Algorithms from AIMA in Haskell
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README.md

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