How does the AI understand what’s going on
26 September 2017
Most researchers regard AI as a static function without memory. This is one
of the few articles where AI is seen as a device with memory. When we have memory, we can ask ourselves: “Where am I?”, and
“What is going on?” When we have no memory, we have to assume that we are
always in the same place and that the world is always in the same state.
Keywords: Artificial
Intelligence, Machine Learning, Reinforcement Learning, Partial
Observability.
Introduction
The standard
approach in AI is to take a set of positive examples and a set of negative
examples. We seek for a function that says “YES” for the positive
examples given, and “NO” for the negative examples given. Using the function found, we begin to predict the right answer
for examples which we do not know whether are positive or negative.
In essence,
the standard approach in AI represents an approximation. What is
sought for is an approximation function. It is
usually sought for in a given set of functions. For
example, in neural networks, the function is sought for in the set of neural
networks.
As a typical
example of the standard approach to AI we can indicate [1] where a static
function covering certain positive and negative examples is sought. In articles
like [2], things are a bit different, because there are new examples at every
step and at each step the approximation function changes. However, in [2] we are again seeking for a static function
without memory, although at each step this function is different. In [2] the
idea is that we constantly improve the approximation function, but at each
specific moment we have one specific static function.
The only
research in which AI is seen as a memory device is the research related to
Reinforcement Learning. Even in this area there are two main branches
called “Partial Observability” and “Full Observability”. Do we need memory when we see everything? Of course not! If we see the
current state of the world, we do not need to remember anything from the past
because everything we need to know is encoded in what we see. That is, the only branch of AI where AI is considered as a
memory device is the Reinforcement Learning with Partial Observability. This article refers exactly to this branch of Computer
Science.
Why are we
saying that such articles are few? There are many articles
in the field of Reinforcement Learning, but they are largely devoted to the
Full Observability case. For example [3, 4].
When referring to Partial Observability, it is usually
said that there is such an option, but it is not said what we are doing in this
case.
In this article we ask the question “What
is going on?” If you translate this question in terms of Reinforcement
Learning, it will sound like this: “What is the state of the world, we are in at
the moment?” If you look at the states of the world as a graph, the question “What
is going on?” can be translated as “Where are we? In which vertex
of the graph are we in?” If we think of the real world, “What is going on?”
means “What is our physical location at the moment and what is the state of the
world at this moment?”
To say in what state of the world we
are, we must first describe the states of the world. It will never be easy, because we do not see these states
completely, but only partially (Partial Observability). For this reason, the description of the states of the world
is related to imagining something invisible.
For this purpose we will introduce
the concept of “property of the world”, representing a set of states of the world. With this
concept we will describe the current state of the world. This will happen by the properties of the world that we know
are valid or not. In this way, we will determine
that the current state is an element of the intersection of a group of sets,
i.e. an intersection between
properties that are valid at this moment and the
complements of the properties that are not valid. If these properties are enough, then the intersection of all
these sets will be small enough and the description of the current state of the
world will be precise enough. (That is, we do
not define the current states with precision to one single state but with
precision to a set of states.)
To describe the properties of the
world, we will first see what properties we have. All we have
are the experimental properties. We will want to
describe particular property through what we have. This will happen by stating that from a given experimental property
it follows something for the property we are describing. For example, that it follows that the property is true with
some probability. The good cases are when it
follows that it is true with a 100% probability, or vice versa – it is false to
a 100%.
How will we determine the
properties? To form a drop of rain we need a speck of dust that serve as
a condensation nucleus. Similarly, in order to
create a property that is defined for all the states of the world, we need to
have the property defined for a relatively small subset. Based on this subset we will make an extrapolation and assume
that the probability for the whole set is the same as for this subset.
Who are these condensation nuclei by
which we will obtain the required properties of the world? These will
be the tests defined in [8] and the corresponding
function of the test.
This article is, in its essence, a
continuation of [8], but readers shouldn’t have necessarily read [8] to grasp
the idea of the present article. (Short versions of [8]
have been published in [9] and [10]). In [8] the
term “Testable state” describes five different things: tests, function of the
test, prediction of the function, test property, and test state. Those
five different things denoted by a single term can be considered a mistake,
though between the first and second there is a bijection. The prediction is also determined completely provided that we’ve
specified the moment it is for (i.e. after how many steps). As to the test property, it is a continuation of the function
of the test. This function can be continued in
many ways, but we are looking for a continuation that is as natural as
possible. The test state depends on the splitting
into groups of relative stability. This splitting
can be done in many ways, but the meaningful splittings are few.
This article begins with the
introduction of three new definitions of Reinforcement Learning and proof of the
equivalence of these new definitions with the standard definition. The aim of the new definitions is to help us introduce the
concepts of chance, test state and noise. We
will give a specific example that justifies the introduction of the new
definitions. We will introduce the concepts of
event (experiment), we will say what a property of the world is and we will consider
the properties defined by experiments. We will
introduce the term of test. From it we get the test
property. When we have not one, but many groups
of relative stability, then instead of a test property we will talk about test state.
That means that we will talk about a test that defines
not one, but several properties of the world. Finally,
we say how to define theories that describe the properties of the world.
Thus we will answer the question “What
is going on at the moment?” The answer is a set of properties so that we know
whether they are valid at the moment. The
next action of our device will not depend solely on what it sees at the moment.
It will also depend on what properties of the world are
currently valid (that is, on what is going on at the moment). This means that our device
will be a device with memory.
Formulation of the problem
We have a sequence: action, observation, action, observation ..., and we want to understand this
sequence. Our goal is to predict how this sequence will continue and to
choose such actions to achieve the best possible outcome. We will assume that this sequence is not accidental, but that
it is determined by the rules of a world in which we are.
The representation of the world is essential because we will try to
understand the world, that is, we will try to build a model, and we will seek
for this model in a form close to the form we’ve chosen.
We will look at four possible definitions of the type of world. We will
prove that these four definitions are equivalent. The benefit of the three new definitions we will offer is that
they will help us in building a model of the world. The second definition will help us add the notion of chance to
our model, the third definition will naturally lead us to the notion of Testable
Property, and the fourth definition will give us the
possibility to add the notion of noise.
