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Comment by mezentius | Hacker News Reader

>>The best performing variant happened to be random.

Some years ago I heard an anecdote from a developer who had worked on a video game about American football. The gist of it was that they had tested various sophisticated systems for an AI opponent to choose a possible offensive/defensive play, but the one that the players often considered the most "intelligent" was the one that simply made random decisions.

In certain domains, I think, it's quite difficult to beat the perceived performance of an AI system that merely makes committed random decisions (i.e. carried out over time) within a set of reasonable choices. If we don’t understand what an agent is doing, we often assume that there is some devious and subtle purpose behind its actions.

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It's quite common in games for AI to pick a random decision. Simply put, a good AI is a character/NPC that appears to have a mind of its own, and its own life. Nothing beats random at explaining someone's behaviour based on a personal history you don't know.

If AI responded/acted based on a predefined set of patterns that could be recognizable, the player would automatically feel it (pattern matching) and makes the NPC far less interesting.

Right-- but often an NPC is merely scenery or traffic, in the sense that their behavior does not compete with your interests. What I found interesting about the football example is that the random strategy of the NPC-oach both suggested a deeper intelligence AND proved to be an apparently effective opponent.

Beyond reinforcing our tendency to project, as you say, a personal history on random behavior, it also highlights what a few other people have commented: that in many non-cooperative situations a committed random strategy is extremely effective, and perhaps more effective than a biased, seemingly "rational" strategy. (For another example, I believe Henrich's "The Secret Of Our Success" discusses the possible adaptive benefits of divination as a generator for random strategies among early societies.)

It's how in Rock Paper Scissors you can probably do better against a smart opponent by playing randomly than by trying to trick them. At least they can't get in your head, because there's nothing there. You won't do better than chance, but at least you can't do much worse.

So, use a random strategy and measure the entropy of your opponent's behavior. And modify accordingly, as needed.

A lot of the best ml right now is effectively about making better conditional probability distributions. You always get random output, but skewed according to the circumstances, and sharp according to confidence in the result.

Unless you consider that randomness, whatever source, is some form of intelligence. (I am only half-joking here).

I'm not sure what the term for it is, but humans have an uncanny ability to ascribe meaning to pure randomness. I'm not surprised a random AI can appear smart.

When your audio player picks these random tracks https://i.imgur.com/QRoGQRy.png (example from earlier in the day) you start believing in a Higher Power..

Humans are great at finding "patterns" in random noise.

Sometimes being unpredictable makes for a good strategy

We are very good at finding patterns where there aren't any but it's important to remember that random is actually the best answer a lot of the times. Maybe it's as simple as random plays being the hardest to predict and that's the best until you can get an AI trained on the meta of how to plan for what the current player is thinking you'll do.

It is well known that tit-for-tat is the best strategy for iterated prisoners' dilemma. See https://en.wikipedia.org/wiki/Tit_for_tat

It may sound similar but offensive vs. defensive behavior in (video) game strategy is a much different concept than cooperation in game theory.

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