Google Teaches Computers "Regret"

mikejuk writes "Google is funding an AI project that will introduce the technical concept of regret into programs — but there's a big difference between regret and being sorry. In fact regret is just the difference between maximum possible reward and the actual reward received and the project is about optimization. There are two things to learn from this situation. The first is that just because some numerical measure is called 'regret' it doesn't mean it has anything to do with the common use of the term. Secondly if you are going to invent an AI technique then picking emotive words for your jargon is a good way to ensure publicity."

Re:Regret is a standard term in economics

[retchdog]

here i am feeding the troll, but the best AI for backgammon is trained by regret-based reinforcement learning (it's needed since the dice rolls blow up the search space for standard perfect-information strategies): http://www.research.ibm.com/massive/tdl.html [ibm.com]. in this case the regret-function is unknown and is stochastically approximated ("learned") by repeated play.

it's notable that unlike chess AI which is considered effective but unnatural, this backgammon AI is considered to play mostly "like a human" and its play has actually inspired new strategies for human backgammon players.

regret-based methods are typically heuristic, and i'd call them much less "autistic" than, say, infinitely-rational nash agents or game tree pruners.

Re:Regret is a standard term in economics

[Daniel Dvorkin]

Furthermore, the emotion we identify as "regret" seems to me to line up neatly with the economic definition. I defy anyone to prove that when we feel regret, our brains are doing anything other than comparing the reward we received for a particular action with the maximum reward we think we could have received for a different action. TFA is more or less playing the Chinese Room game: "I assert that computers can't do X, because computers can't do X, because I assert that computers can't do X."