Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Introduction
- Part II Concepts and Techniques
- Part III Reflections and Elaborations
- Part IV Applications
- 10 Coherence as Constraint Satisfaction
- 11 Analogy as Structure Mapping
- 12 Communication as Bayesian Inference
- Appendix A Mathematical Background
- Appendix B List of Computational Problems
- Appendix C Compendium of Complexity Results
- References
- Index
11 - Analogy as Structure Mapping
from Part IV - Applications
Published online by Cambridge University Press: 18 April 2019
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Introduction
- Part II Concepts and Techniques
- Part III Reflections and Elaborations
- Part IV Applications
- 10 Coherence as Constraint Satisfaction
- 11 Analogy as Structure Mapping
- 12 Communication as Bayesian Inference
- Appendix A Mathematical Background
- Appendix B List of Computational Problems
- Appendix C Compendium of Complexity Results
- References
- Index
Summary
In this chapter, we consider a computational-level theory of analogy derivation as structure mapping. We again illustrate the use of classical complexity analysis to assess the theory's intractability. In addition, we show how parameterized complexity analysis can be used to formally assess intuitive conjectures about possible sources of this intractability. This illustration demonstrates that such intuitions can often be wrong, underscoring the importance of formal analyses.
- Type
- Chapter
- Information
- Cognition and IntractabilityA Guide to Classical and Parameterized Complexity Analysis, pp. 234 - 245Publisher: Cambridge University PressPrint publication year: 2019