Retrieving data from memory through intelligent
Data in memory are stored using different data types.
These data types will come in the form of 5 sense objects, activated element
objects, pattern objects or hidden objects. Since the data in memory are
different, how will the search functions work? The answer is through
intelligent pathways. The robot has to learn how to analyze data in the
real world. Teachers have to teach the robot how to look at a form and
extract information from the form. For example, if the robot was asked a
question such as: “can you read me the name on the form?”,
the robot has to look at the form, locate the name, and read what the name
is. Then, the robot has to face the teacher and tell her the name that was
on the form.
Using this lesson (an intelligent pathway) the robot
can extract a form image from memory and use the lesson to extract the name
on the form image. This form image is data stored in memory, as an
experience by the robot.
In another example, a teacher plays a song for the
robot and after the song is over, the teacher asks the robot to describe
what category the song belongs to. Next, the robot will analyze aspects of
the song, such as rhythm and lyrics; and say to the teacher what the most
likely category of the song is.
Using this lesson (an intelligent pathway) the robot
can extract a song from memory and use the lesson to output the category the
song most likely belongs to. This song is a memory of the song, experienced
by the robot.
The question is: how does the robot know that the
intelligent pathway learned by teachers can be used to analyze and extract
information from data in memory? The answer is through patterns. FIG. 9 is
a diagram depicting how the robot finds the patterns between lessons to
analyze and extract information from data in the real world and analyze and
extract information from data in memory.
In pathway23, the teacher teaches the robot how to
analyze and extract information in a form he is currently looking at. If
the robot doesn’t understand the steps to analyze or to extract information,
then the teacher has to teach the robot these steps.
Intelligent pathway23 is universalized in memory, which
means the robot can answer any question related to searching and extracting
information on a form. This can happen only if the robot was taught many
similar examples. For example, the universal pathway23 can answer similar
questions such as:
1. “can you read me the
phone number on the form”
2. “can you read me the
occupation on the form”
3. “can you read me the
home address on the form”
The universal pathway23 can cater to similar
questions. The instructions to analyze and extract information are very
In pathway24, the teacher gives the robot a command:
“I want you to remember the form and read me the name”. The robot’s brain
will extract the memory of the form and use pathway23 to analyze and extract
the name on the form. Finally, he will tell the teacher what the name is,
which is john doe.
In pathway25, the teacher gives an ambiguous command to
the robot; and based on logic (another intelligent pathway) the robot assume
the teacher wants to know the name that was on the form. Pathway23 has
already been encountered and the robot answered the question in pathway23,
he simply has to repeat that answer.
To sum things up, the robot is taught many various
examples and based on patterns and logic, he will know that pathway23 can be
used not only for analyzing and extracting information on a form the robot
is currently looking at, but also, to use pathway23 to analyze and extract
information from a data in memory.
Intelligent pathways to set a goal,
plan a strategy and achieve the goal
The pathways in memory can create any form of
intelligence. Referring to FIG. 10, an intelligent pathway to set goals,
plan a strategy and achieve goals can be created (called A1). Teachers will
have to teach the robot what are the steps to achieve a
goal. The steps are: to set goals, plan a strategy, trial and error
and successfully achieve goals. During the planning and trial/error steps,
there exists a loop because some strategies might not work and the pathway
has to loop itself again to plan a new strategy and to take a different
FIG. 11 is a diagram depicting an intelligent pathway
called M1 that basically solves a problem. The steps are presented in the
diagram on how the robot will solve a problem. Notice that intelligent
pathway A1 and M1 are very similar. In memory, these two intelligent
pathways will be very close to each other because of their commonalities.
Both A1 and M1 can be universalized so that they can
cater to a wide variety of situations. For example, the problem solving
pathway M1 can be used for all problems. It can be used to solve a business
problem, a math problem, a science problem, a conflict problem, a personal
problem, a videogame problem, or driving problem. On the other hand,
pathway A1 can be used to achieve “any” goal/s. The robot can use A1 to
solve a math problem or to drive a car to a destination or do a math
problem, or do a series of math problems or past a videogame.
Similar types of problems will self-organize. For
example, doing a science problem might be similar to doing a math problem or
driving a car might be similar to driving a motorcycle. Intelligent pathway
A1 and M1 are universal pathways that will organize specific pathways in
their hierarchical trees.
Another note is that pathway A1 can encapsulate pathway
M1 and/or vice versa. Steps in pathway M1 might be to achieve 20 different
separate goals. Or steps in pathway A1 might be to solve 10 interconnected
problems. The brain of the robot will self-organize data in memory so that
similar data will be grouped together. A bootstrapping process occurs,
whereby pathways in memory add, delete and modify data on previously learned
Intelligent pathways used to manage tasks, plan
strategies and follow rules
We learned that intelligent pathways can search and
extract data from memory. These data in memory have different data types;
and the storage of data are not preprogrammed by
computer scientists. The brain learns how to organize data through examples
and lessons from school.
Other remarkable things intelligent pathways can do for
the robot are: to manage tasks, plan strategies, extract relevant data and
follow rules. Referring to FIG. 12, the intelligent pathways the robot’s
brain uses to take action controls the computer program in the conscious.
The optimal pathway controls vital things like what the robot’s goals are,
what tasks to do in the future, what rules should be followed and what
information in memory should be extracted for the purpose of logical
The diagram in FIG. 12 really shows the overall idea
behind the conscious and how the robot actually controls itself in an
intelligent way to take action.
