The role of randomness in the Copycat architecture

This is an addendum to my post The Coderack and Codelets.

I forgot to write about one important characteristic of the Copycat architecture. In traditional Game AI randomness is used to make the character behave less predictable and thus look more human. But this randomness is applied at a very high level, for example upon an encounter there is a 80% chance that the character reacts hostile to the player and 20% chance that he is not. While this might be fine in many cases, there might be situations where such “dumb” randomness leads to quite absurd behaviors. So the random weights would need to be adjusted to the situation, which in itself is a very hard job.

Copycat also makes extensive use of randomness, i.e. every time it picks the next Codelet to execute it will do so randomly – weighted but still random. But this randomness happens at a very low level. As can be seen in Copycat the top level behavior is much more stable – if there is one obvious behavior, it will pick that quite deterministically. Only if there are multiple conflicting behaviors which are valid reactions to the situation, each one will have a certain probability to win (if you repeat the decision making process multiple times with the same input, it will pick decision A_i with probability p_i). But these probabilities are highly dynamic, depending on the input, and will (hopefully) always lead to decisions that “make sense” in the given situation. And the probabilities are not defined in advance by the programmer but a result of the interactions of the different Codelets as the system comes to a conclusion.