jadegeek/aima-haskell

Algorithms from AIMA in Haskell

aima-haskell

Algorithms from Artificial Intelligence: A Modern Approach by Russell and Norvig.

Part I. Artificial Intelligence

2. Intelligent Agents

• Environment (Fig 2.1)
• Agent (Fig 2.1)

Part II. Problem Solving

3. Searching

Completed:

• Problem
• Node
• Tree Search (Fig 3.7)
• Graph Search (Fig 3.7)
• Breadth First Search (Fig 3.11)
• Uniform Cost Search (Fig 3.14)
• Depth First Search
• Depth-Limited Search (Fig 3.17)
• Iterative Deepening Search (Fig 3.18)
• Greedy Best First Search
• A* Search

To do:

• Recursive Best First Search (Fig 3.26)
• Iterative-Deepening A*
• Memory-Bounded A* (MA*)
• Simplified MA*
• Bidirectional Search
• Eight Puzzle

4. Beyond Classical Search

Completed:

• Hill-Climbing (Fig 4.2)
• Simulated Annealing (Fig 4.5)

To do:

• Genetic Algorithm (Fig 4.8)
• And/Or Graph Search (Fig 4.11)
• Online Depth First Search (Fig 4.21)
• LRTA* (Fig 4.24)

5. Adversarial Search

Completed:

• Minimax Search (Fig 5.3)
• Alpha-Beta Search (Fig 5.7)
• Searching with cutoff

To do:

• Stochastic games

6. Constraint Satisfaction Problems

Completed:

• AC3 (Fig 6.3)
• Backtracking Search (Fig 6.5)

To do:

• Min Conflicts (Fig 6.8)
• Tree CSP Solver (Fig 6.11)

Part III. Knowledge, Reasoning and Planning

7. Logical Agents

Completed:

• TT-Entails (Fig 7.10)
• PL-Resolution (Fig 7.12)
• PL-FC-Entails (Fig 7.15)

To do:

• DPLL-Satisfiable (Fig 7.17)
• WalkSAT (Fig 7.18)
• Wumpus World

8-9. First-Order Logic

Completed:

• Unify (Fig 9.1)
• FOL-FC-Ask (Fig 9.3)

To do:

• FOL-BC-Ask (Fig 9.6)

10. Classical Planning

11. Planning and Acting in the Real World

12. Knowledge Representation

Part IV. Uncertain Knowledge and Reasoning

14. Probabilistic Reasoning

Completed:

• Enumeration-Ask (Fig 14.9)
• Elimination-Ask (Fig 14.11)
• Prior-Sample (Fig 14.13)
• Rejection-Sampling (Fig 14.14)
• Likelihood-Weighting (Fig 14.15)

To do:

• Gibbs-Ask (Fig 14.16)
• Fit Bayes Networks from data

15. Probabilistic Reasoning Over Time

To do:

• Kalman Filter
• Particle Filter (Fig 15.17)

16/17. Making Complex Decisions

Completed:

• Value Iteration (Fig 17.4)
• Policy Iteration (Fig 17.7)

To do:

• POMDP Value Iteration (Fig 17.9)

18. Learning from Examples

Completed:

• Decision Tree Learning (Fig 18.5)
• Cross-Validation (Fig 18.8)
• Linear regression
• Logistic regression

To do:

• Decision List Learning (Fig 18.11)
• Artificial Neural Networks
• Back Prop Learning (Fig 18.24)
• Nearest Neighbour
• Nonparametric Regression
• Regression Trees
• Support Vector Machines
• AdaBoost (Fig 18.34)

20. Statistical Learning

To do:

• Naive Bayes

21. Reinforcement Learning

To do:

• TD-Learning
• Q-Learning
• SARSA