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\markright{(BSTU Seminor - November 2013)}
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\begin{document}
\begin{huge}

%% Thank you
%% This is the room where I talked in a conference for the first time in Belarus
%% So I'm glad to give this opportunity to talk, welcoming you from outside Belarus
%% I'm interested in ...
%% My approach is via comuter simulation of human brain
%% topic is ... robot navigation, specifically intelligent rogbot navigation
%% This is a lecture not a talk in a conference, so feel free to inturrupt my talk at any moment to make a question
%% and I want to have an  enough time to discuss with you after talk

\clearpage  % (title) =========================================
\color{blue}
\begin{center}
{\bf Intelligent navigation? \hspace{4in}\\ \hspace*{2in} Then  how intelligent?}
\hspace*{.5in}\\
\color{black}
\bigskip
\hspace*{1in}\\
\begin{Large}
Akira Imada  \\
\bigskip
Brest State Technical University (Belarus)
\end{Large}
\end{center}



\clearpage  % =========================================
\color{blue}
\begin{center}
{\bf Let's name a few}
\end{center}
\color{black}
\begin{large}

\begin{itemize}
\item Navigation for an Intelligent Mobile Robot
   \begin{itemize}
   \item J.~Crowley (1985) IEEE
   \end{itemize}
\item Intelligent Mobile Robot Navigation
   \begin{itemize}
   \item F.~Cuest et al. (2005) Springer
   \end{itemize}
\item Optimal Path Planning For Intelligent Mobile Robot Navigation
   \begin{itemize}
   \item N.~Shiltagh et al. (2013) IJEAT
   \end{itemize}
\end{itemize}

\end{large}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Then how intelligent?\\
or\\
Which agent is more intelligent?}\\
\hspace*{1in}\\
\color{black}
{\Large
How those proposed machine intelligences are actually intelligent?\\
How we define intelligence? \\
How can we measure it?\\ 
How those measurements really represent an intelligence? \\ 
and so on.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Or, not intelligent at all? \\
We'd better ask monkey to throw darts.}\\
\hspace*{1in}\\
\color{black}
{\Large
Burton G.~Malkiel "A Random Walk Down Wall Street"\\
\hspace*{1in}\\
{\it "A monkey throwing darts at the WSJ to select a portfolio\\
might be better than the one carefully selected by experts."}\\
$\Downarrow$\\
Are those machines less intelligent than a monkey?\\
(A key question of this talk.)}
\end{center}




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Are Roomba or Mars Rover intelligent?}\\
\end{center}
\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=5cm,height=4cm]{roomba.jpg}
   \includegraphics[width=5cm,height=4cm]{mars-rover.jpg}
\end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Or more generally}\\
\hspace*{1in}\\
\color{black}
{\Large
\color{red}IBM's Deep Brue \color{black} ... beat Kasparov in 7 matches of chess\\
\color{red}Watson \color{black} ... beat human champions in U.S. TV quize show "Jeopardy"\\
\color{red}Dr. Fill \color{black} ... made a good job in world Crosswords tornament\\
\color{red}Sinii \color{black} ... i-phone
}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf A degree to how a machine is intelligent}\\
\bigskip
\bigskip
\color{black}
{\Large
Gregory Chaitin (1987), Warren Smith (2006)\\ tried to answer it 
using \color{red} complexity \color{black} theory.\\
%% \bigskip
%% Time: "{\it intelligent peoples are more difficult to learn to know.} (2012)\\
$\Downarrow$\\
Now we have a fair amount of such definitions.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Legg \& Hutter's definition}\\
\color{black}
\bigskip
\bigskip
{\Large
From informal\\
``An \color{red}agent\color{black}'s ability to achieve \color{red}goals \color{black} in a wide range of \color{red}environments\color{black}.''\\
\bigskip
$\Downarrow$\\
to formal
\color{red}
\begin{displaymath}
\gamma(\pi) = \sum_{\mu \in E} 2^{-K(\mu)} \cdot V_{\mu}^{\pi}
\end{displaymath}
\color{black}
}\\
\end{center}


%% Let's see this more in detail
%% In their definition
%% in each time slice i
%% we might imagine stack market as environment
%% the function is called pi
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Agent interacts with environment}\\
\color{black}
{\Large
\bigskip
Observation $o_i$ $\Rightarrow$ Action $a_i$ $\Rightarrow$ Reward $r_i$}

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=16cm,height=4cm]{environment-agent.jpg}
\end{center}
\end{figure}

{\Large creating a series \color{red}$o_1a_1r_1o_2a_2r_2o_3a_3r_3 \cdots$}
\end{center}




