| Description |
Cognitive-science researchers describe experiments using high-level concepts like factor, crossing, and constraints on the relationship among trials. To solve those constraints, we translate the high-level language into a low-level formula. SweetPea currently uses boolean satisfiability (SAT), but other encodings are possible which may have advantages compared to SAT solving. The goal here is to implement an encoding of SweetPea programs as integer linear programming (ILP). This project is aimed at exploring how well ILP works for finding solutions to experiment constraints using Gurobi, an ILP solver, and whether this approach can sample from the possible solutions randomly and uniformly. |
