`aima-python`

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 and later 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. You can run the code in an IDE, or from the command line with python -i filename.py where the -i option puts you in an interactive loop where you can run Python functions.

In addition to the filename.py files, there are also filename.ipynb files, which are Jupyter (formerly Ipython) notebooks. You can read these notebooks, and you can also run the code embedded with them. See jupyter.org for instructions on setting up a Jupyter notebook environment.

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 algorithm in the book and in the repository, and the file where they are implemented in the repository. 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. An asterisk next to the file name denotes the algorithm is not fully implemented.

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

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, @reachtarunhere, @MrDupin, and @Chipe1.