Online Code Repository

Supported Implementations

• Java: aima-java project, by Ravi Mohan. Read Me file.
• Python: aima-python project, by Peter Norvig. Read Me file.
• Lisp: AIMA Lisp Overview, by Stuart Russell and Peter Norvig.
• Data: aima-data project.

Unsupported Implementations

Implementation Choices

Language	23 Sep 2004	2 Feb 2005	15 Jun 2007
none	8,080	20,100	75,200
java	1,990	4,930	44,200
c++	875	1,820	35,300
lisp	844	974	30,100
prolog	789	2,010	23,200
python	785	1,240	18,400

Of course, neither recall nor precision is perfect for these queries, nor is the estimated number of results guaranteed to be accurate, but they offer a rough estimate of popularity. Also, the links in the table let you investigate individual courses using each language.

Index of Pseudocode in Book Fig. Page Name (in Book) Kind 2 32 Environment type 2.1 33 Agent fn 2.3 34 Table-Driven-Vacuum-Agent fn 2.7 45 Table-Driven-Agent fn 2.8 46 Reflex-Vacuum-Agent fn 2.10 47 Simple-Reflex-Agent fn 2.12 49 Reflex-Agent-With-State fn 3.1 61 Simple-Problem-Solving-Agent fn 3 62 Problem type 3.2 63 Romania data 3 69 Node type 3.7 70 Tree-Search fn 3 71 Queue type 3.9 72 Tree-Search fn 3.13 77 Depth-Limited-Search fn 3.14 79 Iterative-Deepening-Search fn 3.19 83 Graph-Search fn 4 95 Best-First-Search fn 4 97 A*-Search fn 4.5 102 Recursive-Best-First-Search fn 4.11 112 Hill-Climbing fn 4.14 116 Simulated-Annealing fn 4.17 119 Genetic-Algorithm fn 4.20 126 Online-DFS-Agent fn 5 137 CSP type 5.3 142 Backtracking-Search fn 5.7 146 AC-3 fn 5.8 151 Min-Conflicts fn 6.3 166 Minimax-Decision fn 6.7 170 Alpha-Beta-Search fn 7 195 KB type 7.1 196 KB-Agent fn 7.7 205 Propositional Logic Sentence type 7.10 209 TT-Entails fn 7 215 Convert to CNF fn 7.12 216 PL-Resolution fn 7.14 219 PL-FC-Entails? fn 7.16 222 DPLL-Satisfiable? fn 7.17 223 WalkSAT fn 7.19 226 PL-Wumpus-Agent fn 9 273 Subst fn 9.1 278 Unify fn 9.3 282 FOL-FC-Ask fn 9.6 288 FOL-BC-Ask fn 9.14 307 Otter fn 11.2 380 Airport-problem data 11.3 381 Spare-Tire-Problem data 11.4 383 Three-Block-Tower data 11 390 Partial-Order-Planner fn 11.11 396 Cake-Problem data 11.13 399 Graphplan fn 11.15 403 SATPlan fn 12.1 418 Job-Shop-Problem data 12.3 421 Job-Shop-Problem-With-Resources data 12.6 424 House-Building-Problem data 12.10 435 And-Or-Graph-Search fn 12.22 449 Continuous-POP-Agent fn 12.23 450 Doubles-tennis data 13.1 466 DT-Agent fn 13 469 Discrete Probability Distribution fn 13.4 477 Enumerate-Joint-Ask fn 14.10 509 Elimination-Ask fn 14.12 512 Prior-Sample fn 14.13 513 Rejection-Sampling fn 14.14 515 Likelihood-Weighting fn 14.15 517 MCMC-Ask fn 15.4 546 Forward-Backward fn 15.6 552 Fixed-Lag-Smoothing fn 15.15 566 Particle-Filtering fn 16.8 603 Information-Gathering-Agent fn 17.4 621 Value-Iteration fn 17.7 624 Policy-Iteration fn 18.5 658 Decision-Tree-Learning fn 18.10 667 AdaBoost fn 18.14 672 Decision-List-Learning fn 19.2 681 Current-Best-Learning fn 19.3 683 Version-Space-Learning fn 19.8 696 Minimal-Consistent-Det fn 19.12 702 FOIL fn 20.21 742 Perceptron-Learning fn 20.25 746 Back-Prop-Learning fn 21.2 768 Passive-ADP-Agent fn 21.4 769 Passive-TD-Agent fn 21.8 776 Q-Learning-Agent fn 22.2 796 Naive-Communicating-Agent fn 22.7 801 Chart-Parse fn 23.1 837 Viterbi-Segmentation fn 24.21 892 Align fn

Index of Programming Exercises in Book Ex. Page Description 2.7 57 Environment simulator for vacuum world (partially implemented) 3.9 90 Missionaries and Cannibals 3.10 91 Successor function for 8-puzzle 3.11 91 Iterative Lengthening Search 3.14 91 Path connecting two web pages 3.15-16 91 Shortest path in plane with polygonal obstacles 3.19 93 Search agents for vacuum world 4.4 134 Suboptimal admissible but inconsistent h(n) 4.8 135 Travelling Salesperson via Minimum Spanning Tree 4.10 135 Improved 8-puzzle heuristics 4.15 136 Local search to solve Travelling Salesperson 4.16 136 Compare hill-climbing, annealing on 8-puzzle, 8-queens 4.17 136 Hill-climbing for robot navigation 4.18 136 Compare A* and RBFS on 8-puzzle 5.7 159 Compare CSP algorithms on map-coloring 5.12 159 Find minimal cycle cutset 6.4 191 Move generator and evaluation functions for board games 6.6 191 Expectiminimax and *-alpha-beta for games with chance nodes 7.15 239 Extend PL-Wumpus-Agent to track all facts within the KB 8.11 269 Tell and Ask facts about family tre ein Fig. 8.5 8.17 270 Define addition for n-bit numbers; verify adder is correct. 9.14 317 Sorting in Prolog 9.15 318 Recursive rewrite rules (demodulators) in Logic programming 12.16 460 Modify And-Or-Graph-search to generate a cyclic plan 14.12 536 Relational Probabilistic Model of Soccer League 16.5 611 Determine utility of money for subjects 16.8 611 Model of airport-siting problem 17.6 647 Policy iteration on 4x3 (and larger) world 17.7 647 Find threshold values for R(s) 18.12 677 Decision-Tree-Learning with missing attribute values 19.5 711 Inverse resolution in Logic Programming 20.15 761 Perceptron and Decision-tree learning on a data set 21.1 788 Compare algorithms for passive learning agent 21.7 789 Exploring RL agent that uses direct utility estimation 21.11 789 Two RL agents learning to play a game 21.12 789 Reinforce and Pegasus algorithms for 4x3 world 22.10 832 Chart-parsing with a packed tree representation 22.11 832 Chart-parsing with a partial packed tree representation 23.1 861 Stylometry: determining authorship 23.2 861 Statistics of n-gram models 23.3 861 Detection of spam email 23.7 862 Regular expression or program to extract company names 25.2 943 Monte Carlo localization 25.4 943 Voronoi diagram

AI: A Modern Approach by Stuart Russell and Peter Norvig

Modified: Jun 19, 2008