Results
An upper bound on the time complexity of iterative-deepening-A*
Patrick, B. G. | Almulla, M. | Newborn, M.
Artificial Intelligence, 5 (2-4), 265-78.
Constraint satisfaction
In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence., Vol. 1, pp. 285-293. Wiley. Links to a variety of constraint satisfaction articles. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, Volume 25, Issue 1, January 1985, Pages 65–74 (http://www.sciencedirect.com/science/article/pii/0004370285900414). Constraint Satisfaction. Technical Report, University of British Columbia, 1985 (http://dl.acm.org/citation.cfm?id=901711). The logic of constraint satisfaction. Artificial Intelligence, Volume 58, Issues 1–3, December 1992, Pages 3–20 (http://www.sciencedirect.com/science/article/pii/000437029290003G). The complexity of constraint satisfaction revisited. Artificial Intelligence, Volume 59, Issues 1–2, February 1993, Pages 57–62 (http://www.sciencedirect.com/science/article/pii/000437029390170G). Parallel and distributed algorithms for finite constraint satisfaction problems. Proceedings of the Third IEEE Symposium on Parallel and Distributed Processing, 1991 (https://ieeexplore.ieee.org/document/218214). Hierarchical arc consistency: exploiting structured domains in constraint satisfaction problems. Computational Intelligence, Volume 1, Issue 1, pages 118–126, January 1985 (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.1985.tb00064.x). Knowledge structuring and constraint satisfaction: the Mapsee approach. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume:10, Issue: 6) (https://ieeexplore.ieee.org/abstract/document/9108?section=abstract). Chapter 2 – Constraint Satisfaction: An Emerging Paradigm. Foundations of Artificial Intelligence, Volume 2, 2006, Pages 13–27. Handbook of Constraint Programming (http://www.sciencedirect.com/science/article/pii/S1574652606800064).
Protein secondary structure prediction using logic-based machine learning
Protein Engineering 5(7), pp. 647-657
The convergence of TD(lambda) for general lambda
Comments:!! See this http URL for any accompanying files
Hard and Easy SAT Problems
Mitchell, David | Selman, Bart
"We report results from large-scale experiments in satisfiability testing. As has been observed by others, testing the satisfiability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult. Our results provide a benchmark for the evaluation of satisfiability-testing procedures." Proc. AAAI-92.
From knowledge bases to decision models
Wellman, M. P. | Breese, J. S. | Goldman, R.
Knowledge Engineering Review, 7 (1), 35-53.
The CLP language and system
Jaffar, J. | Michaylov, S. | Stuckey, P. J. | Yap, R. H. C.
ACM Transactions on Programming Languages and Systems, 14 (3), , 339–395.
Randomized incremental construction of Delaunay and Voronoi diagrams
Guibas, L. J. | Knuth, D. E. | Sharir, M.
Algorithmica, 7, 381–413. See also 17th Int. Coll. on Automata, Languages and Programming, 1990, pp. 414–431.