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

Explore the integration of Constrained Heuristic Search (CHS) into the Soar Cognitive Architecture to demonstrate domain-independent learning capabilities. This study showcases how CHS augments problem-solving paradigms in Soar to enable learning of generalized knowledge and improve solution quality. Experiments illustrate the scalability and adaptability of CHS-Soar across diverse problem types like Job Shop Scheduling, Map Colouring, and more.

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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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