This page lists my most recent publications, making them easier to find than searching through my other research web pages.
· Laird, J. E. (2008). Extending the Soar Cognitive Architecture. Artificial General Intelligence Conference, Memphis, TN.
· Langley, P., & Laird, J. E. (2002). Cognitive architectures: Research issues and challenges (Technical Report). Institute for the Study of Learning and Expertise, Palo Alto, CA. We hope to have a published version of this soon (late 2005).
We are in the process of adding reinforcement learning to Soar.
· Wang, Y., and Laird, J.E. 2007. The Importance of Action History in Decision Making and Reinforcement Learning. Proceedings of the Eighth International Conference on Cognitive Modeling. Ann Arbor, MI. http://www-personal.umich.edu/~yongjiaw/publications/ICCM_2007.pdf
· Nason, S. and Laird, J. E., Soar-RL, Integrating Reinforcement Learning with Soar, Cognitive Systems Research, 6 (1), 2005, pp. 51-59. Also in International Conference on Cognitive Modeling, 2004.
· Lathrop, S.D., and Laird, J.E. (2007). Towards Incorporating Visual Imagery into a Cognitive Architecture. Proceedings of the Eighth International Conference on Cognitive Modeling. Ann Arbor, MI. http://www.eecs.umich.edu/~slathrop/publications/ICCM07_paper_final.pdf
· Lathrop, S., and Laird, J.E. 2006. Incorporating Visual Imagery into a Cognitive Architecture: An Initial Theory, Design and Implementation. http://ai.eecs.umich.edu/soar/sitemaker/docs/pubs/cca_tech_report_2006-01.pdf
· Wintermute, S., and Laird, J. E. (2007). Predicate Projection in a Bimodal Spatial Reasoning System. Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), Vancouver, B.C., Canada.
We are also adding an episodic memory to Soar.
· Nuxoll, A. M. and Laird, J. E. (2007). Extending Cognitive Architecture with Episodic Memory. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI). http://ai.eecs.umich.edu/soar/sitemaker/docs/pubs/AAAI2007_NuxollLaird_ver14(final).pdf
· Nuxoll, A., Laird, J., A Cognitive Model of Episodic Memory Integrated With a General Cognitive Architecture, International Conference on Cognitive Modeling 2004.
Procedural Learning:
· Pearson, D. J ., Laird, J. E., “Incremental Learning of Procedural Planning Knowledge in Challenging Environments,” Computational Intelligence, 2005, 21:4, 414
We added activation to Soar’s working memory. We built upon the original ACT activation scheme that was then implemented originally in Soar by Ron Chong. Our implementation is completely new and attempts to provide a very efficient implementation so that there is little overhead to use activation. We are initially using the activation for feature selection in the episodic learning work and expect to use it in the reinforcement learning work. Some day we might even use it for forgetting (but not yet).
· Nuxoll, A., Laird, J.,
James, M. Comprehensive
Working Memory Activation in Soar.
International Conference on Cognitive Modeling, Poster, 2004.
We are delving into adding a model of emotion to Soar. We initially have developed a framework for how emotion fits in Soar and have tried it on a simple task.
· Marinier, R.P., Laird, J.E. 2007. Computational Modeling of Mood and Feeling from Emotion. CogSci 2007. Nashville, TN. http://sitemaker.umich.edu/marinier/files/Marinier_Laird_CogSci_2007_ComputationalModeling.pdf
·
Marinier, R. and Laird, J. A Cognitive
Architecture Theory of Comprehension and Appraisal. Agent Construction and
Emotion 2006, Vienna, Austria, April 2006. http://sitemaker.umich.edu/marinier/files/Marinier_Laird_ACE_2006_ComprehensionAndAppraisal.pdf
· Marinier, R., Laird, J. Toward a Comprehensive Computational Model of Emotions and Feelings, International Conference on Cognitive Modeling 2004.
Doug Pearson and I have been working on a new was for doing knowledge acquisition that involves scenarios described as diagrams by an expert.
·
Douglas Pearson, John E. Laird, Redux: Example-Driven Diagrammatic Tools for Rapid
Knowledge Acquisition, Proceedings of Behavior Representation in Modeling
and Simulation, 2004,
This is Scott Wallace’s thesis research on validation.
· Wallace. S. A., Laird, J. E. Comparing Agents and Humans Using Behavioral Bounding. International Joint Conference on Artificial Intelligence (IJCAI-03).
·
Wallace, S. Validating
Complex Agent Behavior, Ph.D. Thesis
The first paper describes our work on the HAUNT II game - a mod to Unreal Tournament that includes Soar characters and a director. The second two describe our work on hooking Soar up to a real-time strategy game engine.
· Brian
Magerko, John E. Laird, Mazin Assanie,
Alex Kerfoot,
· Wintermute, S., Xu, J., Irizarry, J., Laird, J.E. 2007. SORTS Tech Report. http://ai.eecs.umich.edu/soar/sitemaker/docs/pubs/sorts_report.pdf
· Wintermute, S., Xu, J., and Laird, J.E. SORTS: A Human-Level Approach to Real-Time Strategy AI. Proceedings of the Third Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE-07), Stanford, California http://www.eecs.umich.edu/~swinterm/papers/AIIDE07-SORTS.pdf
This
is Brian Magerko’s thesis research – building a drama
manager/director that oversees an interactive game.
·
Magerko, B. and Laird, J. "Building
an Interactive Drama Architecture with a High Degree of Interactivity."
1st International Conference on Technologies for Interactive Digital
Storytelling and Entertainment,
Together with Bob Wray of Soar
Technology, we developed an adversary for MOUT training. One issue that we
addressed in the research was variability. The second paper is an overview of
that project.
·
Wray,
R.E., Laird, J. E. (2003) “Variability
in Human Behavior Modeling for Military Simulations”, Behavior
Representation in Modeling and Simulation Conference. Scottsdale, AZ.
· Robert E. Wray, John E. Laird, Andrew Nuxoll, Devvan Stokes, Alex Kerfoot, Synthetic Adversaries for Urban Combat Training, Proceedings of the 2004 Innovative Applications of Artificial Intelligence Conference, San Jose, CA, July 2004. AAAI Press. Also published in AI Magazine, 26(3):82-92, 2005.