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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 from the third edition of the book. As we write the fourth edition, we will put the updated and new algorithms here:

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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