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Sean A. Bittle Mark S. Fox March 7th, 2009

Introducing Constrained Heuristic Search to the Soar Cognitive Architecture (demonstrating domain independent learning in Soar) The Second Conference on Artificial General Intelligence, AGI-09. Sean A. Bittle Mark S. Fox March 7th, 2009. 1 /11. The Problem.

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Sean A. Bittle Mark S. Fox March 7th, 2009

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  1. Introducing Constrained Heuristic Search to the Soar Cognitive Architecture(demonstrating domain independent learning in Soar) The Second Conference on Artificial General Intelligence, AGI-09 Sean A. Bittle Mark S. Fox March 7th, 2009 1/11

  2. The Problem • General problem solving and learning are central goals of AI research on cognitive architectures • However, there are few examples of domain independent learning in cognitive architectures The Goal • Demonstrate Soar can learn and apply domain independent knowledge But to achieve this goal we need to augment the Soar’s problem-solving paradigm (CHS-Soar) 2/11

  3. Soar Cognitive Architecture • Developed by Newell, Laird and Rosenbloom at CMU, 1983 • Symbolic Cognitive Architecture where all long term knowledge is encoded as productions rules. • Based on the hypothesis that all goal-oriented behavior can be cast as the selection and application of operators to a state in a problem space 3/11

  4. Constraint Graph Va Ci Vc Cii Vb Constrained Heuristic Search (CHS) • Developed by Fox, Sadeh and Bayken, 1989 • CHS is a problem solving approach that combination of constraint satisfaction and heuristic search where the definition of the problem space is refined to include: • Problem Topology • Problem Textures • Problem Objective • CP/CHS allows us to employ a generalized problem representation (CSP) and utilize generic, yet powerful problem solving techniques 4/11

  5. CHS-Soar “What are we trying to learn?” 5/11

  6. CHS-Soar What are “Texture Measures?” • Minimum Remaining Values (MRV) – variable selection • Degree (DEG) – variable selection • Least Constraining Value (LCV) – value selection 6/11

  7. CHS-Soar “How Do We Select a “Good” Texture Measure?” 7/11

  8. CHS-Soar “What Do We Learn...Again?” Traditional Soar Agent Chunks tend to include domain specific knowledge Hyper-heuristics: heuristics to choose heuristic measures 8/11

  9. Experiments Three experiments conducted to investigate: • Integration of rule and constraint based reasoning • Domain Independent Learning • Scalability of externally learned chunks Problem types being considered: • Job Shop Scheduling (JSS) • Map Colouring • Radio Frequency Assignment Problem (RFAP) • N-Queens, Sudoku, Latin Square • Towers of Hanoi, Water Jugs • Configuration Problems • Random CSPs 9/11

  10. Results: Domain Independent Learning Map Colouring(n = 11) Job Shop Scheduling(n = 15) RFAP(n = 200) 10/11

  11. Conclusions • Demonstrated integration of rule and constraint based reasoning • Demonstrated the ability to learn rules while solving one problem type that can be successfully applied in solving another problem type • Demonstrated ability to discover, learn and use multi-textured “hyper-heuristics” leading to improved solutions over traditional unary heuristics 11/11

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