MRI 3 - The Foundations of a Foundation

MRI 3.1 Organising the Foundations of a Foundation
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In this document we need to do some consolidation by laying out a foundation
of principles and ideas from what research we have gathered, and the ideas
which we have come up with so far.

This foundation will have to consist at least, of the following components:

1. A set of principles that provide the vision of our kind of intelligent
system

2. The basic principles and ideas of our approach, and the rationale behind
them

3. What questions remain to be answered, and what needs to be done to answer
them before we can at least start building something that fulfils our vision
partially


We will try to fill up those categories shown above, taking exerpts from MRI
1 and 2.


MRI 3.2 Our vision of an intelligent system
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"Intelligence" includes (ie. not exhaustively):
1. The ability to execute difficult tasks efficiently
2. The ability to adapt to increase the efficiency in tackling these tasks

The intelligent system must have the following qualities (...some apart from,
others in assistance in...) fulfilling the above two criteria:

1. Decentralised operation - everything is everywhere and nowhere...and in no
particular place.

2. The architecture breeds its own intelligence. Hence the complexity of its
behaviour comes from the operation of the system.
Our design is concerned with maximising the potential of the system to
generate complex behaviour. (Thus for a robotic organism living in a highly
dynamic environment requiring complex behavioural mechanisms, the system can
provide that)

3. Precision is not always necessary. The system should be tolerant of
inprecision. But we should take a step further ahead of nature, and demand
that the system should be capable of producing precision when required.

4. The system should be adaptive. It should learn and optimise itself.
It should posses the skill of learning, which we said in MRI 1.




MRI 3.3 Basic Principles and Ideas of our Approach and their Rationale
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1. The Intelligent System is composed of a network of interconnected units
whose behaviour follows certain rules.
These are the key features of the biological neural network, which we believe
are directly responsible for the desired characteristics which we specified
in the previous section. Hence we have reason to investigate these features
more thoroughly and incorporate them in our nascent intelligent system.

The underlying simplicity of such an architecture is also attractive to us,
yet there is empirical evidence that this form has potential to generate
complex behaviour.

As the system derives its functionality in a decentralised manner, the
resulting robustness is also very desirable.

2. In order to exhibit the two important qualities that characterise
intelligence (as stated in the previous section), we need to create a source
of power or motivation for the system to do so. We call these DRIVEs.

We propose two drives, the EMOTION drive (E drive) and the SURVIVAL drive (S
drive).

The S drive is based on the biological organism's need to survive. To
survive, certain physiological parameters must be satisfied. These parameters
are fed into the system, which seeks to act via its outputs to ensure the
parameters stay within certain prescribed limits. In biological organisms,
these limits probably evolved. In our robotic organisms, we can choose to set
these limits ourselves in our designs, and perhaps allow those limits to
evolve through successive generations of robots (refer to the document on
robocology).


The E drive is based on emotions in biological organisms, most obviously in
humans. Emotions come in pairs, positive and negative ones. The system seeks
to minimise the negative emotions and maximise the positive ones.

The E drive may help complement the S drive in ensuring survival.
In addition, its purpose may also help increase the variety and complexity of
behaviour the system can exhibit.
Perhaps emotions evolved to enhance the survivability of biological
organisms. Perhaps some emotions like anger and fear were evolved to enhance,
but the nature of emotions in a network architecture meant emotions were dual
and had to come in pairs. Hence came the positive emotions. And perhaps at
first this served as a drive to keep the emotional state positive, since
negative emotions implied a threat to survival and actions had to be taken to
avoid this. Then the dependance on positive emotions evolved from this.

We propose that, similar to the S drive, what condit\ions evoke positive
emotions in the E drive mechanism can be set by the designer, and then
allowed to evolve over generations of robots.


3. In the conception of robocology, we seek to create an environment that a
robotic organism would survive in. The environment creates the conditions and
the challenge, while the DRIVES created the driving force for the robot to
behave in a way that would meet the challenges of the environment.

The driving force alone is not enough. It must drive some 'machinery'. These
are certain specifics of behaviour and skills to design. We draw inspiration
from subsumption, to break down certain broad tasks (which we must first
define) such as motor control and visual perception into smaller, more
manageable bits and layered into hierarchies of control.


4. These 'modules' may perform highly specific functions, such as those
performed by the lowest level pattern generators in the brain's motor control
hierarchy. They merely 'execute'.

Other higher level modules, or groups of them or the whole layer of modules
interacting with one another might perform the additional tasks of 'creation
and conception', and also 'learning and self-enhancement'. These fulfil our
two criteria of intelligence stated in the previous section.

while conception could have given rise to the patterns of behaviour exhibited
by the lowest level 'modules', this would have taken too much time to evolve,
since their effect is least profound, and to hit upon the right combination
of behaviour among a large number of modules to meet the high level of
objectives provided by the S and E drives takes too long to happen.


5. We can generalise every task to be a task of
1) conception of objects
2) destroying or creating connections between sets of objects

In other words we have incorporated the connectionist model of cognition, and
extended it to all parts of the intelligent system from low level behaviour
to high level ones.


5. In Summary...

In point 1, We have specified certain features our architecture must possess.
This is based on the assumption that they lead to the highly desired features
that we have outlined.

Point 3 shows us the way we intend to manage the complicated problem of
generating behaviour.

Clearly the idea of conception and self-enhancement is an innate objective in
the design of the architecture. The design must fulfil these two goals from
bottom up, as specified by point 4 in order to satisfy our criteria for
intelligence.

Point 5 specifies the general way in which stuff is processed in the system,
which is drawn from Connectionism.

Point 2 provides power to run the whole system through its 'drives'.

I STRESS THE IMPORTANCE OF THE SYSTEM TO MODIFY ITSELF TO ENHANCE ITS
ABILITIES. THIS IS AN IMPORTANT GOAL OF THE DESIGN.

MRI 3.3 What needs to be answered first, and what must be done to do so
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1. In reference to point 4 of the previous section, we need to know how much
specifics we need to design and how much to let the system evolve.

2. How exactly do emotions enhance the normal S drive mechanism in biological
organisms? (do they? or how else did the Emotions evolve?)
Why do they enhance survival (by supplementing the S drive) in a way the S
drive alone cannot?

3. How do we design the drives into the architecture? Especially the E drive?

4. [THIS IS THE BIG QUESTION] How do the features specified in the previous
section's point 1 provide the advantages which we already see in biological
brains?