TL;DR
This paper presents Nemobot, an interactive environment leveraging large language models to create adaptable, strategic AI game agents across various game types, advancing toward self-programming AI.
Contribution
It introduces Nemobot, a novel platform for designing and deploying LLM-powered game agents that integrate multiple strategies and learning methods.
Findings
LLM-based agents efficiently handle dictionary-based games.
Agents compute optimal strategies with human-readable explanations.
Reinforcement learning with human feedback refines strategies iteratively.
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…
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