TuringEval

Research

Peter Cheeseman’s decades of AI research has focused on probability and logic as basic tools for AI. This research is being incorporated into TuringEval’s evolving AI architecture, along with the experience gained from a professional lifetime immersed in the AI community.

Below are selected publications by Peter Cheeseman and Nick Candau, available for download.

Talks

Peter Cheeseman – Implications of Artificial General Intelligence at Artificial General Intelligence Conference 2017


Research Papers

SetLog: A Language for Knowledge Representation, P. Cheeseman, N. Candau, D. Cutrell, 2025

Maximum Entropy Inference for Machine Learning, P. Cheeseman and N. Candau, 2023 (Working Paper)
Estimating Uncertain Spatial Relationships in Robotics, P. Cheeseman and R. Smith and M. Self, 2013
Generalized Maximum Entropy, P. Cheeseman, 2005
On the Relationship Between Bayesian and Maximum Entropy Inference, P. Cheeseman and J. Stutz, 2004
In Defense of Probability, P. Cheeseman, 2003
Bayes Surface Reconstruction, V. N. Smelyanskiy, P. Cheeseman, D. A. Maluf and R. D. Morris, 2000
Autoclass: Theory and Results, P. Cheeseman, 1999
Bayesian Classification Theory, P. Cheeseman and R. Hanson and J. Stutz, 1995
Where Really Hard Problems Are, P. Cheeseman and B. Kanefsky, 1995
Bayes Methods for Adaptive Models