
(Original Image by everyone’s idle.)
This post was a originally published on Luma Labs, now dead.
As old as stimulus-response techniques are, they still form an important part of many AI systems, even if it is a thin layer underneath a sophisticated decision, planning, or learning system. In this tutorial I give some advice to their design and implementation, mostly out of experience gained from implementing the AI for some racing games.
A stimulus response agent (or a reactive agent) is an agent that takes inputs from its world through sensors, and then takes action based on those inputs through actuators. Between the stimulus and response, there is a processing unit that can be arbitrarily complex. An example of such an agent is one that controls a vehicle in a racing game: the agent “looks” at the road and nearby vehicles, and then decides how much to turn and break.

Tools for editing game levels and AI for your own games are nice to have, but it is not always practical to implement these for small projects, nor is it affordable to buy them off-the-shelf or bundled with expensive middleware.
I wrote an article for Dev.Mag covering
The example for the tutorial
In this new version of 
It is sometimes necessary to find the distribution given a sample set from that distribution. If we do not know anything about the distribution, we cannot recover it exactly, so here we look at ways of finding a (discrete) approximation.

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