Definition 1. This is the
usual definition of the world in Reinforcement Learning. It is the
following: We have a set S of the
internal states of the world and one of them, s_{0}, is the initial one. How the internal
state of the world changes is determined by the function World, and what we see at each step is determined by the function View. The
following applies:
Here, actions and observations (a_{i} and v_{i}) are vectors
of scalars with dimensions n and m, respectively. Each of these scalars will be a finite
function with k possible values,
where k is different for the
different coordinates of a and v.
Let’s ask whether the function World
is singlevalued or multivalued. By this definition this
function is singlevalued, that is, the world is determined. With the new definitions this will change.
The next question is whether the function World is total or partial. In [8] we argued that
we should assume that World is a
partial function and that cases in which it is not defined will be assumed to
be incorrect moves. Again in [8] we have accepted
that at each step we will be able to check for each move whether it is correct
or incorrect. That is, at each step we will see
two things. First, we’ll see what the function View returns, and second, we’ll see which
actions are correct and which are incorrect at this moment.
As we have said, this is the usual definition used by most authors (for
example [3, 4]), except that other authors usually assume that the function World is total and that all moves are
correct. We will not prove that the definitions in the case of total
and partial function World are
equivalent because they are not. In the case in
which we allow incorrect moves the device gets more information. This can be partially emulated in the case in which there are
no incorrect moves, but this emulation will be only partial.
Definition 2. With the singlevalued function World we lack the concept of chance. Let’s see
how we can add it. If we allow World to be multivalued, then the next
state of the world will not be definite but will be one of several possible.
Okay, but we’d like to say something about the
probabilities of these different options. Let’s
have k different options. Let each of them
happen with some probability p_{i}. (Now things
look like the Markov Decision Process or, in particular, the Partially Observable
Markov Decision Process, because here we are dealing with the Partial Observability
case.)
So, we can define chance in two possible ways. With the
first one the probability is not determined at all, and with the second one it is
determined too precisely. Both options are not
what we want, so we will choose something in the middle. In the first possible way, the probability is somewhere in the
interval [0, 1]. In the second way, the
probability has a fixed value p (i.e. it is in the interval [p, p]).
In the variant which we will choose the probability will
be in an interval [a, b]. That is, we will
not know the exact probability, but we will know that the probability is at
least a and less than b.
There are two kinds of chance – predictable chance and
unpredictable chance. If you roll a dice, the
probability of rolling a six is 1/6. This is a
predictable chance. When we ask our boss for a
salary increase, the answer will be ‘YES’ or ‘NO’, but we can not say how
likely it will be ‘YES’. This is an unpredictable
chance. The predictable chance is quite
determined because, thanks to the Law of Large Numbers, we know quite well how many
the successful attempts will be. The option we
chose is a combination of predictable and unpredictable chance. Of course, in order for the definition to be correct, we must
ensure that some inequalities are fulfilled. If World
(s,a) has k possible values of probabilities in the intervals [a_{i,},b_{i}], the following inequalities must be
fulfilled:






Then the following inequalities must also be fulfilled:

(1)





We will assume that inequality (1) is equality
for at least one i. We can assume even that it is equality for at
least two i.
By this, the second definition of the world is complete. We only have
to prove that it is equivalent to the first one.
Note: We will assume that the function World will not be too indeterminate
because it would make the world too incomprehensible. For example,
supposing that from any state and upon every action, the function World with the same probability can move
to any other state, this will make the world completely incomprehensible.
It would be much more understandable if, in most cases, the
function World returns a single possibility,
and when the possibility is not only one, the possibilities are few and one is
much more likely than the others.
Theorem 1. The second
definition is equivalent to the first.
Note: The next proof is technical, so we
advise the reader to skip it and take it on trust.
Proof: One of the directions is easy,
because it is obvious that the first definition is a special case of the
second. For the opposite direction, it is necessary for an arbitrary
world described in the second definition to build a world equivalent to that
described in the first definition.
The idea of the proof is to hide the probability in a natural number. We will have
a function F, which will calculate the next number from the current
random number. Similarly, the pseudorandom
numbers in computers are calculated. There,
starting from one number (we will start from zero), each subsequent
pseudorandom number is obtained by the function F from the previous one
(i.e. F (x)). Function F must be
sufficiently complex so that we could not guess the next number. Furthermore, F must be good, which means that for one
particular Q all remainders of modulus
Q are equally probable.
In our case we have two chances and that is why we will add two natural
numbers and two functions (F_good and F_bad). We will
like these two functions to be complex enough (noncomputable
and not allowing any approximation with a computable function). We will also want the sequence F ^{i}(0) to be without repetition
for both functions. Only function F_good is
to be good for a particular Q, but for
F_bad we will not want anything more. We will use F_good to calculate the predictable chance, and F_bad
to calculate the unpredictable one.
We will define the function F_good as follows:
F_good(0)=0
F_good ^{i+1}(0)= the first unused
number of those which give a remainder k on the division by Q, where kÎ[0, Q1] is
selected randomly.
Note: We’ve defined the function F_good
through an endless process in which at each step we are making a random choice (for
example, pulling balls out of a hat in which there are Q balls). In this way
we get an noncomputable function. In this article
we try to describe a specific algorithm and everything that is part of this
algorithm must be computable. The functions F_good
F_bad are not part of this algorithm. Their
task is only to show the equivalence of two definitions. We only need to show that these two functions exist. We will never build these functions in practice nor calculate
them.
Note: If for some reason we want to
realize the functions F_good and F_bad, (for example, if we want
to build an artificial world in which to test AI), we would use the standard function
Random, which is embedded in most programming languages. Unlike F_good,
Random is not perfect, but it’s good enough for practical purposes.
Random does not work with natural numbers but
with a 32bit integer. It is not perfect in the
sense that it is complex, but it is not infinitely complex and the next random
number can theoretically be guessed (but in practice it cannot). Moreover, the sequence of random numbers is not infinite, but
repeats (but after a rather long period). That
is, for practical purposes F_good can be replaced with Random.
F_bad can also be built using Random.
We only have to take care of what the function returns.
All classes by module Q not to be equally probable
but to have another probability distribution. From time to time, this
probability distribution to change randomly.
After this preparation we are ready to prove the equivalence of the two
definitions. Let us have a world according to the second definition,
respectively, S, s_{0}, and World.
