Purposeful Choice and Point-of-View - A Generalized Quantum Approach

This paper presents a generalized quantum model for describing purposes or goals of individual agents, and the way choices can be made that enable these goals to be achieved. The underlying model is a semantic vector space model, which is turned into a purposeful choice model by labelling some axes as objectives, and describing choices as transformations on the vector spaces that enable agents in the model to set these objective axes in sight. We introduce this framework using a simplified example model of a dog trying to get food. Many parts of what has become the standard generalized quantum toolkit become apparent in this model, including learning, superposition, the importance of the metric used for normalization, classification, and a generalized uncertainty principle. The incorporation of purpose or goal into semantic vectors models also enables the analysis of traditional areas that are relatively new to artificial intelligence, including rhetoric, political science, and some of the philosophical questions touched by quantum theorists.