A Python package for creating and solving constrained randomization problems.

The most powerful and extensible way to control the output of large language models.

Describe processes as type transformations, with inference that supports subtypes and parametric polymorphism. Create and query corresponding transformation graphs.

Solving the two-dimensional strip packing problem, using several combinatorial decision making and optimization approaches: Constraint Programming, Boolean SATisfiability, Satisfiability Modulo Theory; Integer Linear Programming.

Linear Program Solver: Simplex methods are used to solve any linear program (with sensitivity analysis option for constraints)

Solving minesweeper as a Constraint Satisfaction Problem

Simple Horn Clause Reasoner

Repo for 1CK00 Transport and Distribution TUe

A demonstration of perturbation of data

CCR model

Reinforcement learning on OpenAI's mountain car