While recent developments in autonomous vehicle (AV) technology highlight substantial
progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto …

Markov decision process (MDP) models are widely used for modeling sequential decision-
making problems that arise in engineering, economics, computer science, and the social …

This paper is concerned with motion planning for non-linear robotic systems operating in
constrained environments. A method for computing high-quality trajectories is proposed …

Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …

This thesis studies the use of Monte-Carlo simulations for tree-search problems. The Monte-
Carlo technique we investigate is Monte-Carlo Tree Search (MCTS). It is a best-first search …

Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its
variants, have been used extensively for motion planning. Control barrier functions (CBFs) …

Abstract Model-based algorithms are a class of stochastic search methods that have
successfully addressed some hard deterministic optimization problems. However, their …

We review some recent developments of a class of random search methods: model-based
methods for global optimization problems. Probability models are used to guide the …

Photovoltaic monitoring is essential in all kinds of systems for its efficient and optimal
working. Even after development of modern protection devices, a lot of faults remain …

Artificial intelligence in games often leads to the problem of parameter tuning. Some
heuristics may have coefficients, and they should be tuned to maximize the win rate of the …