AIMA(Artificial intelligence a modern approach ).

Posted by Adarshreddy Adelli

Saturday, December 9, 2017

aima is considered as the standard textbook for artificial intelligence written by Stuart J. Russell and Peter Norvig. Below is the listings of aima by Googlecode.

Index of Code

| Fig | Page | Name (in book) | Code | |:--------|:---------|:-------------------|:---------| | 2 | 32 | Environment | Environment | | 2.1 | 33 | Agent | Agent | | 2.3 | 34 | Table-Driven-Vacuum-Agent | TableDrivenVacuumAgent | | 2.7 | 45 | Table-Driven-Agent | TableDrivenAgent | | 2.8 | 46 | Reflex-Vacuum-Agent | ReflexVacuumAgent | | 2.10 | 47 | Simple-Reflex-Agent | SimpleReflexAgent | | 2.12 | 49 | Reflex-Agent-With-State | ReflexAgentWithState | | 3.1 | 61 | Simple-Problem-Solving-Agent | SimpleProblemSolvingAgent | | 3 | 62 | Problem | Problem | | 3.2 | 63 | Romania | romania | | 3 | 69 | Node | Node | | 3.7 | 70 | Tree-Search | tree_search| | 3 | 71 | Queue | Queue | | 3.9 | 72 | Tree-Search | tree_search | | 3.13 | 77 | Depth-Limited-Search | depth_limited_search | | 3.14 | 79 | Iterative-Deepening-Search | iterative_deepening_search | | 3.19 | 83 | Graph-Search | graph_search | | 4 | 95 | Best-First-Search | best_first_graph_search | | 4 | 97 | A*-Search | astar_search | | 4.5 | 102 | Recursive-Best-First-Search | recursive_best_first_search | | 4.11 | 112 | Hill-Climbing | hill_climbing | | 4.14 | 116 | Simulated-Annealing | simulated_annealing | | 4.17 | 119 | Genetic-Algorithm | genetic_algorithm | | 4.20 | 126 | Online-DFS-Agent | | | 4.23 | 128 | LRTA*-Agent | | | 5 | 137 | CSP | CSP | | 5.3 | 142 | Backtracking-Search | backtracking_search | | 5.7 | 146 | AC-3 | AC3 | | 5.8 | 151 | Min-Conflicts | min_conflicts | | 6.3 | 166 | Minimax-Decision | minimax_decision | | 6.7 | 170 | Alpha-Beta-Search | alphabeta_search | | 7 | 195 | KB | KB | | 7.1 | 196 | KB-Agent | KB_Agent | | 7.7 | 205 | Propositional Logic Sentence | Expr | | 7.10 | 209 | TT-Entails | tt_entials | | 7 | 215 | Convert to CNF | to_cnf | | 7.12 | 216 | PL-Resolution | pl_resolution | | 7.14 | 219 | PL-FC-Entails? | pl_fc_resolution | | 7.16 | 222 | DPLL-Satisfiable? | dpll_satisfiable | | 7.17 | 223 | WalkSAT | WalkSAT | | 7.19 | 226 | PL-Wumpus-Agent | PLWumpusAgent | | 9 | 273 | Subst | subst | | 9.1 | 278 | Unify | unify | | 9.3 | 282 | FOL-FC-Ask | fol_fc_ask | | 9.6 | 288 | FOL-BC-Ask | fol_bc_ask | | 9.14 | 307 | Otter | | | 11.2 | 380 | Airport-problem | | | 11.3 | 381 | Spare-Tire-Problem | | | 11.4 | 383 | Three-Block-Tower | | | 11 | 390 | Partial-Order-Planner | | | 11.11 | 396 | Cake-Problem | | | 11.13 | 399 | Graphplan | | | 11.15 | 403 | SATPlan | | | 12.1 | 418 | Job-Shop-Problem | | | 12.3 | 421 | Job-Shop-Problem-With-Resources | | | 12.6 | 424 | House-Building-Problem | | | 12.10 | 435 | And-Or-Graph-Search | | | 12.22 | 449 | Continuous-POP-Agent | | | 12.23 | 450 | Doubles-tennis | | | 13.1 | 466 | DT-Agent | DTAgent | | 13 | 469 | Discrete Probability Distribution | DiscreteProbDist | | 13.4 | 477 | Enumerate-Joint-Ask | | | 14.10 | 509 | Elimination-Ask | | | 14.12 | 512 | Prior-Sample | | | 14.13 | 513 | Rejection-Sampling | | | 14.14 | 515 | Likelihood-Weighting | | | 14.15 | 517 | MCMC-Ask | | | 15.4 | 546 | Forward-Backward | | | 15.6 | 552 | Fixed-Lag-Smoothing | | | 15.15 | 566 | Particle-Filtering | | | 16.8 | 603 | Information-Gathering-Agent | | | 17.4 | 621 | Value-Iteration | value_iteration | | 17.7 | 624 | Policy-Iteration | policy_iteration | | 18.5 | 658 | Decision-Tree-Learning | DecisionTreeLearner | | 18.10 | 667 | AdaBoost | | | 18.14 | 672 | Decision-List-Learning | | | 19.2 | 681 | Current-Best-Learning | | | 19.3 | 683 | Version-Space-Learning | | | 19.8 | 696 | Minimal-Consistent-Det | | | 19.12 | 702 | FOIL | | | 20.21 | 742 | Perceptron-Learning | | | 20.25 | 746 | Back-Prop-Learning | | | 21.2 | 768 | Passive-ADP-Agent | | | 21.4 | 769 | Passive-TD-Agent | | | 21.8 | 776 | Q-Learning-Agent | | | 2y2.2 | 796 | Naive-Communicating-Agent | | | 22.7 | 801 | Chart-Parse | Chart | | 23.1 | 837 | Viterbi-Segmentation | viterbi_segment | | 24.21 | 892 | Align ||

You can get detailed notes on aima https://hub.mybinder.org/user/aimacode-aima-python-tpaqn7ak/tree#notebooks

Now we will see how to how to install aima libraries:

1.create a home directory let me say it as home.

2.Download aima python.zip to your home directory.

3.now unzip it in the home directory

      (Note to do this in python 2.3 + )

Adarshreddy Adelli Saturday, December 9, 2017 Artificial intelligence python