and uses an interaction agent driven by argumentation-based dialogue to respond to user queries.

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