. . "I" . . . . "Predicting Opponent\u2019s Production in Real-Time Strategy Games with Answer Set Programming" . . . "The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent\u2019s actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player\u2019s partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 minutes seems to be surprisingly high, making the method useful in practice. Rootmean- square error grows only slowly with increasing prediction intervals \u2013 almost in a linear fashion."@en . "\u010Certick\u00FD, Michal" . "The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent\u2019s actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player\u2019s partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 minutes seems to be surprisingly high, making the method useful in practice. Rootmean- square error grows only slowly with increasing prediction intervals \u2013 almost in a linear fashion." . "21230" . . . "RIV/68407700:21230/14:00227811" . "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6936913" . "IEEE Transactions on Computational Intelligence and AI in Games" . "Stanescu, M." . "FR - Francouzsk\u00E1 republika" . . "RIV/68407700:21230/14:00227811!RIV15-MSM-21230___" . "10.1109/TCIAIG.2014.2365414" . "6"^^ . "Predicting Opponent\u2019s Production in Real-Time Strategy Games with Answer Set Programming"@en . "1"^^ . "1943-068X" . . . "Predicting Opponent\u2019s Production in Real-Time Strategy Games with Answer Set Programming" . "PP" . "2"^^ . . . . . "38736" . "Real-Time Strategy; Answer Set Programming; Prediction; StarCraft; WarCraft; Opponent Modelling; RTS"@en . "99" . . "Predicting Opponent\u2019s Production in Real-Time Strategy Games with Answer Set Programming"@en . "[541F16C54E43]" . .