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Problem-Solving01:29

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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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The volume of a fuel tank mounted on the wing of a jet aircraft can be modeled using the concept of solids of revolution. In this case, the tank is formed by rotating a two-dimensional region, defined by a mathematical function, about the x-axis. The region extends along the axis from zero to two meters, and the resulting three-dimensional shape is symmetric about the axis of rotation. Because the boundary curve lies directly against the axis, the disk method is an appropriate technique for...
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Complex problem solving: another test case?

Marco Ragni1, Christoph M Löffler

  • 1Center for Cognitive Science, Albert-Ludwigs-Universität Freiburg, Friedrichstr. 50, 79098, Freiburg, Germany. ragni@cognition.uni-freiburg.de

Cognitive Processing
|November 11, 2009
PubMed
Summary
This summary is machine-generated.

We introduce TRAVELPLAN, a natural domain for complex problem-solving research, addressing ecological validity criticisms. This framework enables computational analysis and experimental design for complex planning tasks.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Psychology

Background:

  • Classic complex problem-solving research faces challenges in ecological validity and analyzing intricate system variable interactions.
  • Existing paradigms like Lohhausen, Tailorshop, and Moro offer limited scope for naturalistic problem-solving studies.

Purpose of the Study:

  • To propose the TRAVELPLAN problem as a more ecologically valid domain for complex problem-solving research.
  • To establish a computational framework for analyzing and experimenting with complex planning tasks.

Main Methods:

  • Review and comparison of existing problem-solving scenarios (Lohhausen, Tailorshop, Moro) with the proposed TRAVELPLAN problem.
  • Formalizing TRAVELPLAN subproblems as constraint satisfaction problems.
  • Development of a computational architecture to handle simultaneous subproblem resolution.

Main Results:

  • The TRAVELPLAN problem integrates computationally well-investigated problems solvable via constraint satisfaction.
  • A computational architecture has been developed to manage multiple subproblems concurrently.
  • The framework facilitates the study of computational complexity within complex planning.

Conclusions:

  • The TRAVELPLAN domain offers a more naturalistic approach to complex problem-solving, enhancing ecological validity.
  • The developed computational architecture provides a foundation for empirical investigations into complex planning.
  • This research paves the way for specifying and formulating novel experimental designs in problem-solving research.