The new world, which we will build, will have a set of the
states S´ℕ´ℕ, initial state (s_{0}, 0, 0), and function Big_World, which is defined as follows:
Big_World((s, x, y), a)= (s′, F_good ^{2}(x),
F_bad(y) )
Here F_good is square because this function is used twice in the
calculation of s'.
s′= World(s, a), when World
(s, a) has one possible value
When World(s, a) has k
possible values, we select one of them as follows:
We divide the interval [0, 1] to k+1 subintervals with
lengths from a_{1} to a_{k,} and the last one with a length of the remainder. We choose a
random point in the interval [0, 1]. If
the point has fallen in some of the first k subintervals, s' will
be equal to the i^{th} of the possible values of World (s,
a). Here i is the number of the interval
in which we have fallen in.
If the point has fallen in the last subinterval, we calculate the
coefficients:
We once again take the interval [0, 1] and mark the points c_{i }in it. Thus we divide the interval [0, 1]
into k+1 subintervals (some with zero length). We once
again select a random point in the interval [0, 1] and see in what
subinterval it has fallen in. The first
subinterval corresponds to k possible values of the World (s, a),
the second corresponds to k1 ones,
and the last corresponds to 0 possible values. We
cannot fall in the last interval because it is with length zero (because we
assumed that at least one of the inequalities (1) is equality). We can even assume that the last two intervals are zero.
Once we understand how many and what are the possible
values of World (s, a), we choose one of them with unpredictable chance.
How does this work? If the
possible values are R, we take the number (y mod R)+1. If this number is 1, we take the first of the options, if it
is 2, we take the second, and so on.
We didn’t say how we choose a random point in the interval [0, 1]. For this
purpose, we divide the interval Q into equal parts. We take the number (x mod Q)+1 and this will be the
number of the interval which we will choose. It
does not matter which of the points of this interval we will take because these
intervals will be entirely contained in the intervals we look at. For the latter to be true, we will assume the following:
We assume that the numbers a and b are rational (if
irrational, we can make them rational with a small rounding). We will even
assume that these numbers are of the type x/100 (i.e., that they are
hundredths). If they are not, we can again make
them of the type with a small rounding. (In this
case the rounding is acceptable because we are talking about an interval of probability
and if the difference is small, it will be felt only after a very large number
of steps.) We will further assume that the number Q, which we used in
the construction of F_good, is equal to the least common multiple of the
numbers from 1 to 100.
Note: At the first selection of a random
point we take the number (x mod Q)+1, and for the second selection
instead of x we take F_good (x), i.e. we take the
next random number.
Thus the equivalence of the first and second definitions is proven.
∎
Definition 3. The following idea stands behind this
definition: What we see can be changed without changing the place where we are. For example,
you are in the kitchen and you see that it is painted yellow. The next day you’re back in the kitchen, but this time you see
it is painted blue.
We will change the function View
and the states of the world. If the function View returns a vector of scalars of dimension m, then we
will add m visible variables to each state. Now the finction View
will return the values of these m variables. Apart from the visible variables, we will also add a u
number of invisible variables. The idea of
invisible variables is that not everything can be seen. For example, if someone is angry with you, you do not see it,
but it does matter because it will later affect his actions.
We will introduce the concept of cumulative state and it will consist of
the state of the world and the value of all the S.(m+u) variables. When we want to emphasize that this
is not a cumulative state, we will say a standard state.
The function World will be
defined for the tuple <cumulative state, action> and will return a cumulative
state. Again, the function World
will be multivalued and will not be total. That
is, again, we will allow chance and incorrect moves.
Note: A variable can not be bonded with
the state to which it is attached. This is especially true
for invisible variables. We are asking ourselves
whether we should introduce global variables. The
answer is that global variables are not needed because every local variable can
be considered to be the same for a whole set of states and even for all states.
Note: We will assume that the values of
the variables will not change too violently because that would make the world
too incomprehensible. For example, supposing that the function World of each step changes the value of
all variables in an absolutely arbitrary way, it would produce a completely
incomprehensible world. It would be more comprehensible
if most of the variables are constants and do not change, and when they are no
constants – to change relatively rarely and according to clear and simple
rules.
Theorem 2. The third
definition is equivalent to the second one.
Proof: To prove the equivalence of the
second and third definition is easy. If we assume that all variables
are constants (i.e., the function World
never changes them), we will see that Definition 2 is a partial case of Definition
3, and vice versa, if we look at the cumulative states as standard states, then
from the third definition we get the second. The
only remark is that variables can be infinitely many (if the states
are infinitely many). From there, we get that
cumulative states may be too many (continuum), but if we limit ourselves to the
achievable states, they will still be finitely or countably many.
∎
Definition 4. The next
thing we will do is introduce the notion of noise. We will change
Definition 3 so that the new function View will no longer return the pure
value of the visible variables but the value distorted with some noise. To describe the noise, we will need two things – volume and
spectrum of the noise. The volume will be a
number Volume in the range [0,
1]. The spectrum of the noise will be a
ktuple <p_{1,} ..., p_{k>.} For each
visible variable we will add more k+1
invisible variables that will contain the volume and spectrum of the noise of
this variable (i.e., we will add more S.m.(k+1) invisible variables,
if k is the same for all visible
variables). The function View will return the value of the visible variable with probability
1
Volume. With probability Volume
it will return noise, which will be one of the possible k values, each with a probability of the corresponding p_{i.}
Let the cumulative states in
Definition 4 be the same as in Definition 3. That is, they will depend on all
visible and invisible variables, but will not depend on what the function View returns. In other
words, here function View is not
completely determined by the values of the visible variables of the respective
state, but also by the values of invisible variables that describe the noise.
Also, some predictable chance takes part in the
definition of the function.
Note: This is how we
describe the situation in which at its input the device receives information
distorted by some noise. What do we do when the
output information is also distorted by noise? We’ve
dealt with the second option in Definition 2 because there we’ve already
introduced the possibility for the same action to have different possible
consequences for the world, each one with a different probability.
Note: Again we are dealing
with a type of Partially observable Markov decision process (POMDP) with the
difference that in our case variables have their value, although it is
distorted by noise, while with POMDP there is only noise (i.e., for all visible
variables the noise is 100% which means Volume = 1). The only thing that distinguished the various states in POMDP is
that they have different noise spectra.
