Teaching machines in the way that animal trainers mold the behavior of dogs or horses has been an important method for developing artificial intelligence and one that was recognized Wednesday with the top computer science award.

Two pioneers in the field of reinforcement learning, Andrew Barto and Richard Sutton, are the winners of this year’s A.M. Turing Award, the tech world’s equivalent of the Nobel Prize.

Research that Barto, 76, and Sutton, 67, began in the late 1970s paved the way for some of the past decade’s AI breakthroughs. At the heart of their work was channeling so-called hedonistic machines that could continuously adapt their behavior in response to positive signals.

Reinforcement learning is what led a Google computer program to beat the world’s best human players of the ancient Chinese board game Go in 2016 and 2017. It’s also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a Rubik’s Cube.

But Barto said the field was “not fashionable” when he and his doctoral student, Sutton, began crafting their theories and algorithms at the University of Massachusetts, Amherst.

“We were kind of in the wilderness,” Barto told The Associated Press. “Which is why it’s so gratifying to receive this award, to see this becoming more recognized as something relevant and interesting. In the early days, it was not.”

Google sponsors the annual $1 million prize, which was announced Wednesday by the Association for Computing Machinery.

Barto, now retired from the University of Massachusetts, and Sutton, a longtime professor at Canada’s University of Alberta, aren’t the first AI pioneers to win the award named after British mathematician, codebreaker and early AI thinker Alan Turing. But their research has directly sought to answer Turing’s 1947 call for a machine that “can learn from experience” — which Sutton describes as “arguably the essential idea of reinforcement learning.”