Sean a bittle mark s fox march 7th 2009
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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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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

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Sean a bittle mark s fox march 7th 2009

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)

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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

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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

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CHS-Soar

“What are we trying to learn?”

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CHS-Soar

What are “Texture Measures?”

  • Minimum Remaining Values (MRV) – variable selection

  • Degree (DEG) – variable selection

  • Least Constraining Value (LCV) – value selection

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CHS-Soar

“How Do We Select a “Good” Texture Measure?”

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CHS-Soar

“What Do We Learn...Again?”

Traditional Soar Agent Chunks tend to include domain specific knowledge

Hyper-heuristics:

heuristics to choose heuristic measures

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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

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Results: Domain Independent Learning

Map Colouring(n = 11)

Job Shop Scheduling(n = 15)

RFAP(n = 200)

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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

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