Composite Argumentation Systems with ML Components

Today AI systems are rarely made without Machine Learning (ML) and this inspires us to explore what aptly called composite argumentation with ML components. Concretely, against two theoretical backdrops of PABA (Probabilistic Assumption-based Argumentation) DST (Dempster-Shafer Theory), we present a framework for such c-PABA. It is argued that c-PABA lends itself development tool as well demonstrate show DST-based classifier combination multi-source data fusion can be implemented simple frameworks.