AI researchers love playing games

I was catching up today on a couple of new-ish developments in reinforcement learning/game-playing AI models.

Meta (which, we always need to note, is the parent company of Facebook) apparently has an entire team of researchers devoted to training an AI system to play Diplomacy, a war-strategy board game. Unlike in chess or Go, a player in Diplomacy must collaborate with others to succeed. Meta’s program, named Cicero, has passed the bar, as explained in a Gizmodo article from November 2022.

“Players are constantly interacting with each other and each round begins with a series of pre-round negotiations. Crucially, Diplomacy players may attempt to deceive others and may also think the AI is lying. Researchers said Diplomacy is particularly challenging because it requires building trust with others, ‘in an environment that encourages players to not trust anyone,’” according to the article.

We can see the implications for collaborations between humans and AI outside of playing games — but I’m not in love with the idea that the researchers are helping Cicero learn how to gain trust while intentionally working to deceive humans. Of course, Cicero incorporates a large language model (R2C2, further trained on the WebDiplomacy dataset) for NLP tasks; see figures 2 and 3 in the Science article linked below. “Each message in the dialogue training dataset was annotated” to indicate its intent; the dataset contained “12,901,662 messages exchanged between players.”

Cicero was not identified as an AI construct while playing in online games with unsuspecting humans. It “apparently ‘passed as a human player,’ in 40 games of Diplomacy with 82 unique players.” It “ranked in the top 10% of players who played more than one game.”

See also: Human-level play in the game of Diplomacy by combining language models with strategic reasoning (Science, 2022).

Meanwhile, DeepMind was busy conquering another strategy board game, Stratego, with a new AI model named DeepNash. Unlike Diplomacy, Stratego is a two-player game, and unlike chess and Go, the value of each of your opponent’s pieces is unknown to you — you see where each piece is, but its identifying symbol faces away from you, like cards held close to the vest. DeepNash was trained on self-play (5.5 billion games) and does not search the game tree. Playing against humans online, it ascended to the rank of third among all Stratego players on the platform — after 50 matches.

Apparently the key to winning at Stratego is finding a Nash equilibrium, which I read about at Investopedia, which says: “There is not a specific formula to calculate Nash equilibrium. It can be determined by modeling out different scenarios within a given game to determine the payoff of each strategy and which would be the optimal strategy to choose.”

See: Mastering the game of Stratego with model-free multiagent reinforcement learning (Science, 2022).

See more posts about games at this site.

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What is a stop sign? What is a person?

I was reading an article in Scientific American, found by one of my students, and I came across this passage:

“A Tesla on autopilot recently drove directly toward a human worker carrying a stop sign in the middle of the road, slowing down only when the human driver intervened. The system could recognize humans on their own (which is how they appeared in the training data) and stop signs in their usual locations (as they appeared in the training images) but failed to slow down when confronted by the unfamiliar combination of the two, which put the stop sign in a new and unusual position.”

—Artificial General Intelligence Is Not as Imminent as You Might Think, July 1, 2022 (boldface mine)

The article helpfully linked to a YouTube video, in which we see and hear the situation. The driver is narrating as the car makes its decisions: “All right, we’re having to take over. It’s not slowing early enough. [Pause.] Yep, the car keeps trying to go each time … That’s really unfortunate. It sees the person, it sees the stop sign, but it’s almost not taking it seriously.”

View through a car's windshield shows a person holding a stop sign, standing in the middle of a road
Capture from the YouTube video at 03:14

This is not a big surprise if you understand the nature of training data and the long tail — a person walking across a street is common enough, and a stop sign is very common, but a person holding a stop sign and standing (not walking) in the middle of the lane occurs much less frequently than the other two. It’s not rare, but it’s not something we encounter every day while driving.

Here’s the thing: Later in the article, the author says: “You can’t deal with a person carrying a stop sign if you don’t really understand what a stop sign even is.” And at first, I’m like: Cool, cool. That’s good, that’s a nice observation.

And then I thought: Wait a minute. Wait just a minute. Of course an AI system understands nothing, nothing at all. It has been trained to recognize a stop sign. It has been trained to recognize a human (especially a human in the road). But what is really happening in the video? The car is stopping, briefly, and then starting up again. It does this more than once. The driver has to intervene, put a foot on the brake, to stop the car from going forward and hitting the person. The car is behaving the way it was programmed to behave at a stop sign — and not the way it was programmed to behave if a human is walking in front of the car.

The central point here is not that the car’s system doesn’t know what a stop sign is (which, it’s true, it doesn’t). The central point is that given a human holding a stop sign, the system behavior governing a regular, side-of-the-road stop sign has dominated, has come to the fore as the default behavior — and the system behavior that prevents a human from being run over is not in play.

This is no trolley problem. The car’s AI did not decide to kill the human. It did not weigh the options. In an unlikely case (an edge case), it defaulted to the common, everyday case: There is a stop sign. This is what I do when there is a stop sign.

I’m blogging this because this is great discussion material for students and others!

Creative Commons License
AI in Media and Society by Mindy McAdams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Include the author’s name (Mindy McAdams) and a link to the original post in any reuse of this content.

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