README.md

UC Berkeley CS 188 Artificial Intelligence

Assignment code for UC Berkeley CS 188 Artificial Intelligence.

For open course material in edX, using this class: BerkeleyX: CS188.1x Artificial Intelligence

Projects

1. Project 0: Python Refresher
   • addition.py
   • buyLotsOfFruit.py
   • shopSmart.py
2. Project 1: Search (Python 3 Version)
   • search.py
   • searchAgents.py

Quizzes

1. Lecture 2: Uninformed Search
   • Quiz 1: Planning Agents vs. Reflex Agents
   • Quiz 2: Safe Passage
   • Quiz 3: State Space Graphs and Search Trees
   • Quiz 4: Depth-First Tree Search
   • Quiz 5: Depth-First Tree Search: Space and Time Complexity
   • Quiz 6: Breadth-First Tree Search
   • Quiz 7: Breadth-First Tree Search: Space and Time Complexity
   • Quiz 9: Which Search Algorithm?
   • Quiz 10: Which Search Algorithm?
   • Quiz 11: Uniform Cost Search
   • Quiz 12: Which Search Algorithm?
   • Quiz 13: Which Search Algorithm?
2. Lecture 3: Informed Search
   • Quiz 1: Search Execution
   • Quiz 2: Greedy Search
   • Quiz 3: A* Tree Search
   • Quiz 4: A* Tree Search
   • Quiz 5: Which Search Algorithm?
   • Quiz 6: Which Search Algorithm?
   • Quiz 7: Which Search Algorithm?
   • Quiz 8: Which Search Algorithm?
   • Quiz 9: Which Search Algorithm?
   • Quiz 10: Admissible Heuristics
   • Quiz 11: Combining Heuristics
   • Quiz 12: Consistency
3. Lecture 4: CSPs
   • Quiz 1: Constraints
   • Quiz 2: Constraint Graphs
   • Quiz 3: Constraints
   • Quiz 4: Backtracking Search
   • Quiz 5: Forward Checking
   • Quiz 6: Arc Consistency
   • Quiz 7: Arc Consistency
   • Quiz 8: Least Constraining Value
4. Lecture 5: CSPs II
   • Quiz 1: Tree-Structured CSPs
   • Quiz 2: Smallest Cutset
   • Quiz 3: Min-Conflicts
   • Quiz 4: Hill Climbing
5. Lecture 6: Adversarial Search
   • Quiz 1: Minimax
   • Quiz 2: Evaluation and Collaboration
   • Quiz 3: Alpha-Beta Pruning
6. Lecture 7: Uncertainty and Utilities
   • Quiz 1: Expectimax
   • Quiz 3: Other Games
   • Quiz 4: Monotonic Transformations
   • Quiz 5: Rationality
   • Quiz 6: Certainty Equivalent Values
7. Lecture 8: Markov Decision Processes
   • Quiz 1: MDP Notation
   • Quiz 2: Discounting
   • Quiz 3: Solving MDPs
   • Quiz 4: Value Iteration
8. Lecture 9: Markov Decision Processes II
   • Quiz 1: The Bellman Equation
   • Quiz 2: Policy Evaluation
   • Quiz 3: Policy Iteration
9. Lecture 10: Reinforcement Learning
   • Quiz 1: Reinforcement Learning
   • Quiz 2: Model-Based Learning
   • Quiz 3: Passive Reinforcement Learning
   • Quiz 4: TD Learning
   • Quiz 5: Q-Learning
10. Lecture 11: Reinforcement Learning II
    • Quiz 1: Exploration vs. Exploitation
    • Quiz 2: Feature-Based Representations

