Online learning of causal structure in a dynamic game situation
Yue Gao, Eyal I. Nitzany, Shimon Edelman · eScholarship (California Digital Library) · 2012
Agents situated in a dynamic environment with an initially unknown causal structure, which, moreover, links certain behavioral choices to rewards, must be able to learn such structure incrementally on the fly.We report an experimental study that characterizes human learning in a controlled dynamic game environment, and describe a computational model that is capable of similar learning.The model learns by building up a representation of the hypothesized causes and effects, including estimates of the strength of each causal interaction.It is driven initially by simple guesses regarding such interactions, inspired by events occurring in close temporal succession.The model maintains its structure dynamically (including omitting or even reversing the current best-guess dependencies, if warranted by new evidence), and estimates the projected probability of possible outcomes by performing inference on the resulting Bayesian network.The model reproduces the human performance in the present dynamical task.