<div align="justify">Scientists, including one of Indian origin, have developed a new artificial intelligence (AI) system that can achieve the maximum possible score of 999,990 on the popular video game Pac-Man.<br /><br />The team from Canadian startup Maluuba used a branch of AI called reinforcement learning to play the 1980s arcade game Ms Pac-Man perfectly.<br /><br />Doina Precup, an associate professor at McGill University in Canada said that is a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Pac-Man among the most difficult to crack.<br /><br />To get the high score, the team divided the large problem of mastering Pac-Man into small pieces, which they then distributed among AI agents. This divide-and-conquer method could have broad implications for teaching AI agents to do complex tasks that augment human capabilities.<br /><br />The method is similar to some theories of how the brain works, and it could have broad implications for teaching AIs to do complex tasks with limited information.<br /><br />The method, which the Maluuba team calls Hybrid Reward Architecture, used more than 150 agents, each of which worked in parallel with the other agents to master Pac-Man.<br /><br />For example, some agents got rewarded for successfully finding one specific pellet, while others were tasked with staying out of the way of ghosts.<br /><br />Then, the researchers created a top agent - sort of like a senior manager at a company - who took suggestions from all the agents and used them to decide where to move Pac-Man. The top agent took into account how many agents advocated for going in a certain direction, but it also looked at the intensity with which they wanted to make that move.<br /><br />For example, if 100 agents wanted to go right because that was the best path to their pellet, but three wanted to go left because there was a deadly ghost to the right, it would give more weight to the ones who had noticed the ghost and go left.<br /><br />Figuring out how to win these types of videogames is actually quite complex, because of the huge variety of situations you can encounter while playing the game, said Rahul Mehrotra, a program manager at Maluuba, which was aqcuired by Microsoft earlier this year.<br /><br />"A lot of companies working on AI use games to build intelligent algorithms because there's a lot of human-like intelligence capabilities that you need to beat the games," Mehrotra said.<br /></div>
<div align="justify">Scientists, including one of Indian origin, have developed a new artificial intelligence (AI) system that can achieve the maximum possible score of 999,990 on the popular video game Pac-Man.<br /><br />The team from Canadian startup Maluuba used a branch of AI called reinforcement learning to play the 1980s arcade game Ms Pac-Man perfectly.<br /><br />Doina Precup, an associate professor at McGill University in Canada said that is a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Pac-Man among the most difficult to crack.<br /><br />To get the high score, the team divided the large problem of mastering Pac-Man into small pieces, which they then distributed among AI agents. This divide-and-conquer method could have broad implications for teaching AI agents to do complex tasks that augment human capabilities.<br /><br />The method is similar to some theories of how the brain works, and it could have broad implications for teaching AIs to do complex tasks with limited information.<br /><br />The method, which the Maluuba team calls Hybrid Reward Architecture, used more than 150 agents, each of which worked in parallel with the other agents to master Pac-Man.<br /><br />For example, some agents got rewarded for successfully finding one specific pellet, while others were tasked with staying out of the way of ghosts.<br /><br />Then, the researchers created a top agent - sort of like a senior manager at a company - who took suggestions from all the agents and used them to decide where to move Pac-Man. The top agent took into account how many agents advocated for going in a certain direction, but it also looked at the intensity with which they wanted to make that move.<br /><br />For example, if 100 agents wanted to go right because that was the best path to their pellet, but three wanted to go left because there was a deadly ghost to the right, it would give more weight to the ones who had noticed the ghost and go left.<br /><br />Figuring out how to win these types of videogames is actually quite complex, because of the huge variety of situations you can encounter while playing the game, said Rahul Mehrotra, a program manager at Maluuba, which was aqcuired by Microsoft earlier this year.<br /><br />"A lot of companies working on AI use games to build intelligent algorithms because there's a lot of human-like intelligence capabilities that you need to beat the games," Mehrotra said.<br /></div>