Taking turns, each player must either move one of their playing pieces or attack one of their enemy’s playing pieces. Remove the screen once both players are ready, but beware, there is no luck in this game, and how you place your pieces on the board can determine whether you win or lose. Secretly deploy your army by placing the 40 playing pieces on the first four rows of the game board. Place the screen in the center of the game board. Luckily, you are not alone you have an entire army at your disposal, along with a seductive spy and six deadly bombs. The aim of this classic board game is to capture the enemy’s flag while defending your own. Are you ready to help the Lieutenant defeat the Redcoat army? The Bluecoats are counting on a brilliant strategist like you. The Bluecoat army is weary and has been significantly weakened, but his men will not surrender. Time and again, Lieutenant Jacques Cavalier attempts to attack and capture the Redcoats’ flag with his troops. The DeepNash game theory approach could prove useful in non-game situations where AIs must deal with other intelligent actors, such as in business and defence, says Tuomas Sandholm at Carnegie Mellon University in Pennsylvania.War between the Redcoats and Bluecoats rages on. “DeepNash does both well – likely with a competitive advantage with regards to memory – and plays in interesting and unpredictable manners, showcasing elements of bluffing.” “Good players tend to memorise the opponent’s pieces and predict their deployment patterns,” says Georgios Yannakakis at the University of Malta. The DeepMind AI also notched a 97 per cent win rate against top Stratego-playing bots, including several that had previously won the Computer Stratego World Championship. It achieved an 84 per cent win rate during 50 ranked matches against expert human players through an online games platform and became one of the top three players – without human opponents realising they were playing an AI. “This is a new thing that we couldn’t really do before,” says Julian Togelius at New York University.ĭeepNash has already dominated both human and AI adversaries. The result is an AI capable of making winning decisions despite hidden information about its opponents, a huge number of possible game states and many different possible actions that can be taken during each turn. The optimal strategy is one that would guarantee at least a 50 per cent win rate against a perfect opponent, even if the opponent knew exactly what the AI planned to do. Instead of trying to play by searching all the possible game scenarios, which would be computationally impossible, the DeepNash AI has an algorithm that continually steers its behaviour toward an optimal strategy informed by economic game theory, says Karl Tuyls at DeepMind. Nor did it train to play against specific opponents. But the AI didn’t rely on any knowledge of human strategies specific to the game, as was the case for DeepMind’s StarCraft-playing AI. Perolat and his colleagues at DeepMind developed their “DeepNash” AI to conquer Stratego by playing itself over the course of 5.5 billion games with a simulation training time roughly equivalent to hundreds of years. By comparison, the game of Go has 10 360 possible game states. But players cannot see the identities of opponent game pieces unless two pieces from opposing armies encounter one another – unlike games such as chess or Go where both players can see everything.Ĭomplicating this challenge is the fact that Stratego is an enormously complex game with 10 535 possible game situations. Most pieces consist of soldiers numbered from one to 10, with the higher-ranked soldiers defeating lower-ranked soldiers during encounters on the board. The game of Stratego involves two players trying to capture the opponent’s flag hidden among an array of 40 game pieces.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |