Participatory Learning of Propositional Knowledge

Abstract

Our objective here is to extend the participatory learning paradigm (PLP) to environments in which we are interested in learning information and knowledge expressed in terms of declarative statements. We first recall the basic idea of participatory learning, which stresses the important role of what is already believed in all aspects of the learning process. We then discuss the representation of declarative-type binary knowledge within Zadeh's framework of approximate reasoning. We look at the approximate reasoning inference mechanism and its capability for weighted propositions. We introduce ideas such as consistency, compatibility, and commitment that are needed for our objective. We then provide a version of the PLP that is appropriate for the task of learning declarative knowledge. Central to this is the new updation algorithm that is introduced. We finally look at the dynamic performance of this framework. A particularly notable feature is the unlearning and then learning that takes place when the external environment changes.