A Weighted Bipolar Argumentation Framework and its ASP-based Implementation

Overview

Join us as we explore a groundbreaking approach to argumentation with the Weighted Bipolar Argumentation Framework (WBAF). This advanced system adeptly manages both attack and support relationships between arguments by incorporating argument weights, providing a more nuanced understanding of the dynamics involved.

Gain insights into the ASP-based implementation tool specifically designed for generating minimum-penalty preferred extensions.

This tool represents a significant innovation in computational argumentation, allowing for more efficient and accurate analysis.

Perfect for enthusiasts and professionals in artificial intelligence and computer science, this course offers in-depth knowledge on a cutting-edge topic in the field.

Syllabus

• Introduction to Argumentation Frameworks
Overview of classical argumentation
Bipolar argumentation frameworks (BAF)
• Weighted Bipolar Argumentation Framework (WBAF)
Definition and structure of WBAF
Handling attack and support relations
Assigning and interpreting argument weights
• Theoretical Foundations
Semantics of WBAF
Preferred extensions and penalty minimization
Comparison with traditional frameworks
• ASP-based Implementation
Introduction to Answer Set Programming (ASP)
Using ASP to model WBAF
Generating minimum-penalty preferred extensions
• Tool Utilization and Applications
Installation and setup of ASP tools
Case studies: Applying WBAF in real-world scenarios
Performance analysis of the WBAF ASP tool
• Advanced Topics
Extensions and variations of WBAF
Research trends and future directions
• Course Project
Designing a WBAF model for a selected problem
Implementing the model using ASP
Analyzing results and discussing findings
• Conclusion
Recap of key concepts
Further reading and resources
• Assessment and Evaluation
Quizzes and assignments
Final project presentation and report

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Subjects

Computer Science