[Intelligence Seminar] May 11: John Laird, NSH 3305, 3:30, "A Task-Independent Approach to Diverse Forms of Prediction for Action Modeling"

Noah A Smith nasmith at cs.cmu.edu
Tue May 4 10:31:39 EDT 2010

Intelligence Seminar

Tuesday, May 11, 2010
NSH 3305 (note special room!)

Title:  A Task-Independent Approach to Diverse Forms of Prediction for
Action Modeling
John Laird, University of Michigan
Host:  Manuela Veloso
Please contact Heather Carney(hcarney at cs.cmu.edu) to request a meeting.


Researchers in AI have long studied planning, where an agent
internally simulates possible actions to determine which action leads
to the best future situation. The knowledge to predict results is
called action In general, action models are represented using
rule-like data structures (think STRIPS) that describe the conditions
under which the action can be executed, and the changes the action
makes. Our hypothesis is that there are many forms of knowledge for
action modeling, not just rule-like structures, and that these can be
embedded within a unified task-independent framework where they are
used opportunistically based on the s knowledge and the task
demands. In this talk, I describe such a framework based on the Soar
cognitive architecture, where different processes and sources of
knowledge are available for prediction, including rules, episodic
memory, semantic memory, mental imagery, and general problem
solving. I present results from two simple domains.


John E. Laird is the John L. Tishman Professor of Engineering at the
University of Michigan. He received his Ph.D. in Computer Science from
Carnegie Mellon University in 1983 working with Allen Newell. From
1984 to 1986, he was a member of research staff at Xerox Palo Alto
Research Center. His research interests spring from a desire to
understand the nature of the architecture underlying artificial and
natural intelligence. He is one of the original developers of the Soar
architecture and leads its continued evolution, including the recent
development and integration of reinforcement learning, episodic
memory, semantic memory, visual and spatial mental imagery, and
appraisal-based emotion. He was a founder of Soar Technology, Inc. and
he is a Fellow of AAAI, ACM, and the Cognitive Science Society.

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