2 research outputs found
Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence
Currently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.Ram, Ashwin - Faculty Mentor ; Ontañón, Santi - Committee Member/Second Reade
A Bayesian Model for Plan Recognition in RTS Games applied to StarCraft
The task of keyhole (unobtrusive) plan recognition is central to adaptive
game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS)
game strategic (long term) planning. This paper presents a generic and simple
Bayesian model for RTS build tree prediction from noisy observations, which
parameters are learned from replays (game logs). This unsupervised machine
learning approach involves minimal work for the game developers as it leverage
players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it
yields high quality and robust predictions, that can feed an adaptive AI.Comment: 7 pages; Artificial Intelligence and Interactive Digital
Entertainment Conference (AIIDE 2011), Palo Alto : \'Etats-Unis (2011