Building an Intelligent Pac-Man Agent

Overview

This project series incrementally develops a fully autonomous Pac-Man agent, transforming it from a weak, rule-driven system into an intelligent decision-making agent capable of planning, reasoning under uncertainty, and learning from interaction.

The projects are organized as a coherent progression, each introducing a more advanced AI paradigm.

Logic & Classical Planning

In the initial stage, Pac-Man operates under logic-based representations and classical planning:

• Explicit modeling of world states, actions, constraints, and goals
• Automatic generation of feasible action sequences
• Deterministic planning in fully observable environments

This stage establishes the foundation for symbolic reasoning and goal-directed behavior.

Bayesian Networks

To handle uncertainty, Bayesian networks are introduced:

• Probabilistic modeling of hidden variables
• Inference under noisy or incomplete observations
• Decision-making based on belief states rather than exact knowledge

This enables Pac-Man to reason probabilistically instead of relying on fixed rules.

Ghostbusters: Tracking Under Uncertainty

In the Ghostbusters setting, Pac-Man faces invisible ghosts and noisy sensors:

• Bayesian filtering for belief state updates
• Particle filtering for continuous ghost localization
• Sequential decision-making in partially observable, dynamic environments

This stage bridges probabilistic inference and adversarial game dynamics.

Reinforcement Learning

Finally, the agent transitions to reinforcement learning, removing the need for handcrafted models:

• Learning policies directly from environment interaction
• Optimizing long-term rewards instead of immediate objectives
• Adapting behavior through trial and error

At this stage, Pac-Man becomes a self-improving agent capable of autonomous strategy learning.