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Nemobot Games: Crafting Strategic AI Gaming Agents... | AI Research

Key Takeaways

  • Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models This paper introduces Nemobot, an interactive engineer...
  • 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.
Paper AbstractExpand

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.

Nemobot Games: Crafting Strategic AI Gaming Agents for Interactive Learning with Large Language Models
This paper introduces Nemobot, an interactive engineering environment designed to modernize how we build AI game agents. By leveraging Large Language Models (LLMs), the framework operationalizes Claude Shannon’s classic taxonomy of game-playing machines. The goal is to move beyond static, hard-coded game logic toward a new paradigm of "self-programming" AI, where agents use LLMs to generate, refine, and explain their own strategic decisions through human-AI collaboration.

Extending Shannon’s Taxonomy

The researchers categorize game-playing AI into four classes based on Shannon’s foundational work, enhancing each with modern LLM capabilities:

  • Dictionary-based games: The system compresses vast state-action mappings into generalized models, allowing for rapid adaptability without needing exhaustive memory.

  • Rigorous, solvable games: The AI uses mathematical reasoning to compute optimal moves and provides human-readable explanations for its logic.

  • Heuristic-based games: The agent synthesizes strategies by combining classical minimax algorithms with crowd-sourced human insights.

  • Learning-based games: The agent employs reinforcement learning, using human feedback and self-critique to iteratively improve its performance through trial and error.

Programmable Prompt Engineering

A core innovation of Nemobot is the use of "programmable prompts." Rather than relying on simple, non-deterministic queries, developers treat LLM functions as modular subroutines. This allows the AI to dynamically adapt to game states in real-time. The framework utilizes a concept called "neuralized memoization," which acts as a modern evolution of traditional caching. By storing and reusing successful strategies or semantic insights, the system reduces computational redundancy and allows agents to learn from past interactions, effectively enabling them to write their own instructions over time.

Collaborative Crowdsourcing

Nemobot emphasizes the role of human creativity in AI development. By integrating crowdsourcing, the framework allows diverse groups of users to contribute strategies, refine prompts, and provide feedback. This collaborative approach creates a self-reinforcing cycle: human players interact with the AI, their feedback is used to train and refine the agent’s logic, and the agent becomes more sophisticated. This process is particularly effective in educational settings, where students can use the platform to experiment with AI logic, debug agent behavior, and gain a deeper understanding of strategic game design.

A Step Toward Self-Programming AI

The framework serves as a practical research tool for exploring the long-term goal of self-programming systems. By providing a structured, inspectable environment, Nemobot bridges the gap between opaque, "black-box" AI behavior and the need for reproducible, programmable logic. It enables developers to move away from manually coding every scenario, instead guiding the AI to generate its own strategic modules. This shift transforms the developer's role from a direct instructor to a facilitator, helping the machine evolve its own decision-making capabilities through a blend of algorithmic rigor and human-guided learning.

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