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Linked Argumentation Graphs for Multidisciplinary Decision Support.

Liang Xiao1, Des Greer2

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.

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Summary
This summary is machine-generated.

This study introduces linked argumentation graphs and patterns to improve multidisciplinary clinical decision-making in complex diseases like cancer. It enhances communication among artificial agents for better medical recommendations.

Keywords:
argument linkingargumentationclinical decision supportmultiagent systemsmultidisciplinary decisions

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Computational Social Science

Background:

  • Multidisciplinary clinical decision-making is crucial for complex diseases due to medical specialization.
  • Multiagent systems (MASs) offer a framework for supporting these decisions.
  • Existing agent-oriented approaches lack systematic support for argumentation in multi-agent communication.

Purpose of the Study:

  • To propose a method for systematic argumentation support in multi-agent systems for clinical decision-making.
  • To introduce linked argumentation graphs and identify recurring argument patterns.
  • To enable versatile multidisciplinary decision applications through enhanced agent communication.

Main Methods:

  • Development of a method based on linked argumentation graphs.
  • Identification of three argumentation patterns: collaboration, negotiation, and persuasion.
  • Demonstration using a case study in breast cancer treatment and lifelong recommendations.

Main Results:

  • A novel approach to modeling argumentation among multiple agents with varying beliefs.
  • Three distinct patterns (collaboration, negotiation, persuasion) for agent interaction.
  • Successful application demonstrated in a breast cancer clinical decision-making scenario.

Conclusions:

  • The proposed method enhances communication and decision-making in multi-agent systems for complex diseases.
  • Linked argumentation graphs and identified patterns support belief revision and persuasion among agents.
  • This framework is applicable to real-world clinical scenarios, such as personalized cancer care.