In this project, we are developing a theoretical foundation and computational methods for discovering optimal cognitive strategies and sound principles for good decision making and clear thinking.
As a first step, we have developed a meta-level reinforcement learning method for computing rational heuristics that make optimal use of the decision-makers finite time and limited cognitive resources. Moving forward, we will also apply program induction methods to find efficient and interpretable representations of those heuristics and general principles for good decision-making. Furthermore, we will model increasingly more realistic decision problems and find optimal ways to solve them.
Publications
- Gul, S., Krueger, P.M., Callaway, F., Griffiths, T.L., & Lieder, F. (2018). Discovering Rational Heuristics for Risky Choice. KogWis 2018. [Abstract]
- Callaway, F., Gul, S., Krueger, P.M., Griffiths, T.L., Lieder, F. (2018). Learning to select computations. Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference. doi:10.13140/RG.2.2.20892.80007
- Lieder, F.*, Krueger, P.M.*, & Griffiths, T.L. (2017). An Automatic Method for Discovering Rational Heuristics for Risky Choice. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.). Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin TX: Cognitive Science Society. * These authors contributed equally. [Article]
- Callaway, F.1, Lieder, F.1, Das, P., Gul, S., Krueger, P.M., Griffiths, T.L. (2018). A resource-rational analysis of human planning. Proceedings of the 40ths Annual Meeting of the Cognitive Science Society. DOI: 10.13140/RG.2.2.15636.40326
1These authors contributed equally.