The Real-Time Strategy Game Multi-Objective Build Order Problem

In this paper we examine the build order problem in real-time strategy (RTS) games in which the objective is to optimize execution of a strategy by scheduling actions with respect to a set of subgoals. We model the build order problem as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA). A three dimensional solution space is presented providing a depiction of a Pareto front for the build order MOP. Results of the online strategic planning tool are provided which demonstrate that our planner out-performs an expert scripted player. This is demonstrated for an AI agent in the Spring Engine Balanced Annihilation RTS game.

[1]  Philippe Baptiste.  Disjunctive constraints for manufacturing scheduling: Principles and extensions , 1996 .

[2]  Michael Mateas,et al.  An Integrated Agent for Playing Real-Time Strategy Games , 2008, AAAI.

[3]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[4]  Luiz Chaimowicz,et al.  An Artificial Intelligence System to Help the Player of Real-Time Strategy Games , 2010, 2010 Brazilian Symposium on Games and Digital Entertainment.

[5]  Alan Fern,et al.  Extending Online Planning for Resource Production in Real-Time Strategy Games with Search , 2007 .

[6]  Helmut Simonis,et al.  Modelling Producer/Consumer Constraints , 1995, CP.

[7]  P. Baptiste,et al.  Edge-Finding Constraint Propagation Algorithms for Disjunctive and Cumulative Scheduling , 1996 .

[8]  Michael Buro,et al.  Adversarial Planning Through Strategy Simulation , 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

[9]  Di Trapani,et al.  A Real-time Strategy Agent Framework and Strategy Classifier for Computer Generated Forces , 2012 .

[10]  Michael Buro,et al.  Incorporating Search Algorithms into RTS Game Agents , 2012 .

[11]  Arnav Jhala,et al.  Using Data Mining to Model Player Experience , 2011 .

[12]  Michael Buro,et al.  Build Order Optimization in StarCraft , 2011, AIIDE.

[13]  Arnav Jhala,et al.  Reactive planning idioms for multi-scale game AI , 2010, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games.

[14]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[15]  Michael Mateas,et al.  A data mining approach to strategy prediction , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[16]  Ben George Weber,et al.  Integrating Learning in a Multi-Scale Agent , 2012 .

[17]  Jason M Blackford,et al.  Online Build-Order Optimization for Real-Time Strategy Agents using Multi-Objective Evolutionary Algorithms , 2014 .

[18]  Santiago Ontañón,et al.  Case-Based Planning and Execution for Real-Time Strategy Games , 2007, ICCBR.