Probabilistic Logic Programming

• L. De Raedt, A. Kimming, H. Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. IJCAI-07.
• S. Muggleton. Stochastic logic programs. Advances in Inductive Logic Programming, 1996.
  Both of the above two works attach probability to each definite clause in Prolog. The apparent difference is that in SLP, the logic program resembles a probabilistic context-free grammar, where the sum of the probabilities of a head atom is 1; in ProbLog, the attached number stands for the probability of the clause being present in a randomly sampled logic program. Both rely on variants of SLD refutation for inference. The details need to be further looked into.
  
• D. Poole. Probabilistic Horn abduction and Bayesian networks. AIJ-93.
  In progress...
  

Logics for Probability Reasoning

• C. Baral, M. Gelfond, N. Rushton. Probabilistic reasoning with answer sets. Proc. Logic Programming and Non Monotonic Reasoning.
• C. Baral, M. Hunsaker. Using the probabilistic logic programming language P-log for causal and counterfactual reasoning and non-naive conditioning. IJCAI-07.
  According to my understanding, the first in the defining paper of P-log and the second relates a "light" version of P-log to Pearl's probabilistic causal models, including examples of observation assimilation query, intervention query and counterfactual query. An answer set semantics underlies P-log, which has (i) a set of boolean variables, (ii) a regular part of logic programming rules, (iii) random selection rules, (iv) observations and actions.
  
• T. Sato. Inside-outside probability computation for belief propagation. IJCAI-07.
  This work shows the equivalence between belief propagation for junction trees and the inside-outside algorithm for probabilistic logic programs based on the PRISM architecture.
  
• H. Pasula, S. Russell. Approximate inference for first-order probabilistic languages. IJCAI-01.
  This paper presents a MCMC algorithm for Koller and Pfeffer's probabilistic relational models, and in the extended case where reference and identity uncertainties are allowed.
  
• F. Bacchus, A. Grove, J. Y. Halpern, D. Koller. Statistical foundations for default reasoning. IJCAI-93.
• F. Bacchus, J. Y. Halpern, H. J. Levesque. Reasoning about noisy sensors and effectors in the situation calculus. AIJ-99.
• A. Gabaldon, G. Lakemeyer. ESP: A logic for only-knowing, noisy sensing and acting. AAAI-07.

Natural Language Processing

• N. Chater, C. D. Manning. Probabilistic models of language processing and acquisition. Trends in Coginitive Science (special issue) 2006.

Structual Learning

• C. Kemp, J. B. Tennenbaum, T. L. Griffiths, T. Yamadan, N. Ueda. Learning systems of concepts with an infinite relational model. AAAI-06.
• S. Kok, P. Domingos. Learning the structure of Markov logic networks. ICML-05.
  

Satisfiability

• S. Prestwich, I. Lynce. Refutation by randomized general resolution. AAAI-07.
  

General AI

• V. Savova, L. Peshkin. Is the Turing test good enough? The fallacy of resource-unbounded intelligence. IJCAI-07.
  This paper argues for the turing test as a criterion for "human-level intelligence". For the objection of "information content", it argues that blind and deaf people are intelligent, though deprived of some sensory input; for "generative incapacity" it shows that look-up table like implementation can never pass the Turing test.
  -- "If we accept that blind (or deaf) individuals are intelligent, the question becomes, how much real world deprivation can an entity handle while still be considered intelligent."
  
• K. D. Forbus et al. Integrating natural language, knowledge representation and reasoning and analogical processing to learn by reading. AAAI-07.
• K. Barker et al. Learning by reading: A prototype system, performance baseline and lessons learned. AAAI-07.
• N. Chater, J. B. Tennenbaum, A. Yuille. Probabilistic models of cognition: Conceptual foundations / Where next? Trends in Cognitive Sciences (special issue) 2006.