Ometeotl: An Abstract Meta-Model Framework Designed for AI Strategic Decision-Making

Ometeotl is an abstract meta-model framework specifically designed to equip AI systems with strategic decision-making capabilities. It supports both symbolic reasoning and generative AI, enabling applications in strategic thinking, decision-making, and game theory simulations. The name 'Ometeotl' derives from an Aztec dual deity symbolizing the unity of opposites, reflecting its focus on balancing contradictions in complex strategic scenarios. This framework is versatile, applicable to domains like business strategy, military simulation, game AI, and autonomous driving.

Origin & Name

The framework's name 'Ometeotl' comes from Aztec mythology, representing a dual deity that symbolizes unity of opposites, hinting at its ability to handle conflicting elements in strategic contexts.

What is Ometeotl?

Unlike traditional AI frameworks that focus on concrete algorithm implementations, Ometeotl provides high-level abstract models to describe strategic thinking, decision processes, and game interactions. This meta-model approach gives it strong versatility and adaptability across various fields, from business to military to game AI.

Abstracting Strategic Thinking

Ometeotl breaks down strategic thinking into computable elements, aiming to formalize human strategic intuition into mathematical models. Key components include:

• Situation awareness (understanding environment, resources, constraints)
• Goal hierarchy (managing short/long-term interrelated goals)
• Opponent modeling (predicting others' behaviors/intentions)
• Risk assessment (quantifying uncertainty and consequences)
• Resource allocation (optimizing limited resources across goals)

Unifying Symbolic & Generative AI

A standout feature is its compatibility with both symbolic AI (rules, logic, knowledge representation) and generative AI (LLMs). Symbolic AI offers interpretability and precision, while generative AI brings creativity and adaptability—Ometeotl acts as a bridge for their synergy in strategic decisions.

Multi-Agent Interaction

Ometeotl supports various game theory scenarios:

• Zero-sum games (competitive, one's gain equals another's loss)
• Cooperative games (alliance formation, benefit distribution)
• Incomplete information games (decision-making with partial info)
• Dynamic games (multi-stage decisions affecting future options)
• Evolutionary games (strategy spread in groups)

Nash Equilibrium & Strategy Optimization

The framework may implement algorithms to compute Nash equilibrium (stable strategy combinations for rational agents). It also supports strategy iteration and optimization, allowing AI to learn and improve from experience.

Ometeotl's versatility enables its use in multiple domains:

• Business: Simulate market competition, pricing strategies, and M&A decisions to test options before execution.
• Military: Tactical planning, force deployment, and conflict simulation (e.g., red-blue adversarial exercises, asymmetric warfare).
• Game AI: Develop strategic opponents for strategy/4X games (explore, expand, exploit, conquer).
• Autonomous Driving: Predict road user interactions, assess risks, and plan long-term paths.
• Diplomacy: Simulate international alliances, trade negotiations, and conflict escalation for policy support.

Meta-Model Advantages

As a meta-model, Ometeotl offers:

1. Domain agnosticism: Applicable to diverse fields.
2. Composability: Freely combine different strategic components.
3. Verifiability: Easier formal verification of abstract models.
4. Extensibility: Integrate new strategic concepts easily.

Integration with AI Ecosystem

Ometeotl may provide interfaces to mainstream AI frameworks (TensorFlow, PyTorch) and LLM APIs, leveraging existing infrastructure.

Key Challenges

• Computational Complexity: Strategic decisions involve large state spaces; Nash equilibrium computation is NP-hard, requiring approximations.
• Uncertainty Handling: Real-world scenarios have incomplete info and random changes—need strong probabilistic reasoning.
• Interpretability: AI strategic decisions need transparency; Ometeotl's abstract model may offer better interpretability than black-box models.
• Ethics: Applications in military/business raise ethical concerns—need built-in ethical constraints and value alignment.

Future Directions

Addressing these challenges to enhance the framework's practicality and ethical alignment.

Ometeotl represents an attempt to elevate AI from tactical execution to strategic thinking. Its abstract meta-model framework lays the groundwork for strategic AI development across domains. Though in early stages, its design concepts of unifying symbolic/generative AI, abstracting strategic thinking, and supporting game theory simulations point to a promising direction.

For researchers and developers in AI decision-making, game theory applications, or multi-agent systems, Ometeotl is a project worth attention as it bridges theoretical game theory and practical AI applications.