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Laird, J. E., Rosenbloom, P. S. and Newell, A. Universal Subgoaling and Chunking: The Automatic Generation and Learning of Goal Hierarchies. Hingham, MA: Kluwer Academic Publishers, 1986.
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Rosenbloom, P. S., Laird, J. E. and Newell, A. (Eds.). The Soar Papers: Research on Integrated Intelligence). Cambridge, MA: MIT Press, 1993.
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Rosenbloom, P. S. A world-championship-level Othello program. Artificial Intelligence, 19, 279-320, 1982.
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Rosenbloom, P. S., Laird, J. E., McDermott, J., Newell, A. and Orciuch, E. R1-Soar: An experiment in knowledge-intensive programming in a problem-solving architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 561-569, 1985.
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Laird, J. E., Rosenbloom, P. S. and Newell, A. Chunking in Soar: The anatomy of a general learning mechanism. Machine Learning, 1, 11-46, 1986.
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Laird, J. E., Newell, A. and Rosenbloom, P. S. Soar: An architecture for general intelligence. Artificial Intelligence, 33, 1-64, 1987.
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Tambe, M., Newell, A. and Rosenbloom, P. S. The problem of expensive chunks and its solution by restricting expressiveness. Machine Learning, 5, 299-348, 1990.
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Rosenbloom, P. S., Laird, J. E., Newell, A. and McCarl, R. A preliminary analysis of the Soar architecture as a basis for general intelligence. Artificial Intelligence, 47, 289-325, 1991.
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Golding, A. and Rosenbloom, P. S. A comparison of Anapron with seven other name-pronunciation systems. Journal of the American Voice I/O Society, 14, 1-21, 1993.
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Tambe, M. and Rosenbloom, P. S. Investigating production system representations for non-combinatorial match. Artificial Intelligence, 68, 155-199, 1994.
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Golding, A. R. and Rosenbloom, P. S. Improving accuracy by combining rule-based and case-based reasoning. Artificial Intelligence, 87, 215-254, 1996.
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Tambe, M. and Rosenbloom, P. S. Event tracking in a dynamic multi-agent environment. Computational Intelligence, 12, 499-521, 1996.
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Kim, J. and Rosenbloom, P. S. Bounding the cost of learned rules. Artificial Intelligence, 120, 43-80, 2000.
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Rosenbloom, P. S. A cognitive odyssey: From the power law of practice to a general learning mechanism and beyond. Tutorials in Quantitative Methods for Psychology, 2, 43-51, 2006.
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Rosenbloom, P. S. Rethinking cognitive architecture via graphical models. Cognitive Systems Research, 12(2), 198-209, 2011.
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Hill, R., Gratch, J. and Rosenbloom, P. Flexible group behavior: Virtual commanders for synthetic battlespaces. In C. Sierra, M. Gini & J. S. Rosenschein (Eds.), Proceedings of the Fourth International Conference on Autonomous Agents (pp. 31-38). Barcelona, Spain: ACM Press, 2000.
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Scerri, P., Pynadath, D. V., Johnson, L., Rosenbloom, P., Schurr, N. and Tambe, M. A prototype infrastructure for distributed robot-agent-person teams. In Proceedings of the Second International Joint Conference on Autonomous Agents & Multiagent Systems (pp. 433-440). Melbourne, Australia: ACM Press, 2003.
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Rosenbloom, P. S. Towards a new cognitive hourglass: Uniform implementation of cognitive architecture via factor graphs. Proceedings of the 9th International Conference on Cognitive Modeling, 2009.
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Rosenbloom, P. S. Combining procedural and declarative knowledge in a graphical architecture. Proceedings of the 10th International Conference on Cognitive Modeling, 2010.
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Rosenbloom, P. S. Implementing first-order variables in a graphical cognitive architecture. Biologically Inspired Cognitive Architectures: Proceedings of the First Annual Meeting of the BICA Society. Arlington, VA: IOS Press, 2010.
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Rosenbloom, P S. From memory to problem solving: Mechanism reuse in a graphical cognitive architecture. In Proceedings of the 4th Conference on Artificial General Intelligence, 2011.