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John E Hummel1, John Licato2, Selmer Bringsjord3

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Summary
This summary is machine-generated.

Humans naturally generate explanations, similar to making analogies. A new model shows explanation generation can integrate multiple knowledge sources, unlike single-source analogy, producing varied, coherent explanations.

Keywords:
LISAanalogyexplanationlogicmodeling

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Psychology

Background:

  • Human explanation generation is a fundamental cognitive process.
  • Explanation generation shares similarities with analogical reasoning.
  • A key difference is explanation's need to integrate multiple knowledge sources.

Purpose of the Study:

  • To model explanation generation by adapting analogy models.
  • To address the challenge of integrating multiple knowledge sources in explanation.
  • To simulate and evaluate the model's explanation capabilities.

Main Methods:

  • Developed a computational model derived from analogy models.
  • Modified the model to allow integration of multiple knowledge sources.
  • Conducted simulations to test explanation generation for novel problems.

Main Results:

  • The model successfully generated explanations for novel problems (explananda).
  • Simulated explanations exhibited varying degrees of coherence.
  • Model performance parallels human explanation generation patterns.

Conclusions:

  • Cognitive models of analogy can be extended to explain generation.
  • Integrating multiple knowledge sources is crucial for robust explanation.
  • The model provides insights into the cognitive mechanisms of explanation.