🧠 CLARION-Inspired Cognitive Agent Simulation

This project implements a simple cognitive agent-based simulation inspired by the CLARION cognitive architecture.
The system simulates an agent navigating a grid world to reach a goal, using a combination of implicit decision-making, explicit reasoning, and episodic memory.

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🎯 How It Works

At each step:

1. The agent observes its current state in the environment.
2. An implicit procedure suggests an action (e.g. 'DOWN', 'RIGHT').
3. The agent checks:
   • Is the move within the grid bounds?
   • Is there a memorized obstacle at the intended position?
4. If the move is invalid:
   • The agent memorizes the obstacle (if applicable).
   • Chooses an alternative valid action from remaining options.
5. The agent moves to the new position and repeats the process until:
   • It reaches the goal.
   • A maximum number of steps is exceeded.

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⚙️ Components

Module	Description
Environment	Defines the grid, goal position, and boundary checks.
Agent	Handles decision-making using implicit and explicit logic.
ImplicitProcedure	Provides suggested actions based on the current state.
run_simulation.py	Orchestrates an episode, logging each step.

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📊 Example Run

Step 1
Agent at: [0, 0], Goal at: (4, 4)
Implicit Action: RIGHT
Intended Position: [1, 0]

Step 2
Agent at: [1, 0], Goal at: (4, 4)
Implicit Action: RIGHT
Intended Position: [2, 0]

...

✅ Goal reached in 8 steps!

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🚀 Running the Simulation

Prerequisites:

• Python 3.x

Run:

python run_simulation.py

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📖 Cognitive Principles Demonstrated

• Implicit Decision-Making: Predictive, automatic action suggestions.
• Explicit Reasoning: Alternative selection when implicit moves fail.
• Episodic Memory: Memorizing encountered obstacles to avoid loops.
• Adaptive Planning: Choosing valid moves dynamically based on environment state.

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