Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models
2026-04-23 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors present Nemobot, a new AI tool that helps create smart game-playing agents using large language models (LLMs). Nemobot can handle different types of games by using math for simple ones, combining classic strategies and data for harder ones, and learning from experience for others. It lets users build and improve AI agents interactively, blending human ideas with machine learning. This work shows progress toward AI that can improve its own game strategies over time.
Large Language ModelsGame-playing AIClaude ShannonMinimax algorithmReinforcement learningSelf-programming AIAgent-based systemsImitation learningHuman-in-the-loopStrategic games
Authors
Chee Wei Tan, Yuchen Wang, Shangxin Guo
Abstract
This paper introduces a new paradigm for AI game programming, leveraging large language models (LLMs) to extend and operationalize Claude Shannon's taxonomy of game-playing machines. Central to this paradigm is Nemobot, an interactive agentic engineering environment that enables users to create, customize, and deploy LLM-powered game agents while actively engaging with AI-driven strategies. The LLM-based chatbot, integrated within Nemobot, demonstrates its capabilities across four distinct classes of games. For dictionary-based games, it compresses state-action mappings into efficient, generalized models for rapid adaptability. In rigorously solvable games, it employs mathematical reasoning to compute optimal strategies and generates human-readable explanations for its decisions. For heuristic-based games, it synthesizes strategies by combining insights from classical minimax algorithms (see, e.g., shannon1950chess) with crowd-sourced data. Finally, in learning-based games, it utilizes reinforcement learning with human feedback and self-critique to iteratively refine strategies through trial-and-error and imitation learning. Nemobot amplifies this framework by offering a programmable environment where users can experiment with tool-augmented generation and fine-tuning of strategic game agents. From strategic games to role-playing games, Nemobot demonstrates how AI agents can achieve a form of self-programming by integrating crowdsourced learning and human creativity to iteratively refine their own logic. This represents a step toward the long-term goal of self-programming AI.