/aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

Primary LanguageJupyter NotebookMIT LicenseMIT



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aima-python Build Status Binder

Python code for the book Artificial Intelligence: A Modern Approach. You can use this in conjunction with a course on AI, or for study on your own. We're looking for solid contributors to help.

Python 3.4

This code is in Python 3.4 (Python 3.5, also works, but Python 2.x does not). You can install the latest Python version or use a browser-based Python interpreter such as repl.it.

Structure of the Project

When complete, this project will have Python code for all the pseudocode algorithms in the book. For each major topic, such as logic, we will have the following three files in the main branch:

  • logic.py: Implementations of all the pseudocode algorithms, and necessary support functions/classes/data.
  • logic.ipynb: A Jupyter (IPython) notebook that explains and gives examples of how to use the code.
  • tests/test_logic.py: A lightweight test suite, using assert statements, designed for use with py.test, but also usable on their own.

Index of Algorithms

Here is a table of algorithms, the figure, name of the code in the book and in the repository, and the file where they are implemented in the code. This chart was made for the third edition of the book and needs to be updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The aima-pseudocode project describes all the algorithms from the book.

Figure Name (in 3rd edition) Name (in repository) File
2.1 Environment Environment agents.py
2.1 Agent Agent agents.py
2.3 Table-Driven-Vacuum-Agent TableDrivenVacuumAgent agents.py
2.7 Table-Driven-Agent TableDrivenAgent agents.py
2.8 Reflex-Vacuum-Agent ReflexVacuumAgent agents.py
2.10 Simple-Reflex-Agent SimpleReflexAgent agents.py
2.12 Model-Based-Reflex-Agent ReflexAgentWithState agents.py
3 Problem Problem search.py
3 Node Node search.py
3 Queue Queue utils.py
3.1 Simple-Problem-Solving-Agent SimpleProblemSolvingAgent search.py
3.2 Romania romania search.py
3.7 Tree-Search tree_search search.py
3.7 Graph-Search graph_search search.py
3.11 Breadth-First-Search breadth_first_search search.py
3.14 Uniform-Cost-Search uniform_cost_search search.py
3.17 Depth-Limited-Search depth_limited_search search.py
3.18 Iterative-Deepening-Search iterative_deepening_search search.py
3.22 Best-First-Search best_first_graph_search search.py
3.24 A*-Search astar_search search.py
3.26 Recursive-Best-First-Search recursive_best_first_search search.py
4.2 Hill-Climbing hill_climbing search.py
4.5 Simulated-Annealing simulated_annealing search.py
4.8 Genetic-Algorithm genetic_algorithm search.py
4.11 And-Or-Graph-Search and_or_graph_search search.py
4.21 Online-DFS-Agent online_dfs_agent search.py
4.24 LRTA*-Agent LRTAStarAgent search.py
5.3 Minimax-Decision minimax_decision games.py
5.7 Alpha-Beta-Search alphabeta_search games.py
6 CSP CSP csp.py
6.3 AC-3 AC3 csp.py
6.5 Backtracking-Search backtracking_search csp.py
6.8 Min-Conflicts min_conflicts csp.py
6.11 Tree-CSP-Solver tree_csp_solver csp.py
7 KB KB logic.py
7.1 KB-Agent KB_Agent logic.py
7.7 Propositional Logic Sentence Expr logic.py
7.10 TT-Entails tt_entials logic.py
7.12 PL-Resolution pl_resolution logic.py
7.14 Convert to CNF to_cnf logic.py
7.15 PL-FC-Entails? pl_fc_resolution logic.py
7.17 DPLL-Satisfiable? dpll_satisfiable logic.py
7.18 WalkSAT WalkSAT logic.py
7.20 Hybrid-Wumpus-Agent
7.22 SATPlan SAT_plan logic.py
9 Subst subst logic.py
9.1 Unify unify logic.py
9.3 FOL-FC-Ask fol_fc_ask logic.py
9.6 FOL-BC-Ask fol_bc_ask logic.py
9.8 Append
10.1 Air-Cargo-problem air_cargo planning.py
10.2 Spare-Tire-Problem spare_tire planning.py
10.3 Three-Block-Tower three_block_tower planning.py
10.7 Cake-Problem have_cake_and_eat_cake_too planning.py
10.9 Graphplan GraphPlan planning.py
10.13 Partial-Order-Planner
11.1 Job-Shop-Problem-With-Resources
11.5 Hierarchical-Search
11.8 Angelic-Search
11.10 Doubles-tennis double_tennis_problem planning.py
13 Discrete Probability Distribution ProbDist probability.py
13.1 DT-Agent DTAgent probability.py
14.9 Enumeration-Ask enumeration_ask probability.py
14.11 Elimination-Ask elimination_ask probability.py
14.13 Prior-Sample prior_sample probability.py
14.14 Rejection-Sampling rejection_sampling probability.py
14.15 Likelihood-Weighting likelihood_weighting probability.py
14.16 Gibbs-Ask gibbs_ask probability.py
15.4 Forward-Backward forward_backward probability.py
15.6 Fixed-Lag-Smoothing fixed_lag_smoothing probability.py
15.17 Particle-Filtering particle_filtering probability.py
16.9 Information-Gathering-Agent
17.4 Value-Iteration value_iteration mdp.py
17.7 Policy-Iteration policy_iteration mdp.py
17.7 POMDP-Value-Iteration
18.5 Decision-Tree-Learning DecisionTreeLearner learning.py
18.8 Cross-Validation cross_validation learning.py
18.11 Decision-List-Learning DecisionListLearner learning.py
18.24 Back-Prop-Learning BackPropagationLearner learning.py
18.34 AdaBoost AdaBoost learning.py
19.2 Current-Best-Learning
19.3 Version-Space-Learning
19.8 Minimal-Consistent-Det
19.12 FOIL
21.2 Passive-ADP-Agent PassiveADPAgent rl.py
21.4 Passive-TD-Agent PassiveTDAgent rl.py
21.8 Q-Learning-Agent QLearningAgent rl.py
22.1 HITS HITS nlp.py
23 Chart-Parse Chart nlp.py
23.5 CYK-Parse CYK_parse nlp.py
25.9 Monte-Carlo-Localization

Index of data structures

Here is a table of the implemented data structures, the figure, name of the implementation in the repository, and the file where they are implemented.

Figure Name (in repository) File
3.2 romania_map search.py
4.9 vacumm_world search.py
4.23 one_dim_state_space search.py
6.1 australia_map search.py
7.13 wumpus_world_inference logic.py
7.16 horn_clauses_KB logic.py
17.1 sequential_decision_environment mdp.py
18.2 waiting_decision_tree learning.py

Acknowledgements

Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the contributors who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, and @reachtarunhere.