Gamasutra Article on Intelligent Mistakes
There's a nice, if incomplete, article on Gamasutra today by Neversoft's Mick West titled Intelligent Mistakes: How to Incorporate Stupidity Into Your AI Code. It's not a new subject, certainly, but what caught my eye was the fact that he used the game of Poker as one of his examples.
In the first chapter of my book, "Behavioral Mathematics for Game AI," I actually use Poker as a sort of "jumping off point" for a discussion on why decision-making in AI is an important part of the game design. I compare it to games such as Tic-Tac-Toe where the only decision is "if you want to not lose, move here," Rock-Paper-Scissors where (for most people) the decision is essentially random, and Blackjack where your opponent (the dealer) has specific rules that he has to play by (i.e. < 17 hit, > 17 stand).
Poker, on the other hand (<- that's a joke), is interesting because your intelligent decisions have to incorporate the simple fact that the other player(s) is making intelligent decisions as well. It's not enough to look at your own hand and your own odds and make a decision from that. You have to take into account what the other player is doing and attempt to ferret out what he might be thinking. Conversely, your opponent must have the same perceptions of you and what it is you might be thinking and, therefore, holding in your hand.
Thankfully, there is no "perfect solution" that a Poker player can follow due to the imperfect information. However, in other games, there are "best options" that can be selected. If our agents always select the best options, not only do we run the risk of making them too difficult ("Boom! Head shot!) but they also all tend to look exactly the same. After all, put 100 people in the exact same complex situation and there will be plenty of different reactions. In fact, very few of them will choose what might be "the best" option. (I cover this extensively in Chapter 6 of my book, "Rational vs. Irrational Behavior."
Our computerized AI algorithms, however, excel greatly at determining "the best" something. Whether it be an angle for a snooker shot (the author's other example), a head shot, or the "shortest path" as determined by A*. After all, that is A*'s selling point... "it is guaranteed to return the shortest path if one exists." Congratulations... you inhumanly perfect. Therefore, as the author points out in his article, generating intelligent-looking mistakes is a necessary challenge. Thankfully, in later chapters of "Behavioral Mathematics..." I propose a number of solutions to this problem that can be easily implemented.
Anyway, I find it an interesting quandary that we have to approach behavior from both sides. That is, how do we make our AI more intelligent... and, how do we make our AI less accurate? Kind of an odd position to be in, isn't it?
In the first chapter of my book, "Behavioral Mathematics for Game AI," I actually use Poker as a sort of "jumping off point" for a discussion on why decision-making in AI is an important part of the game design. I compare it to games such as Tic-Tac-Toe where the only decision is "if you want to not lose, move here," Rock-Paper-Scissors where (for most people) the decision is essentially random, and Blackjack where your opponent (the dealer) has specific rules that he has to play by (i.e. < 17 hit, > 17 stand).
Poker, on the other hand (<- that's a joke), is interesting because your intelligent decisions have to incorporate the simple fact that the other player(s) is making intelligent decisions as well. It's not enough to look at your own hand and your own odds and make a decision from that. You have to take into account what the other player is doing and attempt to ferret out what he might be thinking. Conversely, your opponent must have the same perceptions of you and what it is you might be thinking and, therefore, holding in your hand.
Thankfully, there is no "perfect solution" that a Poker player can follow due to the imperfect information. However, in other games, there are "best options" that can be selected. If our agents always select the best options, not only do we run the risk of making them too difficult ("Boom! Head shot!) but they also all tend to look exactly the same. After all, put 100 people in the exact same complex situation and there will be plenty of different reactions. In fact, very few of them will choose what might be "the best" option. (I cover this extensively in Chapter 6 of my book, "Rational vs. Irrational Behavior."
Our computerized AI algorithms, however, excel greatly at determining "the best" something. Whether it be an angle for a snooker shot (the author's other example), a head shot, or the "shortest path" as determined by A*. After all, that is A*'s selling point... "it is guaranteed to return the shortest path if one exists." Congratulations... you inhumanly perfect. Therefore, as the author points out in his article, generating intelligent-looking mistakes is a necessary challenge. Thankfully, in later chapters of "Behavioral Mathematics..." I propose a number of solutions to this problem that can be easily implemented.
Anyway, I find it an interesting quandary that we have to approach behavior from both sides. That is, how do we make our AI more intelligent... and, how do we make our AI less accurate? Kind of an odd position to be in, isn't it?
Labels: agent based AI, Behavioral Mathematics for Game AI, errors, mistakes
posted by Dave Mark | (Permalink)
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