Knowledge Representation for Learning How to Evaluate Partial Plans
In this paper we present some ideas for knowledge representation formalism suitable for rational agents which learn how to choose the best conditional, partial plan in any given situation. In our architecture, the agent uses an incomplete symbolic inference engine, employing Active Logic, to reason about consequences of performing actions — including information-providing ones. It utilises a simpl