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf E.g. Agent to Invest in Stock Market}\\
\color{black}
\bigskip
\bigskip
{\Large
(1) observes financial environment of stock market\\
$\Downarrow$\\
(2) makes an action in the environment, i.e., to sell or to buy\\
$\Downarrow$\\
(3) gets rewards, i.e., profit or loss.
}
\end{center}




%% an exploration of the agent in the environment yields a series of sequences of ... called history
%% next action depends on history

%% indeterministic simulation
%% let's call it a pi/mu
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Definition of agent}\\
\bigskip
\color{black}
{\Large Function that takes the current history as input\\ and \\produces an action as output\\ 
\bigskip \color{red}
%% $\pi(a_3 \vert o_1r_1a_1o_2r_2)$\\
$\pi(a_k \vert o_1r_1a_1o_2r_2a_2\cdots o_{k-1}r_{k-1})$\\
\color {black} or probability function for indeterministic.
} 
\end{center}


%% Similarly
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Definition of environment}\\
\bigskip
\color{black}
{\Large
Function which produces output $o_kr_k$ given the current history\\
\bigskip \color{red}
$\mu(o_kr_k\vert o_1r_1a_1o_2r_2a_2\cdots o_{k-1}r_{k-1}a_{k-1})$\\
\color{black} or probability function for indeterministic.
}\end{center}

%% each time agent receives reward
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Expected value of sum of rewards}\\
\color{red}
{\Large
\begin{displaymath}
V_{\mu}^{\pi} = E (\sum_{i=1}^{\infty} r_i)
\end{displaymath}
\color{black}
%% after a run}
%% after each repetion of\\ $o_i \rightarrow a_i \rightarrow r_i$.
}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Definition of intelligence} \\
\color{black}
\bigskip
{\Large
Weighted sum of \\
expected value of sum of rewards over infinitely various environments\\
\color{red}
\begin{displaymath}
\gamma(\pi) = \sum_{\mu \in E} w_{\mu} \cdot V_{\mu}^{\pi}
\end{displaymath}}
\end{center}
%% Summation if for infinite environments
%% infinitely


%% So that we can calculate Kolmogorov ...
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf How will those weights be specified?} \\
\bigskip
\color{black}
{\Large
Translate the environment into a binary string $x$ by Turing Machine $U$\\
%%\bigskip
$\Downarrow$\\
Calculate Kolmogorov complexity $K$ of $x$ \\
(length of the shortest program that computes $x$)\\ 
%% \bigskip
%% $\Downarrow$\\
$K(x)=\min_p\{l(p) \vert U(p)=x\}$\\
\bigskip
$\Downarrow$\\
\color{red}$w_{\mu}=2^{-K(\mu)}$ \color{black}\\
The smaller the complexity the larger the weight.}
%%\Rightarrow$ Occam's razor}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Universal Machine Intelligence} \\
\color{black}
{\Large
by Legg and Hutter \\
\begin{displaymath}
\gamma(\pi) = \sum_{\mu \in E} 2^{-K(\mu)} \cdot V_{\mu}^{\pi}
\end{displaymath}
``An \color{red}agent\color{black}'s ability to achieve \color{red}goals \color{black} in a wide range of \color{red}environments\color{black}.''}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Too conceptual or too theoretical} \\
\bigskip
\color{black}
{\Large
Goertzel:\\
"Universal but not practical."}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Goertzel} \\
\color{black}
{\Large 
pragmatic intelligence\\
\color{red}
\begin{displaymath}
\Pi(\pi) = \sum_{\mu \in E, g \in G, T} \nu(\mu)\gamma(g,\mu)\tau_{g,\mu}(T) V^{\pi}_{\mu,g,T}
\end{displaymath}
\color{black}
\bigskip
$\Downarrow$\\
It's not very practical yet, isn't it?}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Problem is, translation of environment \\by Turing machine} \\

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=4cm]{turing-machine.jpg}
\end{center}
\end{figure}

{\Large 
\color{black}
$\Downarrow$\\
Are there easier alternatives to the Turing machine?}
\end{center}


%% purpose is to generate ...
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Hern\'andes-Orallo} \\ 
{\Large
\color{red}
\bigskip
The other representations of environment \\
\color{black}
%% Apart from Turing machines many other Turing complete models of computation, such as: \\
$\Downarrow$ \\
%%\bigskip
(i) $\lambda$-calculus, (ii) combinatory logic, (iii) abstract state machines, \\
(iv) register machines, (v) Markov algorithms,\\ (vi) term-rewriting systems, ... \\
\bigskip
%% also Finite state machine (not Turing complete though) 
\color{red}
to generate environments and calculate complexity \color{red}automatically\color{black}.\\
\bigskip
{\it "still Turing-complete, but\\ \color{red}more natural and easy 
\color{black} to work with than the Turing machine."}\\
}
\end{center}


