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Evidence from machines that learn and think like people

Published online by Cambridge University Press:  10 November 2017

Kenneth D. Forbus  and
Dedre Gentner
Kenneth D. Forbus
Affiliation:
Department of Computer Science, Northwestern University, Evanston, IL 60208. [email protected]://www.cs.northwestern.edu/~forbus/
Dedre Gentner
Affiliation:
Department of Psychology, Northwestern University, Evanston, IL 60208. [email protected]://groups.psych.northwestern.edu/gentner/
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Abstract

We agree with Lake et al.'s trenchant analysis of deep learning systems, including that they are highly brittle and that they need vastly more examples than do people. We also agree that human cognition relies heavily on structured relational representations. However, we differ in our analysis of human cognitive processing. We argue that (1) analogical comparison processes are central to human cognition; and (2) intuitive physical knowledge is captured by qualitative representations, rather than quantitative simulations.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e264
Copyright
Copyright © Cambridge University Press 2017 

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References

Carey, S. (2009) The origin of concepts. Oxford University Press.CrossRefGoogle Scholar
Chen, Z. & Klahr, D. (1999) All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development 70(5):1098–120.Google Scholar
Day, S. B. & Gentner, D. (2007) Nonintentional analogical inference in text comprehension. Memory & Cognition 35:39–49.Google Scholar
Dehghani, M., Tomai, E., Forbus, K. & Klenk, M. (2008) An integrated reasoning approach to moral decision-making. In: Proceedings of the 23rd AAAI National Conference on Artificial Intelligence, vol. 3, pp. 1280–86. AAAI Press.Google Scholar
Dunbar, K. (1995) How scientists really reason: Scientific reasoning in real-world laboratories. In: The nature of insight, ed. Sternberg, R. J. & Davidson, J. E., pp. 365–95. MIT Press.Google Scholar
Forbus, K. (2011) Qualitative modeling. Wiley Interdisciplinary Reviews: Cognitive Science 2(4):374–91.Google ScholarPubMed
Forbus, K., Ferguson, R., Lovett, A. & Gentner, D. (2017) Extending SME to handle large-scale cognitive modeling. Cognitive Science 41(5):1152–201. doi:10.1111/cogs.12377.Google Scholar
Forbus, K. & Gentner, D. 1997. Qualitative mental models: Simulations or memories? Presented at the Eleventh International Workshop on Qualitative Reasoning, Cortona, Italy, June 3–6, 1997.Google Scholar
Friedman, S. E. and Forbus, K. D. (2010) An integrated systems approach to explanation-based conceptual change. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, Atlanta, GA, July 11–15, 2010. AAAI Press.Google Scholar
Gentner, D. (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science 7:155–70. (Reprinted in A. Collins & E. E. Smith, eds. Readings in cognitive science: A perspective from psychology and artificial intelligence. Kaufmann.)Google Scholar
Gentner, D. (2010) Bootstrapping the mind: Analogical processes and symbol systems. Cognitive Science 34(5):752–75.Google Scholar
Goodfellow, I., Schlens, J. & Szegedy, C. (2015) Explaining and harnessing adversarial examples. Presented at International Conference on Learning Representations (ICLR), San Diego, CA, May 7–9, 2015. arXiv preprint 1412.6572. Available at: https://arxiv.org/abs/1412.6572.Google Scholar
Hinrichs, T. & Forbus, K. (2011) Transfer learning through analogy in games. AI Magazine 32(1):72–83.CrossRefGoogle Scholar
Liang, C. and Forbus, K. (2015) Learning plausible inferences from semantic web knowledge by combining analogical generalization with structured logistic regression. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, TX. AAAI Press.Google Scholar
Lovett, A. & Forbus, K. (2017) Modeling visual problem solving as analogical reasoning. Psychological Review 124(1):60–90.CrossRefGoogle ScholarPubMed
Marr, D. (1983) Vision. W. H. Freeman.Google Scholar
McFate, C. & Forbus, K. (2016) An analysis of frame semantics of continuous processes. Proceedings of the 38th Annual Conference of the Cognitive Science Society, Philadelphia, PA, ed. Papafragou, A., Grodner, D., Mirman, D. & Trueswell, J. C., pp. 836–41. Cognitive Science Society.Google Scholar
McFate, C. J., Forbus, K. & Hinrichs, T. (2014) Using narrative function to extract qualitative information from natural language texts. Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Canada, July 27–31, 2014, pp. 373–379. AAAI Press.Google Scholar
Mix, K. S. (1999) Similarity and numerical equivalence: Appearances count. Cognitive Development 14:269–97.Google Scholar
Palmer, S. (1999) Vision science: Photons to phenomenology. MIT Press.Google Scholar
Richland, L. E. & Simms, N. (2015) Analogy, higher order thinking, and education. Wiley Interdisciplinary Reviews: Cognitive Science 6(2):177–92.Google Scholar
Tomai, E. & Forbus, K. (2008) Using qualitative reasoning for the attribution of moral responsibility. In: Proceedings of the 30th Annual Conference of the Cognitive Science Society, Washington, DC, July 23–26, 2008. Cognitive Science Society.Google Scholar