Life Goals in Ashmooghville

Digging my aged laptop, I found the following research report which was an attempt to use evolutionary computation to model certain aspects of life and try to solve an optimization problem for life which maximizes happiness.
Of course there are lots of simplifications and assumptions in the study but it can act as a very simple root for further more complex studies.

Anyway if you ever wondered what the Ashmooghs (mythological creatures) did eventually to feel happy in Ashmooghville, you can read this report. (Farsi Text)

Ashmooghville

The idea here is that we assume these creatures have goals in 4 different layers which can be measured by a single attribute.
In the biological layer, they try to maximize their lifetime.
In the psychological layer, which acts as the software for the biological layer, they try to maximize their pleasure.
In the social layer, they try to maximize their power.
In the cultural layer (software for the social layer), their goal is to maximize the amount of meaning in their life.

Assuming they have limited tasks which can be selected in their life span, the question then would be to know what to do in life in order to maximize happiness based on the above 4 parameters.

An evolutionary program has been developed to do a bit of an analysis on the situation.

If youre interested in playing with the code and tweaking the attributes, download the code here:

Ashmooghville Java Source Code

Enter Gyoomard

Gyoomard is the ambitious evolutionary AI project which we've started out recently. With the goal of developing simulated character control mechanisms using evolutionary computation and neural networks.

Gyoomard means the living mortal and has been the first human in Persian Mythology.

Do the Evolution

Artificial Intelligence was the main title of a magazine called "Rizpardazandeh" which was written with big bold letters on it when I was in the 9th grade. With full curiosity I bought the magazine and read all the articles in it related to the topic, it was mostly about the main ideas and the MIT research lab and Marvin Minsky. Whatever it contained, the event raised my interest and made me sneak into any magazine, book, talk or conference related to AI which I saw close by from that time on.

The general idea of intelligent machines was just so exciting and fun to think of but as I studied more and more about the classical/Symbolic AI, this feeling changed a bit and witnessing the different algorithms in symbolic AI which simplify the human intelligence activities to a few simple steps and enable machines to follow it, made the special human activities seem less complex and important and the real magical feeling behind a real intelligent machine was reduced to rather deterministic computing machines. The glory and value of Artificial Intelligence was over shadowed by the reduced value of the actual intelligent tasks. During that time thinking about AI would result into many ideas in other fields such as psychology, biology and neuroscience.

The border between AI and any computer program was soon diminishing since any computer code would follow some steps to solve a problem which would seem it required intelligence. The definition of intelligence itself was moving like an avalanche. A simple example is the game of chess which was thought of as a game needing lots of intelligence to be played but when Deep Blue beat the human chess master, chess wasn't assumed such an important activity to measure AI anymore.

The original magical feeling returned to me one more time when I read about Genetic Algorithms for the first time a few years later. I was even on the absolute climax the first time I wrote a code that would try to optimize the soluton to a problem using GAs. It was just unbelievable to see every line of output getting better and better as the CPU was processing while you knew that deep inside the code, there are no explicit algorithms that would suggest a solution and actually no heuristics in there to help. Just pure Evolution. Finding out about Neural Networks and their mechanisms and the way they can be used to solve problems added to this feeling of joy. AI contained the magical essence once more but this time in the form of evolutionary and non-symbolic AI.

I've worked on implementing these ideas into different projects and the techniques have proved to be very valuable every time. Most recently, along with MNO, we have started a new research project in the field of evolutionary optimization for sensory motor controllers of a simulated robot. The simulation test bed is almost ready now and the main task which is evolving the robot brains for walking (the first test) needs to be followed. The previous project which we worked on with MNO was "Farsi Cursive Hand-Writing Recognition" software which used feed forward neural networks which were trained by GAs but in the current project we will focus on the use of Continuous Time Recurrent Neural Networks (CTRNN).

Its evolution Baby ....