## Wednesday, 29 October 2014

### Gain feelings

This is the 6th -and last- post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence, goal-less intelligence, enjoy feelings, fear feelings and mood feelings before you go on reading for a proper introduction on the subject.

#### Introduction

To end the enumeration of the three basic feelings associated with a goal, we need to deal with gains and looses, and their proper feeling representation.

### Gain or Loose feelings

When something you had at the initial state, like your health level, is higer or lower in the final state representing the end point of the future, it means you have gained or lost something.

In the early example of the "speeding" enjoy feeling, if you started totally stoped, and at the end of the future you are stoped again, then you did not have any real gain. Speeding is something you enjoy experiencing, but when it stops, nothing is left.

But with energy of health it is not true. If you started with 100% of health, but in the middle of the future you crash and loose 40% of it, then, at the end of the future, this lost is conserved. You really have -40% of something valuable for the agent.

Inversely, if you touch an energy green drop and gained energy, then may be you started with 35% of energy and ended the future with 55%. You had a real gain here, as even if you now do a full stop, the agent retain this gain, it still has 55% of energy.

When something "real" is gained or lost during a future, being it health, energy or some other valuable thing, you need to modulate the final score of the future, the "FutureEnjoy" we formed by adding all the "StepEnjoys" on the previous post. We will modulate it, as always, by multiplying if by a future's "FinalCoef".

#### The "GainCoef"

In the first example, as we were tracing the future we got some sum of "FutureEnjoy", but we also started with 100% health and ended with 40%. The "GainCoef" is then calculated as (final value / initial value) = 40/100 = 0.4.

In this example, as we lost 40% of the initial quantity, the "GainCoef" is 0.4, lower than 1 as it is representing a loos and not a gain.

But we can not just use this 0.4 as the "FinalCoef" we need, it would not work (it was in the root of the problem the previous goal model has, not being able to simulate more than a few seconds) as there is a factor we forgoten to take into consideration: the time.

#### The "TimeCoef"

If the doctor says to you "If you don't stop doing this or that, in 80 years may be you will die because of it", would you panic? Nopes. 80 years is way too much for me to care about a possible lost I could have in the future. We would dismiss the alarm and go on with our lives.

But if it says " ... then you will probably die in two months", all the alarms fires in your mind, and you promise to your self to avoid "this or that" as if it were the only important thing in life.

The "GainCoef" is always zero, as your health drops to 0 and you die, but how far in the future it occours makes it more or less important to you. We need a "TimeCoef" to make the "GainCoef" fade away in importance as it happends more and more into the future.

To construct this "TimeCoef" I defined two parameters that controls how near in time it has to happend to start being important tom me, the "ReactionTime" in seconds, and a second "Urgency" factor that controls how fast the alarm then grows as it happends nearest to your present in time.

The following graphic shows a "TimeCoef" plot for a "ReactionTime" of 5 seconds and "Urgency" factors of 1 (blue line) and 2 (purple line), while the green line represent the "FinalCoef" for Urgency=2 applied to a lost of 0.4 (you lost 40%):

Lets start with the blue line, it represent how the "TimeCoef" varies, for a "ReactionTime" of 5 seconds and an "Urgency" of 1, as the loose occurs in different points in time (time is presented in the X axis, where 0 is the initial point where the future started).

"TimeCoef" is 1 for times longer than the "ReactionTime" because if the lost occurs more than 5 seconds far in the future, I will not care about it, so the coef need to be 1 not to change the enjoy feeling we could had.

For times smallers that 5 s. the "TimeCoef" linealy drops to 0, meaning alarm grows linealy as it happends near to the present time.

So in this case ("Urgency"=1) the "TimeCoef" will varies with time t with:

TimeCoef = Min(1, t/ReactionTime)

If "Urgency" were 2, then the lineal growth would convert into a X² growth. As number are smaller than 1, we are lowering down the line, meanig the alarm will initialy grow faster, as you see on the purple line.

If "Urgency" were set to 1/2 the line would had been above the blue line, so we would get the opposite efect: the alarm will initially grow slowly, and only when it happends really near to the present, it gets really scaring.

So for any "Urgency" value, the complete "TimeCoef" formula is:

TimeCoef = Power( Min(1, t/ReactionTime), Urgency)

#### Mixing them into the "FinalCoef"

Finally, we mix both "GainCoef" and "TimeCoef" as in the green line:

FinalCoef = GainCoef  * TimeCoef + (1 - GainCoef)

Where (1 - GainCoef) represent the 60% you didn't lost.

### The final "emotional" formula

We now have all the ingredients and the way to mix them together to score a future ina completely "emotional" way:

If we have 2 goals G1 and G2, and each goal has 3 params for the 3 basic feelings called G.Enjoy, G.StepCoef and G.FinalCoef, then the future would score as this:

StepEnjoy = Sqrt( G1.Enjoy² + G2.Enjoy² ) * (G1.StepCoef * G2.StepCoef)
Future.Enjoy = Sum for all steps( StepEnjoy )

Future.Score = Future.Enjoy * (G1.FinalCoef * G2.FinalCoef)

That was all, this gives you an "emotional" way to socre futures, so if you had 2 options, now you can trace 100 futures for each, discard repeated ones, and score each option with

Option.Score = Sum for all different futures( Future.Score )

And, the AI desicion will be:

Decision = Sum for all option( Option.Vector * Option.Score )

Where Option.Vector contains the changes you does to all the degrees of freedom, or joysticks.

#### All is done then?

Apart for a post I need to add about how to model the joystick itself , yes, we are done with the emotional intelligence: we have fully defined a complete layer of emotional intelligence.

But there is still a big step awaiting: multilayered emotional intelligence.

But it will be on some weeks from now, I need to take a little time to prepare a V2.0 of the demo app, my actual version needs to be cleanep up a little before it.