Working MCTS implementation

This is a basic working implementation of the MCTS algorithm. Though
currently the algorithm is slow compared with other implemenations, and
makes sub-optimal choices when playing tic-tac-toe. Therefore some
modifications are needed

Cargo.toml

@@ -0,0 +1,16 @@
+[package]
+name = "rustic_mcts"
+version = "0.1.0"
+edition = "2021"
+authors = ["David Kruger <david@krugerlabs.us>"]
+description = "An extensible implementation of Monte Carlo Tree Search (MCTS) using an arena allocator."
+license = "MIT"
+repository = "https://gitlabs.krugerlabs.us/krugd/rustic_mcts"
+readme = "README.md"
+keywords = ["mcts", "rust", "monte_carlo", "tree", "ai", "ml"]
+categories = ["algorithms", "data-structures"]
+
+
+[dependencies]
+rand = "~0.8"
+thiserror = "~2.0"