Note: We will
assume that the world is not too noisy, because if everywhere the noise level
is maximum (i.e. one) and everywhere the spectrum of the noise is the same,
then such a world would be completely incomprehensible. It would be
more comprehensible if the noise level is zero or close to zero.
Theorem 3. The fourth
definition is equivalent to the third.
Proof:
It is clear that Definition 3 is a special case of
Definition 4. To every world of Definition 3 we can add noise whose level is
zero everywhere and we will get a world equivalent to it by Definition 4.
Let’s do the opposite. Let’s take
a world by Definition 4. For each of the cumulative states in this world we
will see how many possible outputs the function View can return (in principle, the possible output is only one, but
because of the noise we can have many possible outputs). For each of these possible outputs, we will make a new state
in which the visible variables have exactly the value of the possible output.
Note: From every cumulative
state we make many new standard states. What we will
get will be a world by Definition 3 (and even by Definition 2 because the
visible variables will be constants). But do we
lose any information by this? Do we lose the
value of the visible and invisible variables? No,
because this information is coded in the new state we create. It reflects the value of all variables of all states (not
just of the variables of the current standard state).
How will we define the function World of the new world? If between
two cumulative states there is a connection when the action action takes
place and there is a probability that this connection is likely to take place
in the interval [a, b], then each of these two cumulative states has
been replaced by many standard states and between each state from the left ones
and each state from the right ones there will still be a connection when the action action takes place.
The difference will be only in the probability
interval. It won’t be [a, b] but [a.p, b.p], where p is the probability that it is exactly this
output obtained on the right. That is, no matter
what state you started from on the left, they all behave the same way because
they are the same according to the future. They
differ only in the present (function View)
and they are distinct only because of the noise.
∎
We will give a specific example that will show us the benefits of Definitions 2, 3 and 4. The example will be similar to the one we gave in [6, 7]. The difference will be that here as an example we will use the chess game, whereas in [6, 7] we used the TicTacToe game. The main differences are two:
1. In chess there are 64 squares, while TicTacToe has only nine.
2. In chess we will give the commands “pick up the piece” and “put down the piece”, while in TicTac Toe instead of these two commands we have only “put a cross.”
Let’s have a world in which we play chess against an imaginary opponent. We will not see the whole board with all the pieces. Instead, we’ll see only one square and the piece on this square. Let us remind that here we are dealing with the case of Partial Observability. If we were able to see the whole board, we would deal with the case of Full Observability. However, seeing only one square will not be a problem, because we will be able to move our eyes, i.e. change the square we see and thus look around the entire chessboard.
Our action will be 3tuple consisting of <horizontal, vertical, command> where:
horizontalÎ{Left, Right, Nothing}
verticalÎ{Up, Down, Nothing}
command Î{“pick up the piece”, “put down the piece”, New Game, Nothing}
The function View will return 3tuple <chessman, color,
immediate_reward>
chessmanÎ{Pawn,
Knight, Bishop, Rook, Queen, King, Nothing}
color Î{Black, White, Nothing}
immediate_rewardÎ{1, 0, 1,
Nothing}
In addition to being able to look around the board, we will need to be able to move the pieces. To do so, we have two commands: “pick up the piece”, i.e. pick up the piece you are looking at at the moment. The other command is “put down the piece”, i.e. drop the piece you picked up and put it on the square you looking at at the moment. Of course, you will not see which piece you picked up, but we hope you remember it.
For our convenience, we will assume that we always play with the white pieces and that when we move the piece (i.e., we put down the picked up figure) the imaginary opponent will immediately (exactly on the same step) make his move, i.e. on the next step one of the black pieces will be in another place. When the game ends, immediate_reward will be 1, 1 or 0, depending on whether we have won or lost, or the game has ended in a draw. In the rest of the cases immediate_ reward will be Nothing. We will assume that when the game ends, we will not start the next one immediately, but we will have time to look at the board and find out why we lost. When we are ready for the next game, we call the command “New Game” and the pieces are arranged for a new game.
Will there be wrong moves? Yes, when we are in the left column, we will not be able to move to the left. When we are looking at a black piece or an empty square we will not have the right to pick up a piece. If we have already picked up a figure, we will not have the right to pick up another one until we have put down the first one.
Let us describe this world in the terms of Definition 1. The set of the internal states will consist of three things (of 3tuples). The first will be the position of the board (the possible positions are many), the second will be the coordinates of the eye (64 possibilities), the third will be the coordinates of the picked up piece (65 options – one extra for the case when we have not picked up anything). In order to make the things to be determined, we will assume that the imagined opponent is determined, i.e. at one and the same position he will always play the same move. (Most programs that play chess are determined opponent.) In this case, it is clear how to define the functions World and View.
What are we to do if we want the imagined opponent not to be determined? For example, it may be a person or even a group of people (who are changing and alternating to play against us). The person, and especially the group of people, is an undetermined opponent. It is natural that at certain position some moves are more probable and others less probable, but it is not possible to tell exactly what the probability of choosing a certain move is. In this case, Definition 2 is most natural.
The states of the world will still be the same, but the function World will now be multivalued. If we want to go back to Definition 1 but preserve the indeterminacy of the imaginary opponent, we will have to make a complex construction of the type we did in the proof of Theorem 1. This would increase the number of internal states of the world. They are too many even now, but they are finitely many, while in the example above they will become infinitely many.
How would this example look like with Definition 3? In this case, the states will be only 64 (as many as the squares on the board.) Each state will have three visible variables (chessman, color, immediate _ reward). The third visible variable will be common for all 64 squares. The fact that the third variable is common to all squares is not said in any way. The device that tries to understand the world will have to detect this fact alone. Of course, this fact is not difficult to detect. Much more difficult to detect is the fact that the first two visible variables depend on the state of the world and are different for each square.
In this case, we will not be able to go only with the visible variables. We need somewhere to remember which piece we’ve picked up. This fact is not visible in any square. So we’ll add an invisible variable to each square. When we pick up the piece, it will disappear from the visible variables and will appear in the invisible variable of the square from which we have picked it up.
Thus we’ve constructed a world with 64 states and four variables to each state (three visible and one invisible). The number of achievable cumulative states in the world under Definition 3 is just as much as the achievable states under Definition 2. However, the world with 64 states seems much simpler and more understandable, justifying the introduction of Definition 3.
