51, Argument-Based Machine Learning

Just read 'Argument-Based Machine Learning' by Ivan Bratko, Jure Zabkar and Martin Mozina. I think it's a chapter in a forthcoming book. I skipped the details in the middle but found the high-level explanations and examples in the beginning and end very understandable. A good reminder of machine (/inductive) learning and a nice presentation of how argumentation can aid in this.

Tight Arguments

Not that it is very very important but I thought I would document a few small issues I have with the definition of "Tight arguments" in 'Dialectic proof procedures for assumption-based, admissible argumentation' (2005).

Firstly, "multi-sets". Are they necessary? Why?

Secondly,the "selection function". According to the way it is used and footnote 6 in particular, it is not really a function. It does not give the same output every time for a given input.

Thirdly, the "definition" seems a bit confused with the "construction".