Homeworks

1. Homework 1 - Search
   • Question 1: Search Trees
   • Question 2: Depth-First Graph Search
   • Question 3: Breadth-First Graph Search
   • Question 4: A* Graph Search
   • Question 5: Hive Minds: Lonely Bug
   • Question 6: Hive Minds: Swarm Movement
   • Question 7: Hive Minds: Migrating Birds
   • Question 8: Hive Minds: Jumping Bug
   • Question 9: Hive Minds: Lost at Night
   • Question 10: Early Goal Checking Graph Search
   • Question 11: Lookahead Graph Search
   • Question 12: Memory Efficient Graph Search
   • Question 13: A*-CSCS
2. Homework 1 - Search (Practice)
   • Question 1: Search Trees
   • Question 2: Depth-First Graph Search
   • Question 3: Breadth-First Graph Search
   • Question 4: A* Graph Search
   • Question 5: Hive Minds: Lonely Bug
   • Question 6: Hive Minds: Swarm Movement
   • Question 7: Hive Minds: Migrating Birds
   • Question 8: Hive Minds: Jumping Bug
   • Question 9: Hive Minds: Lost at Night
   • Question 10: Early Goal Checking Graph Search
   • Question 11: Lookahead Graph Search
   • Question 12: Memory Efficient Graph Search
   • Question 13: A*-CSCS
3. Homework 2 - CSPs
   • Question 1: Campus Layout
   • Question 2: CSP Properties
   • Question 3: 4-Queens
   • Question 4: Tree-Structured CSPs
   • Question 5: Solving Tree-Structured CSPs
   • Question 6: Arc Consistency
   • Question 7: Arc Consistency Properties
   • Question 8: Backtracking Arc Consistency
4. Homework 2 - CSPs (Practice)
   • Question 1: Campus Layout
   • Question 2: CSP Properties
   • Question 3: 4-Queens
   • Question 4: Tree-Structured CSPs
   • Question 5: Solving Tree-Structured CSPs
   • Question 6: Arc Consistency
   • Question 7: Arc Consistency Properties
   • Question 8: Backtracking Arc Consistency
5. Homework 3 - Games
   • Question 1: Minimax
   • Question 2: Expectiminimax
   • Question 3: Unknown Leaf Value
   • Question 4: Alpha-Beta Pruning
   • Question 5.1: Non-Zero-Sum Games
   • Question 5.2: Properties of Non-Zero-Sum Games
   • Question 6: Possible Pruning
   • Question 7: Suboptimal Strategies
   • Question 8: Shallow Search
   • Question 9: Rationality of Utilities
   • Question 10: Certainty Equivalent Values
   • Question 11: Preferences and Utilities
6. Homework 3 - Games (Practice)
   • Question 1: Minimax
   • Question 2: Expectiminimax
   • Question 3: Unknown Leaf Value
   • Question 4: Alpha-Beta Pruning
   • Question 5.1: Non-Zero-Sum Games
   • Question 5.2: Properties of Non-Zero-Sum Games
   • Question 6: Possible Pruning
   • Question 7: Suboptimal Strategies
   • Question 8: Shallow Search
   • Question 9: Rationality of Utilities
   • Question 10: Certainty Equivalent Values
   • Question 11: Preferences and Utilities
7. Homework 4 - MDPs
   • Question 1: Solving MDPs
   • Question 2: Value Iteration Convergence Values
   • Question 3: Value Iteration: Cycle
   • Question 4: Value Iteration: Properties
   • Question 5: Value Iteration: Convergence
   • Question 6: Policy Evaluation
   • Question 7: Policy Iteration
   • Question 8: Policy Iteration: Cycle
   • Question 9: Wrong Discount Factor
   • Question 10: MDP Properties
   • Question 11: Policies
8. Homework 4 - MDPs (Practice)
   • Question 1: Solving MDPs
   • Question 2: Value Iteration Convergence Values
   • Question 3: Value Iteration: Cycle
   • Question 4: Value Iteration: Properties
   • Question 5: Value Iteration: Convergence
   • Question 6: Policy Evaluation
   • Question 7: Policy Iteration
   • Question 8: Policy Iteration: Cycle
   • Question 9: Wrong Discount Factor
   • Question 10: MDP Properties
   • Question 11: Policies
9. Homework 5 - Reinforcement Learning
   • Question 1: Model-Based RL: Grid
   • Question 2: Model-Based RL: Cycle
   • Question 3: Direct Evaluation
   • Question 4: Temporal Difference Learning
   • Question 5: Model-Free RL: Cycle
   • Question 6: Q-Learning Properties
   • Question 7: Exploration and Exploitation
   • Question 8: Feature-Based Representation: Actions
   • Question 9: Feature-Based Representation: Update
10. Homework 5 - Reinforcement Learning (Practice)
    • Question 1: Model-Based RL: Grid
    • Question 2: Model-Based RL: Cycle
    • Question 3: Direct Evaluation
    • Question 4: Temporal Difference Learning
    • Question 5: Model-Free RL: Cycle
    • Question 6: Q-Learning Properties
    • Question 7: Exploration and Exploitation
    • Question 8: Feature-Based Representation: Actions
    • Question 9: Feature-Based Representation: Update