Let us now present the world in the
terms of Definition 4. There is no noise in this world and there is no point in
presenting it by Definition 4. To make such a presentation necessary, let’s add
some noise.
We will assume that the white is
very dark and the black is very bright and that it is likely to mess up the
color of the piece. We will present this in the following way.
To the visible variable “color” we will add noise with
some volume and spectrum: 50% Black, 50% White, Nothing 0%. When the square is empty, we will assume that the noise is
with Volume=0.
Next we will assume that the pieces
Pawn and Bishop are very much alike
and we may mess up them. To present this, we
will add noise to the visible variable piece, which noise will have a nonzero
value when the figure is a Pawn or a Bishop. For the other pieces
let the noise be zero. The spectrum will be Pawn
50%, Bishop 50% and 0% for the other cases.
Now let’s assume the King is too
feminine and sometimes we mess up him with a Queen, but not the other way
around, i.e. we never mess up the Queen with a King. We will
present this by adding a little noise when the piece is a King with a spectrum:
Queen on 100%.
Thus we saw that we can describe a
rather complex world in a very comprehensive way. This
example justifies the introduction of Definitions 2, 3 and 4.
Event or experiment
An event would be when something
happened, and an experiment when we did something. Of course,
for every event we may have tried to cause it or
prevent it. Therefore, we believe that with
every event we have some involvement. That’s why
we will not distinguish between an event and an experiment and will accept that
these two words are synonymous.
We want to define the concept of
experiment (event). To do this first we will say what history is
and what local history around the moment q
is.
Definition: History is the
sequence of actions and observations a_{1}, v_{1}, ... , a_{t}_{1}, v_{t}_{1}, where t is the current moment.
Note: We will not
tell in which world a given history has happened because we will assume that it
is the world that we have to understand. It does not matter by
what definition this world is defined because all four definitions are
equivalent (we will use mostly Definitions 3 and 4 because they are most
convenient to work with).
Note: What is one
step from the history? Should it be <action, observation> or
vice versa? We’ve decided it would be
<action, observation> because the moment in which we are thinking is the
moment before the action. We are not thinking in the moment after the action. In this moment we just wait for
the observation to take place. For this reason,
the history begins with a_{1.} Our
device will have to make the first action blindly because it would still not
have seen anything. So we can choose the first
action randomly. We will choose it to be the
vector of zeros (we will denote the zero by Nothing – see [6]). We have said that the world starts from s_{0,} but we do not see what happens in s_{0.} Therefore,
the world begins actually from s_{1.}
Definition: Local
history around the moment q is a
subsequence obtained from a history, where from the number of each index we’ve subtracted q.
The representation of the local
history is: a_{k}, v_{k}, ... , a_{0},
v_{0}, ... , a_{s}, v_{s}_{.}
We’ve obtained it from one particular history by taking
the step a_{q}, v_{q}
and adding the last k
steps before it and the next s steps
after it. Once we subtract q from the number of each index, step a_{q}, v_{q} becomes a_{0}, v_{0}_{.}
We will look at the local history as
a sequence of letters (that is, as a word). We will present this
word as a concatenation of two words past.future. Here past ends with a_{0,} v_{0,} and the future
is from there onwards. That is, the present is part of the past,
because it has already happened.
We want to define the concept of
experiment (event) as a Boolean function that is monotonous (that is, if the
event has happened in one particular local history and if the local history is
continued, it still would have happened). We also want this
Boolean function to be computable.
Definition A:
Experiment is a Boolean function defined on local histories past.future,
which is defined by two decidable languages L_{1} and L_{2} and is true
exactly when ∃u_{1}, u_{2} such that u_{i}∈L_{i} and u_{1}
is the end of past and u_{2} is the beginning of the future.
Note: We shouldn’t
necessarily become aware of the event at the moment it happens. We can become aware of it later (when it goes in the news).
This is the reason why the event depends not only on
past but also on the future. We might not need to be aware of the future and not even the
entire past. That is, we can understand that the
event will happen before it has happened (for example, a few steps before it
happens).
In this definition of an event, we
miss some events that we need to count from the day of birth. For
example, the event “Today is Monday” is an event of this kind. If you do not want to miss these events, we will have to
change our definition A as follows:
Definition B: The same as
Definition A with the difference that you want local history to start from the
beginning (from a_{1,} v_{1}) and u_{1} will not
only be an end of past but will
be equal to past.
In [8] we discussed dependencies
without memory (i.e. events, by Definition A, in which the lengths of u_{1} and u_{2} are restricted). In [8] we
discussed the dependencies with memory (i.e. events, by Definition A, where
L_{1} and L_{2} are regular languages.)
Note: Regular languages can
be described as words starting with something, containing something, ending in
something and in which something has happened m mod n times. Definition A tells us
that past must end in something or
contain something. Definition B adds the cases
when past must begin with something and
cases when something has happened m mod n times. However, we are not
interested in cases when past begins with something or
contains something. We
are not interested in these cases because although we are theoretically looking
at many possible histories, in fact, the history is just one (our history).
This single history has either begun with something or
not. In this history, something has either happened or has not happened.
It would be interesting, if something happened recently
(for example, no more than 3 steps ago). This
would create an event that changes its value over time. Events that are constant are not interesting to us.
Experimental
properties
Once we’ve said what an experiment
is, we are ready to define experimental property. Let us
first define property and local history around a state.
Definition: Property is
a set of cumulative states of the world. At one particular
moment, a property will be valid if the corresponding cumulative state is an
element of the property (i.e., of the set of the
cumulative states).
Note: In the
Definitions 1 and 2 there is no difference between the standard and cumulative
state. In this case, the property is simply a set of states.
Note: When we
discuss property, we will usually mean not the set but its characteristic function.
We will talk about partial properties (whose
characteristic function is partial) and the continuation of the partial
property to total.
Definition: Local
history around the state s is a local
history around a particular moment q,
which is obtained from a history in which the corresponding state s_{q} is exactly the state s.
That is, local history around s is a history that tells us how we have
gone through s.
Note: We can have
many different histories around the state
s because the past and the future are not defined unambiguously. The
ambiguity of the future comes from the fact that we do not know which of the
possible actions we will choose. In Definitions
2, 3 and 4 to this ambiguity we’ve added the ambiguity of chance. As for the past, it is also ambiguous as there may be many
different states that after an action could lead to the state s. Of
course, we can assume that each successive state is brand new. That is, assume that the states do not repeat themselves.
