An intelligent agent that plays Minesweeper using constraint-based reasoning and logical inference.
This AI agent uses knowledge representation and automated reasoning to play Minesweeper optimally. Rather than using machine learning, it employs classical AI techniques to maintain and reason about game constraints.
The AI maintains a knowledge base of logical sentences about the game state. It updates this knowledge with each revealed cell and uses inference to derive which cells must be mines or safe. This demonstrates how agents can reason about partially observable environments.
Each piece of information becomes a constraint linking cells to mine counts. The system propagates these constraints - when one cell is identified as safe or a mine, all related constraints update automatically. This cascading inference allows the AI to derive non-obvious conclusions.
The AI performs sophisticated reasoning by comparing constraints. When one constraint's cells are a subset of another's, it derives new constraints by subtraction. This resolution-based inference discovers hidden relationships between different parts of the board.
The system uses forward chaining to repeatedly apply inference rules until reaching a fixed point. This iterative deepening approach builds complex understanding from simple rules, demonstrating how intelligent behavior emerges from systematic reasoning.
When certain knowledge is exhausted, the AI must choose between exploiting known safe cells or exploring unknown areas. This fundamental AI tradeoff appears in many domains from game playing to robotics.
- Symbolic AI: Shows the power of logic-based reasoning without neural networks
- Explainable Decisions: Every move has clear logical justification
- Constraint Satisfaction: Demonstrates real-world CSP techniques
- Rational Agency: Optimal decision-making under uncertainty