Automating the Processes of Inference and InquirySeptember 25, 2009 7:07 pm Intelligent Systems, News, Presentations
In September 2009, Kevin Knuth (President, Autonomous Exploration, Inc.) was invited to present a talk titled “Automating the Processes of Inference and Inquiry” at the From Game Theory to Game Engineering Workshop, Oxford-Man Institute of Quantitative Finance, Oxford, UK. This talk discussed the foundation of automated inference and inquiry and its application to game theory.
The talk involved a demonstration of an autonomous robot that designs its own experiments using Bayesian Adaptive Exploration.
An abstract follows below:
Automating the Processes of Inference and Inquiry
Most decision making processes must be performed in the face of uncertain or incomplete information. It is critical to be able to make the best inferences one can under these situations, as well as to ask for additional information in an intelligent manner. In this talk, I will introduce a unified framework for automated inference and inquiry based on Bayesian probability theory and Bayesian Adaptive Exploration.
I will begin by considering a system described by a finite set of possible states, and from this I will derive the rules of rational inference by considering the lattice of possible statements that can be made about the system. These rules result in Bayesian probability theory, but seen from a new perspective. I will then introduce Bayesian Adaptive Exploration, which can be used to decide which new data to collect given what is currently known.
I will demonstrate the theory with a Bayesian machine (robot) that locates and characterizes shapes on a playing field. The intelligent aspects of this machine rely on two systems: an inference engine and an inquiry engine. The inference engine simultaneously performs Bayesian model estimation and Bayesian parameter estimation by relying on the nested sampling algorithm. Meanwhile the inquiry engine performs experimental design by identifying the most relevant measurement given the current state of knowledge of the machine. This is accomplished by quantifying the entropy of the possible experimental outcomes. This research represents a real-time implementation of a unified framework of inference and inquiry.