However, we prefer to assume that there are no
unnecessary states in our world (i.e., if two states are equivalent according
to the future and to the present, we have merged these two states into one).
That is, we will assume that the states can be
repeated. Even cumulative states can be
repeated, and standard ones are often repeated.
Each experiment defines one property
in the following way:
Definition: Experimental
property is the set of cumulative states s,
such that for s there is a local
history around s, such that in this
local history the experiment has happened (i.e. the experiment was carried out
in this local history).
Each experiment defines a property,
but that property is not recognizable but semirecognizable. That is, if
the experiment is performed, the property is valid (at that moment). We can not say the opposite. If
the experiment has not been performed, we can not say that the property is not valid
because in another development of the past and the future maybe the experiment
would be possible. If we have noise (Definition
4) then even the present may have another development because of the noise.
Each experiment divides the set of cumulative
states into two parts (those that the experiment may be performed around and those
for which this can not happen). When the experiment has been conducted around a
cumulative state, this means that it is one of the states of the property. Not
all states of the property are equally probable. Some are more probable, others
are almost improbable. We can not say what the
probability of the corresponding cumulative state is, but we hope that
gathering statistics for a particular experiment will indirectly take account
of these probabilities.
Experimental properties are the best
we have. If we want to define a property, we will have to describe it
through the experimental properties. This
description will be made using the statistics we have collected. In [8] we saw how to collect statistics for dependencies
without memory. Also in [8] we discussed even events
with memory.
What is a test?
The drawback of experimental
properties is that they are semirecognizable. We want to add
properties that are recognizable (they may not be recognizable at any moment,
but there should be moments when we can say whether the property is valid or
not).
For this purpose we will introduce
the term “test”.
Definition: Test is an
experiment with a result. The result is a Boolean
function that is defined always when the experiment has been performed and
which does not depend on the way the experiment has been performed. We
will call to the experiment the condition of the test.
The idea is, each time an experiment
is conducted to have a result and this result to be “YES” or “NO”. We do not
want the result to depend on the past and the future, because there are many
possible developments for the past and for the future. So let us assume that the result depends only on the present.
Present is what we see at the moment
(the moment of interest, not the current moment because the current moment is
one, but we are interested in all moments). That is, the present is v_{q}._{
}We see this, but we see something more. We
see which of the actions are correct at this moment and which are not. To the vector v_{q} we can add Boolean variables, one
for each action.
These variables will be visible. The value of each of them will tell us whether the action is
correct at this moment.
Note: When writing
a theoretical article we choose a structure that is easy to describe. This
article is a practical one and therefore we will choose a structure, which is
most suitable for realization. Therefore,
instead of describing the possible moves, we will describe the cumulative of
moves (see [8]). The idea is not to play with
the individual moves, but to operate with whole sets of moves. We will see two Boolean variables for each cumulative move.
This variable will tell all and nobody.
In [8] we’ve presented some
arguments showing that we can assume that the result of the experiment has the
form of x_{i} =
constant. Here x_{i} is one of
the visible variables and constant is one of the possible values of this
variable.
Note: In [8] we decided
that we will not necessarily try all possible moves in order to see which ones
are correct. In the example we gave the possible moves are 36. If we try
them all at each step, it would be annoying. Therefore,
if the visible variable is one of those who we call all and nobody, then we will assume that the condition of
the test guarantees that the necessary moves have been tried. If the value of the variable is true, then all the moves of
the group should have been tried. If the value
is false, then at least one move from the group must have been tried and it should
be such a move that shows that the variable is not true.
Test functions
Each test determines one function on
local histories.
Definition: Function of
the test is the function defined in the moments in which the condition
happened. The value of this function will be equal to the result of the
test.
We want to continue the test
function so that we have some prediction of the moments when it is not defined.
Definition: Theory of
the test is a function that for each local history returns two numbers (prediction
and confidence).
We will assume that when the function of the test is
defined, the theory returns the value of the function as a prediction with a
confidence of one. In other cases, the confidence
will be less than one.
The prediction is usually zero or
one, but it may be between these two values. In this case, we have
a prediction of the result with some probability.
Each test function defines a partial
property. We will call it the smallest property of the test. The set in which this property is defined is the experimental
property (here the experiment is the condition of the test). The value of the partial property is the value of the test
result.
Note: The
smallest property of the test is a generalization of the test function, because
it is defined for particular moments for which the function is not defined.
Can we continue the smallest
property of the test to a total property? Yes, we can, but we
can do this in many different ways. The goal is
to continue it in a natural way so that the resulting property actually
describes the world.
Definition: Property of
the test is each continuation for the smallest property of the test.
In [8] we discussed the example
where the test is “Is the door locked?” At the moments when
we’ve conducted the experiment, we know the answer. That is, we know the test function. We can correctly define
what the correct answer would be for the states around which the test can be performed
(although we will not know this answer, if we have
not conducted the test). Is there a property of
the world that describes the outcome of this test? Let us have the property “the door is locked” and let this
property is changing by some rules. It seems
like we only need to determine that property so we can predict the outcome of
this test. In fact, in our world we may have
many different doors and then it is not appropriate to describe the state of
the world with just one property. Of course, we
could say: “The door to which we are located is locked.” This is a property that is always defined except when we are
not to any door. However, a better description
of the world would be if we introduce many properties (one for each door).
So we come to the definition of a test state:
Definition: Let’s assume
we’ve split the set of cumulative states of the world into groups that we call
groups of relative stability. The test state
will be a Boolean function that is defined for
each moment and for each of these groups.
Roughly speaking, the test state
tells us which doors are locked and which doors are unlocked at the moment.
Definition: Theory of
the test state is a function that for each local history and for each group of
relative stability returns two numbers (prediction and confidence).
Note: We may have
a group of relative stability in which the test is impossible. Then what
to do? First possibility is to assume that the theory of the test state will
give a prediction of this group with a confidence of zero. Second possibility
is to make prediction anyway. Look at the open problem below.
