Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.

"Jia Jia" can hold a simple conversation and make specific facial expressions when asked, and her creator believes the eerily life-like robot heralds a future of cyborg labour in China. Billed as China's first human-like robot, Jia Jia was first trotted out last year by a team of engineers at the University of Science and Technology of China. Team leader Chen Xiaoping sounded like a proud father as he and his prototype appeared at an economic conference organised by banking giant UBS in Shanghai's futuristic financial centre. Chen predicted that perhaps within a decade artificially intelligent (AI) robots like Jia Jia will begin performing a range of menial tasks in Chinese restaurants, nursing homes, hospitals and households. "In five to 10 years there will be a lot of applications for robots in China," Chen said.