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lythrum comments on Secrets of the eliminati - Less Wrong
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I wonder:
if you had an agent that obviously did have goals (let's say, a player in a game, whose goal is to win, and who plays the optimal strategy) could you deduce those goals from behavior alone?
Let's say you're studying the game of Connect Four, but you have no idea what constitutes "winning" or "losing." You watch enough games that you can map out a game tree. In state X of the world, a player chooses option A over other possible options, and so on. From that game tree, can you deduce that the goal of the game was to get four pieces in a row?
I don't know the answer to this question. But it seems important. If it's possible to identify, given a set of behaviors, what goal they're aimed at, then we can test behaviors (human, animal, algorithmic) for hidden goals. If it's not possible, that's very important as well; because that means that even in a simple game, where we know by construction that the players are "rational" goal-maximizing agents, we can't detect what their goals are from their behavior.
That would mean that behaviors that "seem" goal-less, programs that have no line of code representing a goal, may in fact be behaving in a way that corresponds to maximizing the likelihood of some event; we just can't deduce what that "goal" is. In other words, it's not as simple as saying "That program doesn't have a line of code representing a goal." Its behavior may encode a goal indirectly. Detecting such goals seems like a problem we would really want to solve.
If you had lots of end states, and lots of non-end states, and we want to assume the game ends when someone's won, and that a player only moves into an end state if he's won (neither of these last two are necessarily true even in nice pretty games), then you could treat it like a classification problem. In that case, you could throw your favourite classifier learning algorithm at it. I can't think of any publications on someone machine learning a winning condition, but that doesn't mean it's not out there.
Dr. David Silver used temporal difference learning to learn some important spatial patterns for Go play, using self-play. Self play is basically like watching yourself play lots of games with another copy of yourself, so I can imagine similar ideas being used to watching someone else play. If you're interested in that, I suggest http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-170.pdf
On a sadly less published (and therefore mostly unreliable) but slightly more related note, we did have a project once in which we were trying to teach bots to play a Mortal Kombat style game only by observing logs of human play. We didn't tell one of the bots the goal, we just told it when someone had won, and who had won. It seemed to get along ok.