How can we obtain the test theory
from the theory of the test state? That is, how the fact
that we have an idea about which doors are locked and which doors are unlocked
will give us an idea of whether the door in front of us is locked? First, we need to answer the question of which door we are at
(i.e., in which of the groups of relative stability we are in). Then we will take the prediction for this door that the theory
of the test state gives us.
The groups of relative stability will
be represented by the states of a finite automaton. If the finite
automaton is deterministic, we will know exactly which group we are in. If not, we will know only approximately. If the automaton is deterministic, the test state can be
expressed by several tests and the properties of these tests. The conditions of these tests will be the condition of the
test plus the fact that we are in the corresponding state of the automaton.
That’s why we prefer the automaton to be deterministic.
Otherwise, the condition that we are in the respective
state of the automaton will not be a computable function.
Test states
We gave a formal definition of what
a test state is.
Let’s say what the relationship between test states and
Definition 3 is.
If the condition of the test is the universally
valid condition, then the test states are exactly the visible variables. This is not
entirely accurate, because the tests are Boolean and the visible variables have
k possible values. To fix this inaccuracy,
we will temporarily assume that the visible variables are also Boolean.
Let’s have a test whose condition is
the universally valid one and which returns the value of the first visible
variable. Then the state of this test will be the first visible
variable. Of course, this is not one variable
because every standard state has its first visible variable. We are talking about many variables (perhaps even infinitely
many). Many of these variables may be equal.
They may even all be equal. Then the test state will not be composed of all of these
variables. Instead of this we will take one variable from each group of equal
variables.
That is, we have seen that if we
have a world by Definition 3, its visible variables are test states. Let’s do
the opposite. Let’s take the world by Definition
2 and a test whose result is the first visible variable. Let’s build a world by Definition 3, whose first visible
variable is exactly the test state of this test.
For this purpose, we will split the
states of the world into groups of relative stability. Which splitting
are we to choose? Whichever we choose will do
the job, but it is good to make the splitting so that it actually corresponds
to the world and that the groups are really groups of relative stability.
Let us now assume that each group of
relative stability is one standard state. We’ll add more
invisible variables, if necessary, to store in them all the information we have
about the cumulative state. We will define the functions
View and World in a way that the resulting world is equivalent to the one
from which we’ve started.
Note 1: We can represent
a group of relative stability through several standard conditions. This splitting
is useful, though not necessary. With this splitting
the world can become much simpler. For example,
instead of thinking that all permanently locked doors are in one standard
state, it might be simpler if we have different states corresponding to
different permanently locked doors.
What would happen if we choose an
inappropriate splitting? Let us imagine that we have split the number of
doors into metal and wooden ones. We assume that
if one of the metal doors is locked, then all the metal doors are locked and
likewise with the wooden doors. Then we see a
locked metal door and we conclude that all metal doors are locked at the moment.
The next moment we see an unlocked metal door. We decide that they are all unlocked now. Is it possible it really be so? It is possible and it cannot be confirmed or rejected
experimentally. However, if the splitting is not
adequate, the groups of relative stability will be very unstable.
Examples of test
states
Let’s take the world we described in
the example (the game of chess). Let’s suppose that the
description of this world is by Definition 2. We will discuss two tests and see
how through their test states the world can be split into groups of relative
stability and thereby the world to be presented by
Definition 3.
The first test will be “I see a
white piece”. The condition of the test will be the universally valid
condition (that is, when do I check whether I see a white piece. I always check.)
The result of the test will be color=white.
What are the groups of relative stability? These will be
the squares on the chessboard. The test property
will be “There is a white piece in the square we are looking at”. The test state will be the position of the chessboard.
Well, not exactly the position but that on which
squares there is a white piece. The test state
will not contain information about where the black pieces are or about which exactly
is the white piece that is in the square.
We received a world with 64 standard
states. The visible variables are clear. We need to add invisible variables in which to store the
information about the cumulative state that has not stored in the visible
variables. We can make this in the same way as
we already did.
The second test will be “If I see a
white piece, I can pick it up.”
The result of the test will be “Pick
up the piece is a correct move.” We should note that “pick up the piece” is not
a single move, but a whole group of moves (i.e. a cumulative move) because by
picking up the piece we can simultaneously move horizontally or vertically. That is, the result of the test is “In the group of moves <*, *, pick up the piece> There is at least one correct move.” We cannot have all moves of
the group to be correct, because a move may be incorrect because of the
movement (for example, if we are in the left column and try to move to the
left). This group has a visible variable nobody,
which has to be false. Formally, it will look like this: nobody(<*, *, “pick up the piece”>)=false. Here is given for which
cumulative move nobody is, because different cumulative moves
have different variables nobody.
The condition of the test will be “I
see a white piece”
(i.e., color=white). We
must add to the condition of the test that we have tried the moves from the
group. If nobody is true, we’ve tried all the moves, if nobody is false, we’ve tried at least one move, but such one which is correct.
Here the groups of relative
stability are two:
states for which the test will always return true, and those for which it will always
return false. We will have false
when we’ve picked up another piece and have not put it down yet and when the
game is over and we have not played “New Game” yet in order to begin a new one.
How will we present the world by
Definition 3? As noted in Note 1, a group of relative stability can be
represented by more than one standard state. Here
we will present the group in which the test always returns false by two standard states (when we picked up a piece and when
the game is over). Let’s note that there is no
way to pick up a piece if the game is over.
We got a world with three standard
states. The visible variables are clear. We need to add invisible variables in which to store all the information
for the cumulative state. In other words, we
need to store the position of the chessboard (for this we will need 64
variables or 2.64 if we are wasteful). We also
need to store the coordinates of the square that we are looking at, and if we’ve
picked up a piece – where we’ve picked it up from, and which this piece it is.
How do we find the theories?
What is the difference between the
theory of the test and the theory of the test state? In the
first case we assume that we have only one group of relative stability and that
the test determines one property. In the second
case, we assume that we have many groups of relative stability and that our
test determines many properties of the world (one for each group).
We will describe how we determine
the theory of the test. (The theory of the test state is determined in
an analogous way.)
We have collected statistics for
specific experiments. When both the experiment and the test are
performed, we count how many times the test has returned “YES” and how many
times it has returned “NO”. Let these be the numbers n and m. Then the prediction given by this
experiment is , and the confidence depends on n+m. At some point, many experiments
have been conducted, each of which gives us some kind of prediction with some
confidence. Here we will not discuss how to calculate the overall prediction
and overall confidence.
