plan for week2 lab

Author: abeljim

lab_plans/week2.md

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+# Week 2 Lab Plan: Setting Up AIMA Python Environment and Agent Testing
+
+## Overview
+This lab session will guide students through setting up the AIMA Python environment and working with intelligent agents using the provided `agents.ipynb` notebook. Students will learn to run agent simulations and make modifications to understand how agents interact with their environments.
+
+## Learning Objectives
+By the end of this lab, students will be able to:
+- Set up the AIMA Python environment using conda
+- Run Jupyter notebooks for AI agent simulations
+- Understand basic agent-environment interactions
+- Modify agent behaviors and environment parameters
+- Experiment with 2D grid world environments
+
+## Prerequisites
+- Basic Python knowledge
+- Understanding of AI agent concepts from lectures
+- Laptop with internet connection
+
+## Lab Setup Instructions
+
+### 1. Environment Setup (15 minutes)
+
+#### Step 1: Install Conda/Miniconda
+If students don't have conda installed:
+- Download Miniconda from https://docs.conda.io/en/latest/miniconda.html
+- Follow installation instructions for their operating system
+
+#### Step 2: Clone the Repository
+```bash
+git clone https://github.com/your-repo/aima-python-eecs118-fall-25.git
+cd aima-python-eecs118-fall-25
+```
+
+#### Step 3: Create and Activate Conda Environment
+```bash
+conda env create -f environment.yml
+conda activate aima-python
+```
+
+#### Step 4: Launch Jupyter Notebook
+```bash
+jupyter notebook
+```
+
+### 2. Exploring the Agents Notebook
+
+#### Understanding the Basic Components
+Students will work through the `agents.ipynb` notebook to understand:
+
+1. **Agent Class Structure**
+   - Review the `Agent` base class
+   - Understand agent properties: `alive`, `bump`, `holding`, `performance`, `program`
+
+2. **Environment Class Structure**
+   - Review the `Environment` base class
+   - Understand key methods: `percept()`, `execute_action()`, `is_done()`
+
+3. **Simple Agent Example - BlindDog**
+   - Run the BlindDog simulation in 1D park
+   - Observe how the agent moves and interacts with food/water
+   - Understand the agent program logic
+
+4. **2D Environment - Park2D**
+   - Run the EnergeticBlindDog simulation
+   - Observe 2D movement and visual representation
+   - Understand direction handling and boundary detection
+
+### 3. Hands-On Exercises
+
+#### Exercise 1: Modify Grid World Size
+**Objective**: Change the park dimensions and observe behavior
+
+**Task**: In the Park2D example, modify the park size from (5,5) to (8,8)
+```python
+# Find this line in the notebook:
+park = Park2D(5,5, color={'EnergeticBlindDog': (200,0,0), 'Water': (0, 200, 200), 'Food': (230, 115, 40)})
+
+# Change to:
+park = Park2D(8,8, color={'EnergeticBlindDog': (200,0,0), 'Water': (0, 200, 200), 'Food': (230, 115, 40)})
+```
+
+**Questions for Students**:
+- How does the larger environment affect the dog's ability to find food and water?
+- Does the random movement strategy become less efficient?
+
+#### Exercise 2: Change Initial Agent Position
+**Objective**: Experiment with different starting positions
+
+**Task**: Modify the dog's starting position
+```python
+# Find this line:
+park.add_thing(dog, [0,0])
+
+# Try different starting positions:
+park.add_thing(dog, [4,4])  # Center of 8x8 grid
+# or
+park.add_thing(dog, [7,7])  # Corner position
+```
+
+**Questions for Students**:
+- How does starting position affect the agent's performance?
+- Which starting position seems most efficient for finding resources?
+
+#### Exercise 3: Implement Barriers/Walls
+**Objective**: Add obstacles to make the environment more challenging
+
+**Task**: Create a new `Wall` class and add barriers to the environment
+```python
+class Wall(Thing):
+    pass
+
+# Add walls to the environment
+wall1 = Wall()
+wall2 = Wall()
+park.add_thing(wall1, [2,2])
+park.add_thing(wall2, [3,3])
+
+# Update the color dictionary
+park = Park2D(8,8, color={
+    'EnergeticBlindDog': (200,0,0),
+    'Water': (0, 200, 200),
+    'Food': (230, 115, 40),
+    'Wall': (100, 100, 100)  # Gray color for walls
+})
+```