%% so far so good, however,
%% his target was strings like this
%% for some strings of different difficulty such as these strings
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Hernandes's target was strings}\\
\bigskip
\color{black}
{\Large
He measured the ability of finding the shortest explanation\\ 
for some strings of different difficulty in a fixed time”\\
e.g.\\
{\it 
x = aaaaaaabaaaaaaabaaaaaaabaaaaaaa\\
x = aabbbccdddeefffgghhhiijjj\\
x = 1,2,3,5,7,11,13,17,19,23}\\

$\Downarrow$\\
Still not so practical for our purpose.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Hibbird used finite state machine (FSM)\\ to be a little more realistic}\\
\bigskip
\color{black}
{\Large 
Environment and agent are both modeled as FSMs.\\
$\Downarrow$\\
Create a hierarchy of increasingly difficult environments.\\ 
$\Downarrow$\\
Agent intelligence is measured as
the highest level of environment\\ against which it can win the game.}
\end{center}
%% I like this approach


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Hibbird's game}\\
\bigskip
\color{black}
{\Large
\color{red}Evader \color{black} $e$ produces the sequence by
\begin{displaymath}
x_{n+1} = e(y_1 y_2 y_3 \cdots y_n),
\end{displaymath}
and \color{red}predictor \color{black} $p$ produces the sequence by
\begin{displaymath}
y_{n+1} = p(x_1 x_2 x_3 \cdots x_n).
\end{displaymath}
Then $p$ wins round $n+1$ if $y_{n+1}=x_{n+1}$\\
and $e$ wins if $y_{n+1} \ne x_{n+1}$.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Our purpose, however, is}\\
\bigskip
\color{black}
{\Large
to measure inteligence of proposed machine in proposed environment!\\
$\Downarrow$\\
So far not very good!
}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Let's try our definition!}\\
\bigskip
\color{black}
{\Large To fulfill our purpose}
\end{center}

{\Large
\begin{itemize}
\item Let's be specific not universal!
\item Let's be more unpredictable!
\item Let's be simple! 
\item Let's be sophisticated more or less!
\item Let's learn from previous experience!
\end{itemize}}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be specific not universal!}}\\
\color{black}
\bigskip
{\LARGE Our goal is to measure intelligence of a proposed machine\\
not a universal almighty robot!}\\
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be specific not universal!}}\\
\color{black}
\bigskip
{\Large
$\star$ "She is an \color{red} intelligent \color{black} dancer," \\ while we know she is not good at Mathematics.}\\
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be specific not universal!}}\\
\color{black}
\bigskip
{\Large
$\star$ "He is an \color{red} intelligent \color{black} football player," \\ while we know he is not good at Mathematics,\\
which we don't care.}
\end{center}

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=7cm]{soccer-bot.jpg}
\end{center}
\end{figure}





\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be specific not universal!}}\\
\color{black}
\bigskip
{\Large
$\star$ This conductor always makes an \color{red}intelligent \color{black} interpretation of symphony, but very bad at football.\\
\bigskip
$\star$ Did Einstein play football \color{red} intelligently?\\
\color{black} $\Downarrow$\\
\color{red}
Intelligence doesn't need to be universal!}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be specific not universal!}}\\
\bigskip
\color{black}
{\Large
\begin{displaymath}
\gamma(\pi) = \sum_{\mu \in E} 2^{-K(\mu)} \cdot V_{\mu}^{\pi} \hspace{.4in} \Rightarrow \hspace{.4in} \gamma(\pi) = 2^{-K(\mu)} \cdot V_{\mu}^{\pi} 
\end{displaymath}
}
\end{center}




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\color{black}
{\Large 
\bigskip
Human-intelligence is
\color{red}
spontaneous\color{black}, \color{red}flexible\color{black}, 
and/or \color{red} unpredictable\color{black},\\ 
\color{black}more or less.\\
Or even \color{red} erroneous \color{black} sometimes.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\color{black}
{\Large {\bf Which route, do you think, is more intelligent?}}\\

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=5cm]{trace-96-end-new-val-1.jpg}
   \includegraphics[width=5.2cm]{trace-96-start-val-1.jpg}\\
(A business person \& a philosopher going for a walk in Manhattan)\\
\end{center}
\end{figure}

