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  /Title (aritificial-intelligence-cheat-sheet.pdf)
  /Creator (Cheatography)
  /Author ({[}deleted{]})
  /Subject (Aritificial Intelligence Cheat Sheet)
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\noindent
\begin{multicols}{3}
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    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Aritificial Intelligence Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{{[}deleted{]}} via \textcolor{DarkBackground}{\uline{cheatography.com/31421/cs/9528/}}}
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\noindent
\begin{multicols}{3}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}{[}deleted{]} \\
  \uline{cheatography.com/deleted-31421} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 19th October, 2016.\\
   Updated 19th October, 2016.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
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  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
\raggedright
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% www.emerson.emory.edu/services/latex/latex_169.html
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\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{x{1.84 cm} x{6.16 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Search Methods}}  \tn
% Row 0
\SetRowColor{LightBackground}
Tree Search & Expand nodes using gradients \{\{nl\}\} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
Graph Search & Avoids revisiting nodes and ensure efficiency \tn 
% Row Count 4 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{2.56 cm} x{5.44 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Uninformed search}}  \tn
% Row 0
\SetRowColor{LightBackground}
Uniform cost search & aka Cheapest-first \{\{nl\}\} Add visited node to Explored and add its neighbors to the frontier \{\{nl\}\} Visit cheapest node in the frontier \{\{nl\}\}  Move to next cheapest if all neighbors are explored \tn 
% Row Count 8 (+ 8)
% Row 1
\SetRowColor{white}
Iterative deepening & Iteratively calls depth-limited search \{\{nl\}\} Initialize frontier with root \{\{nl\}\} If not goal, remove from frontier and expand \{\{nl\}\} If at the end of depth, terminate and start over \{\{nl\}\} Repeat until goal is found \{\{nl\}\} Guaranteed to find optimal path \{\{nl\}\} More efficient than DFS \{\{nl\}\} Time complexity: O(b\textasciicircum{}d)   Space complexity: O(d) \tn 
% Row Count 21 (+ 13)
% Row 2
\SetRowColor{LightBackground}
\seqsplit{Bidirectional} & Finds shortest path of a grpah \tn 
% Row Count 23 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{3.04 cm} x{4.96 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Informed Search}}  \tn
% Row 0
\SetRowColor{LightBackground}
Best-first search & Choose unvisited node with the best heuristic value to visit next \{\{nl\}\} Same as lowest cost BFS \{\{nl\}\} \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
Greedy best-first search & Uses heuristic to get the node closest to the goal \{\{nl\}\} Bad performance if heuristic is bad \{\{nl\}\} Does NOT consider cost of getting to the node \tn 
% Row Count 12 (+ 7)
% Row 2
\SetRowColor{LightBackground}
A* search & Always expand to node with minimum f \{\{nl\}\}Evaluate cost of getting to goal using heuristics \{\{nl\}\} f(n) = g(n)+h(n) where g is cost to get to n \{\{nl\}\} Uses priority queue \tn 
% Row Count 20 (+ 8)
% Row 3
\SetRowColor{white}
Heuristics & Cost to get to the goal \tn 
% Row Count 21 (+ 1)
% Row 4
\SetRowColor{LightBackground}
Admissible herustic & Optimistic model for estimating cost to reach the goal \{\{nl\}\} Never overestimates \{\{nl\}\} h(n) \textless{}= c(n) where c is actual cost \tn 
% Row Count 27 (+ 6)
% Row 5
\SetRowColor{white}
Consistent heuristic & h(n) \textless{}= c(n, a, n') + h(n') \{\{nl\}\} Immediate path costs less than longer path \{\{nl\}\} Consistent ⟹ Admissible \tn 
% Row Count 32 (+ 5)
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Consistent heuristic}}  \tn
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\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{2.56 cm} x{5.44 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Adversarial Search}}  \tn
% Row 0
\SetRowColor{LightBackground}
Hill climbing & Method of local search \{\{nl\}\} Only move to neighbors to find the max \{\{nl\}\} Does NOT guarantee to find optimal \tn 
% Row Count 5 (+ 5)
% Row 1
\SetRowColor{white}
Simulated annealing & Method of local search \{\{nl\}\} Combine hill climbing and random walk \{\{nl\}\} Always find global max \tn 
% Row Count 9 (+ 4)
% Row 2
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Local beam & Generate k random states \{\{nl\}\} Generate successors of all k states \{\{nl\}\} If goal stop; else, pick k best successors and repeat \{\{nl\}\} Different from hill-climbing since information is shared between k points \tn 
% Row Count 17 (+ 8)
% Row 3
\SetRowColor{white}
Genetic algorithm & Cross-Over and mutation \{\{nl\}\} Decomposes strands of DNA and permute \{\{nl\}\} Produces children by: Selection, Cross-over, Mutation \tn 
% Row Count 22 (+ 5)
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Minimax Tree}}  \tn
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\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{\{\{ar\}\}◎ Max node \newline $\square{}$ Min node}  \tn 
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\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{α-β Pruning}}  \tn
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\mymulticolumn{1}{x{8.4cm}}{function alphabeta(node, depth, α, β, maximizingPlayer) \newline   if depth =  or node is a terminal node \newline       return the heuristic value of node \newline   if maximizingPlayer \newline       v := -∞ \newline       for each child of node \newline           v := max(v, alphabeta(child, depth - 1, α, β, FALSE)) \newline           α := max(α, v) \newline           if β ≤ α \newline               break ({\emph{ β cut-off }}) \newline       return v \newline   else \newline       v := ∞ \newline       for each child of node \newline           v := min(v, alphabeta(child, depth - 1, α, β, TRUE)) \newline           β := min(β, v) \newline           if β ≤ α \newline               break ({\emph{ α cut-off }}) \newline       return v} \tn 
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{SAT}}  \tn
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\mymulticolumn{1}{p{8.4cm}}{\vspace{1px}\centerline{\includegraphics[width=5.1cm]{/web/www.cheatography.com/public/uploads/sonyapur_1476910933_Screen Shot 2016-10-19 at 5.01.20 PM.png}}} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
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\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4 cm} x{4 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{Propositional SAT: Graph coloring}}  \tn
% Row 0
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At lest 1 of K per i & (Ci,1 ∨ Ci,2 ∨ ... ∨ Ci,k) \{\{nl\}\} O(n) clauses \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
1 ≥ color per i & ∀ k≠k' (¬Ci,k ∨ ¬Ci,k') \{\{nl\}\} O(n\textasciicircum{}2) \tn 
% Row Count 6 (+ 3)
% Row 2
\SetRowColor{LightBackground}
If node i and j share an edge \{\{nl\}\} assign different colors & ∀ x∈k, (¬Ci,x ∨ ¬Cj, x) \tn 
% Row Count 9 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}