Nemobot brings LLM-powered game agents to interactive AI learning

A paper by Chee Wei Tan, Yuchen Wang, and Shangxin Guo introduces Nemobot, an interactive agentic engineering environment that uses large language models to extend Claude Shannon's taxonomy of game-playing machines. Nemobot supports four game classes: dictionary-based games (using compressed state-action mappings), rigorously solvable games (using mathematical reasoning with human-readable explanations), heuristic-based games (combining classical minimax algorithms with crowdsourced data), and learning-based games (using reinforcement learning with human feedback and self-critique). The environment is programmable, supporting tool-augmented generation and fine-tuning of strategic agents, and is framed as a step toward self-programming AI.