Analogy, explanation, and proof.

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.