Probabilistic Knowledge Representation

• L. Ngo, P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases.
• L. Ngo, P. Haddawy, J. Helwig. A theoretical framework for contest-sensitive temporal probability model construction with application to plan projection. UAI-95.
  To my understanding, these two papers introduce another logic programming approach for probabilistic reasoning. The basic idea is that a small Bayes network can be constructed from such an LP, in which only relevent nodes are included.
  
• T. Sato. A statistical learning method for logic programs with distribution semantics. ICLP-95.
  This paper introduces a formal distributional semantics for logic programs with deterministic rules and random facts, and claims that this semantics bridges logic programming and learning.
  
• J. Binder, D. Koller, S. Russell, K. Kanazawa. Adaptive probabilistic networks with hidden variables.
  The authors argue for the importance of hidden variables in BNs to reduce the complexity of the networks. (Not yet read in detail.)
  
• KR&R Book.
  Section 12.4.3 gives a Bayes interpretation of the production-system-like tip calculation in Section 12.4.2. Can we get a similar result for RPS?
  

Machine Learning and Connectionist Models

• K. B. Laskey. Adapting connectionist learning to Bayes networks. I. J. of Approximate Reasoning 1990.
  As an attempt to unifying symbolic and "sub-symbolic" reasoning, this paper finds some close relationship between the Boltzmann machines and the Bayes networks with Markov random fields as the intermediate bridge. The author suggests that the learning algorithm for BM can be adapted to adjust conditional probabilities in BN.
• D. H. Ackley, G. E. Hinton, T. J. Sejnowski. A learning algorithm for Boltzmann machines. Cognitive Science 1985.
  The classic paper introducing the SA algorithm for learning Boltzmann machine parameters.
  

General AI and Others

• C. Baral, J. Lobo. Characterizing production systems using logic programming and situation calculus.
  This paper offers production systems a declarative semantics by mapping production rules to logic programs encoding the situation calculus. This is likely to help formally analyze properties of production systems.
  
• E. Baum. A working hypothesis for general intelligence.
  The author argues for the important role of an extrapolated version of Occam's razor in general intelligence, and offers an evolutionary view of the formation of intelligence. While I agree with the hypothesis in the first section, I doubt if his introspection experiments will finally lead to general ingelligence.
  
• R. J. McEliece, D. MacKay, J. F. Cheng. Turbo decoding as an instance of Pearl's "belief propagation" algorithm. IEEE J. on selected areas in communication 1998.
  Identifies the relationship between turbo decoding in information theory and belief propagation in Bayes belief networks. (Reading in progress.)