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Pseudocode descriptions of the algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

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Pseudocode

Pseudocode descriptions of the algorithms from Russell and Norvig's Artificial Intelligence - A Modern Approach.

The file aima3e-algorithms.pdf contains all the algorithms exactly as they appear in the third edition of the book. The files listed below duplicate that information but with typography that is not as fancy. When there are differences between the third editiona and fourth edition pseudocode, both versions will be shown in the files listed below:

AIMA3e AIMA4e Pseudo-code (in book)
TABLE-DRIVEN-AGENT
REFLEX-VACUUM-AGENT
SIMPLE-REFLEX-AGENT
MODEL-BASED-REFLEX-AGENT



SIMPLE-PROBLEM-SOLVING-AGENT
TREE-SEARCH and GRAPH-SEARCH
BREADTH-FIRST-SEARCH
UNIFORM-COST-SEARCH
DEPTH-LIMITED-SEARCH
ITERATIVE-DEEPENING-SEARCH
RECURSIVE-BEST-FIRST-SEARCH



HILL-CLIMBING
SIMULATED-ANNEALING
GENETIC-ALGORITHM
AND-OR-GRAPH-SEARCH
ONLINE-DFS-AGENT
LRTA*-AGENT



MINIMAX-DECISION
ALPHA-BETA-SEARCH



AC-3
BACKTRACKING-SEARCH
MIN-CONFLICTS
TREE-CSP-SOLVER



KB-AGENT
TT-ENTAILS
PL-RESOLUTION
PL-FC-ENTAILS?
DPLL-SATISFIABLE?
WALKSAT
HYBRID-WUMPUS-AGENT
SATPLAN



UNIFY
FOL-FC-ASK
FOL-BC-ASK
APPEND



GRAPHPLAN



HIERARCHICAL-SEARCH
ANGELIC-SEARCH



DT-AGENT



ENUMERATION-ASK
ELIMINATION-ASK
PRIOR-SAMPLE
REJECTION-SAMPLING
LIKELIHOOD-WEIGHTING
GIBBS-ASK



FORWARD-BACKWARD
FIXED-LAG-SMOOTHING
PARTICLE-FILTERING



INFORMATION-GATHERING-AGENT



VALUE-ITERATION
POLICY-ITERATION
POMDP-VALUE-ITERATION



DECISION-TREE-LEARNING
CROSS-VALIDATION-WRAPPER
DECISION-LIST-LEARNING
BACK-PROP-LEARNING
ADABOOST



CURRENT-BEST-LEARNING
VERSION-SPACE-LEARNING
MINIMAL-CONSISTENT-DET
FOIL



PASSIVE-ADP-AGENT
PASSIVE-TD-AGENT
Q-LEARNING-AGENT



HITS



CYK-PARSE



MONTE-CARLO-LOCALIZATION



POWERS-OF-2

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