The robot’s conscious applied to a videogame
The task of playing a videogame is a good example of
how human robots act intelligently in a dynamic environment. When playing a
game, the robot has to use logic and common sense knowledge in order to past
the game. Playing a racing game or a RPG game is very difficult and the
player has to understand the rules of the game, the objectives of the game,
how the controls work according to the game, how to solve problems in the
game, how to get the character from one destination to the next, how to come
up with plans to beat the game and so forth.
The legend of Zelda is a very good example because the
robot has to use human-level intelligence in order to past the game. The
Zelda game isn’t like a side-scrolling action game, whereby the player
accomplishes levels in linear order. In Zelda, the player has to talk to
characters in the game and these characters will tell the player what to do
next. If the player doesn’t follow the instructions from characters, he
will get lost and will not past the game. The key to passing the Zelda game
is to use human logic to come up with planned strategies and to take action;
and through countless trial and error.
The intelligence in the robot’s brain comes from a
bootstrapping process, whereby new data is built on top of old data. When
we play a videogame, we are actually using our knowledge of playing a
general game. The lessons of playing sports games in real life, the lessons
of playing chase master, the lessons of playing a board game, the lessons of
driving a car, the lessons of an occupation actually comes from one
universal way of playing a game.
Referring to FIG. 13, pathway B1 is a universal pathway
to play any game. The steps in B1 are very general, in that, all games
played have these linear steps. If you observe a sports game or a board
game, they have these general steps. B2 is a more general pathway to play a
game. In this case, B2 represent playing a videogame. All the intelligent
pathways (B1-B3) are all encapsulated and structured in a hierarchical
manner so that the data goes from general to specific. Intelligent pathway
B3, on the other hand, record detailed steps to play a specific game. If
the game is the legend of Zelda, the steps to playing this game are
different from the steps to playing a racing game.
In intelligent pathway B2, the player has to set goals
in terms of the type of videogame played and based on the player’s goals in
the game. Next, in the videogame are various rules and boundaries the
player has to follow. Then, the player has to also know what buttons
control what actions in the videogame. When playing a videogame, the
controller controls the actions of the character on the monitor the robot is
seeing. On the other hand, if the robot was playing a real sports game, he
has to use his body to take action.
Knowledge of the videogame
When the robot plays a given game, the robot’s
conscious extracts relevant data about the game (activated element
objects). The computer program in the conscious will open a valve and
knowledge about the game will start to pour in. The intelligent pathways
will help a great deal in extracting the relevant knowledge, but the
computer program in the conscious actually controls when certain rules might
be needed or when certain knowledge is needed for logic in a given
For example, when playing a real baseball game,
knowledge about what happens during the timeline of the baseball game will
be mapped out by the conscious. During the estimated timeline of the game,
knowledge will enter the conscious at specific times. In the beginning of
the game, the player (the robot) will know where to go. It will follow the
commands given by the coach. The coach will designate each player their
positions and to set the batting lineup. Next, the coach will tell everyone
what the hidden signals are for this game. The robot’s conscious will tell
the robot where to be and what instructions from the coach he has to
When the game begins, the robot already has tasks it
will do based on the lecture by the coach. Previous knowledge about
baseball will pour in such as where the player has to be and what its roles
are. The rules of the position the robot will play must be known. These
rules sets up the limited actions the robot can take, what rules to follow,
what actions to take at given situations and so forth.
As stated in the last chapter, the conscious primarily
has 4 containers: task container, rules container, planning container, and
identity container. Based on the position the robot is playing, the rules
container is filled with rules (most notably if-then statements). If the
robot is playing center field, some of the rules might be: If the fly ball
is hit hard, back up; if the fly ball is hit lightly move forward; if
catcher throws the ball to second base, move forward. These are the rules
that the computer program in the conscious is trying to maintain in the
As the robot changes positions, the rules in the rules
container will be filled with their respective position rules. The rules of
a pitcher are different from the rules of a left fielder. The computer
program within the conscious will extract and maintain the rules in the
rules container based on the tasks in the task container.
Some games do not need to follow commands from
someone. Playing the legend of Zelda is a game that simply requires
knowledge from memory. The robot’s conscious will extract all the tasks and
rules of a RPG game (short for role playing game) and at the beginning of
the game, the robot knows what type of game it is, what its goals are and
what rules to follow.
This information has been learned through magazines and
strategy guides of previously played RPG games. The knowledge of what to do
and how to play the game has been described in magazines. The robot learned
how to play an RPG game through reading videogame magazines.
For me personally, when I play a new RPG game, my first
goal is to understand the story and what the game is about. Then, I would
follow the guidelines from previous RPG games, which are:
1. talk to all characters
in the game and follow their instructions
2. read the instruction
3. keep a map of the game.
You will find a map either from the game or an instruction manual.
4. past level 1 before going to level 2 and so forth.
5. if you get stuck in the
game, ask yourself why you can’t advance.
These are just some important facts that are needed in
order to play the legend of Zelda or any RPG game. These facts will pour
into the conscious the moment the robot wants to play a RPG game. In fact,
specific tasks/rules will pour into the rules container and task container
at specific situations. During the game, problem solving pathways such as
M1 will be used to solve problems in the game or to come up with imaginative
ideas to go around obstacles. Other intelligent pathways will be needed
during the game such as pathway A1 to achieve goals.
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