An Integrated Systems Approach to Explanation-Based Conceptual Change

By Scott E Friedman, Kenneth D. Forbus. 2010

23 citations recorded by the source corpus.

Understanding conceptual change is an important problem in modeling human cognition and in making integrated AI systems that can learn autonomously. This paper describes a model of explanation-based conceptual change, integrating sketch understanding, analogical processing, qualitative models, truth-maintenance, and heuristic-based reasoning within the Companions cognitive architecture. Sketch understanding is used to automatically encode stimuli in the form of comic strips. Qualitative models and conceptual quantities are constructed for new phenomena via analogical reasoning and heuristics. Truth-maintenance is used to integrate conceptual and episodic knowledge into explanations, and heuristics are used to modify existing conceptual knowledge in order to produce better explanations. We simulate the learning and revision of the concept of force, testing the concepts learned via a questionnaire of sketches given to students, showing that our model follows a similar learning trajectory.

Subfields: Artificial Intelligence, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Josh Tenenbaum
2. Tomer D. Ullman
3. Brenden M. Lake
4. Samuel J. Gershman
5. Bruce L. Sherin
6. Kenneth D. Forbus
7. Scott E Friedman
8. John K. Tsotsos

In Cognitive Science & Psychology

1. Tomer D. Ullman
2. Brenden M. Lake
3. Bruce L. Sherin
4. Scott E Friedman
5. Samuel J. Gershman
6. Kenneth D. Forbus
7. Josh Tenenbaum
8. John K. Tsotsos

Citations in the corpus

• What can the brain teach us about building artificial intelligence? (2016, background): “Trends in Cognitive Sciences 16(7):382–89. [arBML] Schulz, L. E., Gopnik, A. & Glymour, C. (2007) Preschool children learn about”
• 40 years of cognitive architectures: core cognitive abilities and practical applications (2018, background): “2004),Companions (Friedman and Forbus 2010), ADAPT (Benjamin and Lyons 2010), ERE (Kedar and McKusick 1992), REM (Murdock and Goel 2008), CELTS (Faghihi et al.”
• Representing, Running, and Revising Mental Models: A Computational Model (2018, methodology): “1 T IMBER has been used to simulate students’ conceptual change in commonsense science domains including physics (Friedman & Forbus, 2010), biology (Friedman & Forbus, 2011), and the day – night cycle (Friedman, Barbella, & Forbus, 2012).”