Fear feelings

This is the 4th post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence, goal-less intelligence and enjoy feelings before you go on reading for a proper introduction on the subject.

Introduction

As commented in Introducing Emotional Itelligence post, the goals, when are defined usign "feelings" toy models, have only three scoring parameters, three kind of emotional "outputs".

The first of them correspond to things you "enjoy" experiencing, like speeding where you enjoyed velocity. Enjoy feelings are basically added together into a general "enjoy feeling" score after each movement the agent does.

But all the three components of a goal, each kind of basic feeling (enjoy, moods and gains) has a reverse, a negative counterpart you need to know and properly manage in your algorithm.

Fear feelings

Enjoy feelings

This is the 3rd post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence and goal-less intelligence before you go on reading for a proper introduction on the subject.

Enjoy feelings

Once I had this simulation with the goal-less algortihm working I wanted to go further. The kart was really driving it quite nicely, but it clearly was not optimal.

Why? The idea was so simple and powerful it was not clear the problem at a first glipse.

Goal less intelligence

This is the 2nd post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence before you go on reading for a proper introduction on the subject.

Goal less intelligences

In my first post I already commented on the internals of the simpliest entropic intelligence possible, one that scores all the futures as 1. If you haven't read it and want to know in more detail this case, you can visit the link first. Anyhow, I will try to summarize the basic working of tis model again.

First "emotional" video

My code is not still "fully emotional" this far, some cases are not still used and others lack more testing, but I am ready to produce my first video where goals are considered in this new "emotional" way.

The video just shows the old test case of a set of agents -karts in this case- moving around, where the must collect drops and then deploy it on squared containers to get a big reward, but this time they are rockets inside a cavern and follow the goals in a fully "emotional way".

The changes are not totally evident in this case, the task is too simple to make a great difference, surely I need to find more challenging scenarios for the next videos. But you will still notice the big step in the small details: how actively the pursue theirs goals and how efficiently they do it.

Introducing Emotional Intelligence

In the actual entropic intelligence algorithm, the scoring you assign to each different future you imagine is the golden key, as they determine how the options you are considering will compare to each other, ultimately defining the resulting "psicology" of the agent that makes it behaves one way or the other.

These future scorings are made up by adding the effects of a set of different "motivations" or goals the agent has, like in "I love speeding", "I care about energy" or "I care about health", measured over the future's path, step by step, like in a path integral aproximation.

Being able to define the right motivations set for an agent, along with a proper way to calculate the different effects those motivation could have on every step the agent takes, and mix them together to get the correct future's score, is ultimately what I am looking for and the life motiv of this blog.

Adding evolution to the mix

My second experiment was about using a simple evolutive algorithm to fine adjust the goal strengths in order to get the best possible set of strengths given the environment you place the players in.

I added a "magic" check box on the app so you can switch on this evolution, then add new players if desired, goals, and let the population grow and the best adapted selected over time.

Follow my orders!

After some months without working in this algorithm, I am back with some new ideas to code, but before this, I want to show you a couple of experiments I made before the summer break.

First one, showed at the talk in Miguel Hernandez University in an early stage, is just a concept test: could this intelligence be used to drive a vehicle without effort and safely, but following you directions in real time?

Imagine a real car using this algorithm to drive you anywhere, you can let it drive for you as in a google car, but with an added hability of a "semi-autonomous mode".