The Soar papers: research on integrated intelligence, Volume 1
Soar is a state-of-the art computational theory of the mind that has had a significant impact in both artificial intelligence and cognitive science. Begun by John E. Laird, Allen Newell, and Paul S. Rosenbloom at Carnegie Mellon in the early 1980s, the Soar Project is an investigation into the architecture underlying intelligent behavior with the goal of developing and applying a unified theory of natural and artificial intelligence. The Soar Papers - sixty-three articles in all - provide in one place the important ideas that have emerged from this project.
The book is organized chronologically, with an introduction that provides multiple organizations according to major topics. Readers interested in the entire effort can read the articles in publication order, while readers interested only in a specific topic can go directly to a logical sequence of papers to read on that topic.
Paul S. Rosenbloom is Associate Professor of Computer Science at the Information Sciences Institute. John E. Laird is Associate Professor of Electrical Engineering and Computer Science at the University of Michigan. The late Allen Newell was U.A. and Helen Whitaker University Professor of Computer Science at Carnegie Mellon University.
Major topics include: The direct precursors of Soar, the Soar architecture, implementation issues, intelligent capabilities (such as problem solving and planning, learning, and external interaction), domains of application, psychological modeling, perspectives on Soar, and using Soar.
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acquisition actions activation agent algorithm and-node applied architecture Artificial Intelligence attribute backplane behavior Carnegie-Mellon University chunking mechanism chunking theory cognitive component Computer Science configuration constraints context control knowledge created curve cycle decision decoding domain domain theory Eight Puzzle elaboration phase elements encoding evaluation example exist exponential Figure function goal hierarchy hash tables heuristic hill climbing identifier impasse implementation initial input instantiations knowledge level Laird learning mechanism log-log Machine Learning macro macro-operators match means-ends analysis Newell object occurs On-light operator OPS5 parameters pattern performance possible power law problem solving problem space problem statement production system production-system representation Rete Rete algorithm Rl-Soar Rosenbloom rules search control search-control knowledge Seibel selection simple Soar solver specific stimulus structure subgoal symbol level task environment tile tion tokens Tower of Hanoi trial unibus universal weak method variables vote working-memory Xaps2
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