\end{center}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\bigskip
\color{black}
{\Large {\bf Intelligence avoids a similar behavior}\\
{\bf E.g. ``I beg your pardon?''}}\\
\hspace*{1in}\\
\color{black}
{\Large 
Intelligent people try a \color{red}different 
\color{black}explanation for an easier understanding\\
\bigskip
while \\
others just repeat the \color{red}same \color{black}expression, maybe louder.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\color{black}
{\Large Excelent but always slightly diffiernt\\
(One of the reason I like this.)}
\end{center}

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=5cm]{gyoza-0.jpg}
   \includegraphics[width=5cm]{gyoza-1.jpg}
   \includegraphics[width=5cm]{gyoza-2.jpg}
{\large (Professional should make always same performance?)}
\end{center}
\end{figure}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\color{black}
{\Large \bf Why spontaneous?}\\
\begin{figure}[h]
\begin{center}
%% \rotatebox[origin=c]{270}{
\includegraphics[width=3cm,height=2.25cm]{aibo.jpg}

\end{center}
\end{figure}
\color{black}
{\Large We sometimes need \color{red} spontaneous \color{black} and \color{red} unpredictable \color{black} 
intelligence rather than \color{red} efficiency \color{black} or \color{red}effectiveness \color{black} like in case of SONY's AIBO.\\
\bigskip
It learns excellently and acts \color{red} differently in different situation \color{black}
but repeats \color{red} the same action in a same situation\color{black}.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE{\bf Let's be more unpredictable!}}\\
\bigskip
\color{black}
{\Large What we expect for the robot to be intelligent is,\\
\color{red}  "A different action even in a similar situation!"}\\ 
\end{center}





\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE {\bf Let's be simple!}}\\
\color{black}
{\Large{\bf ``Occam's Razor Principle''}}\\
\bigskip
{\Large
The simpler the expression, the more beautiul it is!\\
$\Downarrow$\\ 
The simpler the actions, the higher the intelligence.
}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE {\bf Let's be sophisticated more ore less!}}\\
\bigskip
\color{black}
{\Large
Once Kluger wrote in the TIME Magazine\\
{\it "intelligent individuals are more difficult to learn to know."}\\
$\Downarrow$\\
So does a Machine Intelligence?}
\end{center}

%% entropy

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE {\bf Let's be sophisticated more ore less!}}\\
\bigskip
\color{black}
{\Large "The rest of the paper is organized as follows. \\
Section 2 reviews ---. 
--- are presented in Section 3. Then, --- is given in Sections 4. 
Section 5 presents ---.  Finally, Section 7 concludes the paper."\\
vs\\
"I describe --- in Section 2, --- in Section 3, --- in Section 4, ... \\ and conclusion in Section 7."}\\
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE {\bf Let's be sophisticated more ore less!}}\\
\bigskip
\color{black}
{\Large Which looks more sophisticated?}
\end{center}

\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=5cm]{simple.jpeg}
\end{center}
\end{figure}






\clearpage  % =========================================
\begin{center} 
\color{blue}
{\LARGE {\bf Let's learn from previous experiences!}} 
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf What we include in our definition are:}\\
\end{center}
\color{black}
{\Large
\begin{itemize}
\item Let's be specific not universal!
\item Let's be more unpredictable!
\item Let's be simple! 
\item Let's be sophisticated more ore less!
\item Let's learn from previous experience!
\end{itemize}}



\clearpage  % ========================================= negative
\begin{center} 
\color{blue}
{\bf Our proposal}\\
\bigskip
\color{black}
{\Large
Repeat a run in a same condition to see similarity, simplicity, learning
\hspace*{1in}\\
\color{red}

\begin{displaymath}
\gamma(\pi) = 
\sum_i \frac{\{\mbox{\small REWARDS}\}_{i} \cdot \{\mbox{\small IMPROVEMENT}\}_{i} \cdot 
             \{\mbox{\small EXPECTED REWARD}\}_{i}}
{\{\mbox{\small COMPLEXITY}\}_{i} \cdot \{\mbox{\small SIMILARITY}\}_{i}}
\end{displaymath}

 }
 \end{center}



\clearpage  % ========================================= negative
\begin{center} 
\color{blue}
{\bf Not a Conclusion but ... }\\
\bigskip
\color{black}
{\Large
It's still too early to claim "Our machine is intelligent (as human)!"\\
Lots of things awaits for our further research.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Thank you for your interest! }
\end{center}





%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\end{huge}
\end{document}