Note: When a test
state is distorted with noise (Definition 4), then we can not find rules that
give an exact prediction (i.e. the prediction to be an integer). Each prediction, which is with a large enough certainty, will
be approximate (i.e. it will be some probability p such that 0<p<1).
Conversely, if the prediction is approximate, there is
no way to know if it’s because the result of the test is distorted by noise or
because the condition of the test is such that it gives a rough prediction.
If all predictions are approximate, we may assume that
we have some noise or that we have not yet found a rule which will give us an exact
prediction.
Besides experiments, we will predict the property of the test based on the
assumption that this property is stable. We will assume that we have gathered
statistics on how stable this property is. Based on this, we will assume that
once we have checked the property (we have done the test), the value of the
property is still the same for some time. The confidence of this assumption
will decrease over time (i.e., the more steps goes after the test, the less we
count on the assumption that the value is the same).
The next level is to make
the assumption that we are
not alone in the world. That there are other agents in the world and that this
agents change the test state at their own discretion (see [8]).
How do we define groups of relative stability to define a test state that
is meaningful and adequate. Once again we will use statistics. In fact, this is
the task of finding a finite automaton that can meaningfully split the states of the world into groups. This
task will also remain outside of the topics discussed in this article.
Conclusion
This article began with the claim to
be different and offer for AI something more than an approximation. However,
here again the approximation method is used. When
we have a test function and we want to continue it to a test property, then what we do in practice is an approximation.
However, there are differences. Most authors search for an approximating function that should
be the solution (i.e. it must be AI). Here we seek
not one but many approximating functions (one for each property). These functions are not the AI. Their only task is to assist the
device to understand what is going on at the moment.
Note: So to
speak, if we had Full Observability and if we were able to see everything,
these approximations would not have been needed at all. Although the search for test properties
is directed to the case of Partial Observability, it would be helpful for us
even in the case of Full Observability. The
problem with Full Observability is that in this case we have too much
information and it is very difficult to distinguish the essential from the inessential.
Let’s forget what we see and focus only on test
properties that we have found. We will choose these
test properties that are interesting and this
will be the essential information that we will need.
As we have said, the approximation
is not the whole solution, but only part of the solution. Before
that, we need to collect statistics to have the basis on which to approximate.
Here we should mention dependencies with and without
memory, which we’ve discussed in [8]. Then we
make an approximation based on the experiments and the statistics we have
collected about them. We also use the assumption
for the stability of the test states (this can also be regarded as an
approximation). The next method we use to
determine the value of test states is the assumption that there are other
agents in the same world apart from us and that they are changing these test states
in order to help us or screw us. This approach
can no longer be called approximation, at least because there is no formula to compute
it by. That is, the description in this article
is more than just an algorithm for approximation.
Even after finding the test states
the problem is not yet solved, because what remains is to plan the future
actions to obtain maximum rewards.
Note: Here we set
the task to describe a particular algorithm and it is the algorithm of the thinking
machine. We are not asking ourselves what AI is or the question what the definition of AI is. These questions were discussed in [5] as well as in other
earlier articles. That’s why in [5] we use the
classic definition of Reinforcement Learning, while in this article we have
offered three equivalent definitions. When we
want to give a theoretical definition of AI,
then the classic definition of Reinforcement Learning us enough, but when we
want to describe AI in sufficient detail to bring it to a realization, then we
need to change the definition so that it is easier to work with it.
Why test properties and test states
are so important?
It is these properties and these states that give us
the understanding of the world. To understand
the world means to have an idea for things we do not see directly. For example, let’s know that the door on the third floor is
unlocked. In order to formulate this proposition,
we need the respective test. In order to decide
whether this proposition is true, we will need a theory of the test state of
this test.
Many researchers in the field of AI
agree that AI should be able to make logical conclusions based on a system of automated
theorem proving – for example, propositional or predicate calculus. We also
share this view, but the question is how from the sequence (action, observation)
to reach propositional or predicate calculus. To
have a propositional calculus we need propositions.
To make a predicate calculus, we will need predicates.
If we cannot understand the sequence (action,
observation), it would be just a noise for us. How
can we make propositions and predicates from this sequence? The binding unit we need is test properties and test states.
For example, the test property “The door is unlocked”
may be the proposition we are looking for. The
test state “Door X is unlocked” will be the predicate from which we can make statements
of the type “All doors are unlocked.”
References
[1] Boris Kraychev
and Ivan Koychev. Computationally Effective Algorithm for Information Extraction
and Online Review Mining. In Proc. of International Conference on Web
Intelligence, Mining and Semantics June 1315, 2012, Craiova, Romania, ACM.
[2] Tatiana
Kosovskaya. SelfModificated Predicate Networks. International Journal
“Information Theories and Applications”, Vol. 22, Number 3, 2015, pp.245257.
[3] Richard
Sutton and Andrew Barto. Reinforcement Learning: An Introduction. MIT Press,
Cambridge, MA, 2017. ISBN 0262193981.
[4] Dimitri
Bertsekas and John Tsitsiklis. NeuroDynamic Programming. Nashua, NH: Athena
Scientific, 1996. ISBN 1886529108.
[5] Dimiter Dobrev. Comparison between
the two definitions of AI. arXiv:1302.0216,
January, 2013. http://www.dobrev.com/AI/
[6] Dimiter
Dobrev. Giving the AI definition a form suitable for engineers. April, 2013. http://www.dobrev.com/AI/
[7] Dimiter
Dobrev. The algorithm of the Thinking machine. viXra:1605.0190, May, 2016. http://www.dobrev.com/AI/
[8] Dimiter
Dobrev. Incorrect Moves and Testable States. viXra:1705.0108, May, 2017. http://www.dobrev.com/AI/
[9] Dimiter
Dobrev. Incorrect Moves and Testable States. International Journal “Information
Theories and Applications”, Vol. 24, Number 1, 2017, pp.8590. http://www.foibg.com/ijita/vol24/ijitafv24.htm
[10]
Dimiter Dobrev. Incorrect Moves and Testable States. In Proc. of 11th
Panhellenic Logic Symposium, July 1216, 2017, Delphi, Greece, pp.6570. http://pls11.cs.ntua.gr/
Няма коментари:
Публикуване на коментар