Agentified Argumentative Learning

Argumentative learning amounts to integrating argumentative reasoning into forms of machine learning from examples. Amongst several approaches, ABA Learning is a form of argumentative learning that, given a background knowledge, and positive and negative examples, derives an Assumption-Based Argumentation (ABA) framework. The learnt ABA frameworks can be deployed to make run-time inference about previously unseen examples, even after having seen very few positive and negative examples. This inference is determined by (non-)acceptance of examples in extensions of the ABA frameworks. However, it may be impossible to determine definite (non-)acceptance when the learnt ABA frameworks admit no or several extensions. In this paper, we explore how this behaviour can be managed by "agentifying" ABA learning. This agentification amounts to leveraging the use of rules in non-flat ABA frameworks, representing denial integrity constraints, towards definite conclusions. Specifically, agentified ABA Learning can identify actions in the external environment aimed at generating observations for expanding the original ABA frameworks so that they admit extensions and at choosing amongst the extensions of (expanded) ABA frameworks.