Denormalized quantum density operators for encoding semantic uncertainty in cognitive agents

The design of a cognitive agent requires a behaviour control of actions and observations for exploring an unknown world. Typically, observations are influenced by a certain degree of randomness which can be modeled as probabilities. In our scenario we let a mouse explore a maze with walls, boundaries and a random portal. All observations are stored and managed in a so-called inner stage. As a decision problem, we want to be able to plan actions and to predict their resulting observations. In our approach we develop models of the inner stage based on concepts of probabilistic databases and their mapping to denormalized density matrices which are known from quantum mechanics. Density matrices provide a compact representation of the powerful but unwieldy many-world-semantics. We show that density matrices make the many-world-semantics feasible and are well suited to model the inner stage. We propose algorithms for learning and predicting action results.