Midterm Exam

0. Practice I
   • Question 1: Search
   • Question 2: Hive Minds
   • Question 3: CSPs: Time Management
   • Question 4: Surrealist Pacman
   • Question 5: MDPs: Grid-World Water Park
   • Question 6: Short Answer: Search
   • Question 7: Short Answer: Iterative Deepening
   • Question 8: Short Answer: Dominance
   • Question 9: Short Answer: Heuristics
   • Question 10: Short Answer: CSP
   • Question 11: Short Answer: Games
1. Practice I (Practice)
   • Question 1: Search
   • Question 2: Hive Minds
   • Question 3: CSPs: Time Management
   • Question 4: Surrealist Pacman
   • Question 5: MDPs: Grid-World Water Park
   • Question 6: Short Answer: Search
   • Question 7: Short Answer: Iterative Deepening
   • Question 8: Short Answer: Dominance
   • Question 9: Short Answer: Heuristics
   • Question 10: Short Answer: CSP
   • Question 11: Short Answer: Games
2. Practice II
   • Question 1: Search
   • Question 2: Search: Heuristic Function Properties
   • Question 3: Search: Slugs
   • Question 4: Value Functions
   • Question 5: CSPs: CS188x Offices
   • Question 6: CSP Properties
   • Question 7: Games: Alpha-Beta Pruning
   • Question 8: Utilities: Low/High
   • Question 9: MDPs and Reinforcement Learning: Mini-Grids
3. Practice II (Practice)
   • Question 1: Search
   • Question 2: Search: Heuristic Function Properties
   • Question 3: Search: Slugs
   • Question 4: Value Functions
   • Question 5: CSPs: CS188x Offices
   • Question 6: CSP Properties
   • Question 7: Games: Alpha-Beta Pruning
   • Question 8: Utilities: Low/High
   • Question 9: MDPs and Reinforcement Learning: Mini-Grids
4. Practice III
   • Question 1: CSPs: Final Exam Staff Assignments
   • Question 2: Solving Search Problems with MDPs
   • Question 3: X-Values
   • Question 4: Games with Magic
   • Question 5: Pruning and Child Expansion Ordering
   • Question 6: A* Search: Batch Node Expansion
5. Practice III (Practice)
   • Question 1: CSPs: Final Exam Staff Assignments
   • Question 2: Solving Search Problems with MDPs
   • Question 3: X-Values
   • Question 4: Games with Magic
   • Question 5: Pruning and Child Expansion Ordering
   • Question 6: A* Search: Batch Node Expansion
6. Exam
   • Question 1: Pacman's Tour of San Francisco
   • Question 2: Missing Heuristic Values
   • Question 3: PAC-CORP Assignments
   • Question 4: k-CSPs
   • Question 5: One Wish Pacman
   • Question 6: AlphaBetaExpinimax
   • Question 7: Lotteries in Ghost Kingdom
   • Question 8: Indecisive Pacman
   • Question 9: Reinforcement Learning
   • Question 10: Potpourri