<sc>ghost</sc>: A Combinatorial Optimization Framework for Real-Time Problems.

This paper presents <sc>GHOST</sc>, a combinatorial optimization framework that a real-time strategy (RTS) AI developer can use to model and solve any problem encoded as a constraint satisfaction/optimization problem (CSP/COP). We show a way to model three different problems as a CSP/COP, using instances from the RTS game StarCraft as test beds. Each problem belongs to a specific level of abstraction (the target selection as reactive control problem, the wall-in as a tactics problem, and the build order planning as a strategy problem). In our experiments, <sc>GHOST</sc> shows good results computed within some tens of milliseconds. We also show that <sc>GHOST </sc> outperforms state-of-the-art constraint solvers, matching them on the resources allocation problem, a common combinatorial optimization problem. [ABSTRACT FROM PUBLISHER]