Reactive Planning for RTS games

The paper “Reactive Planning Idioms for Multi-Scale Game AI” (Weber et al.), published last year in the proceedings of the IEEE Conference on Computation Intelligence and Games (CIG 2010), proposes a technique called reactive planning for designing a bot for Real-time strategy games (RTS). The agent is implemented in ABL (a behavioral language), an environment that allows the programmer to embed the multi-level reasoning that is required for efficient and complex RTS bots. A bot for RTS games (such as the StarCraft) must deal simultaneously with several goals, making intelligent high-level decisions while micromanaging units in combat, and ABL provides features such as daemons, messaging (memory), managers and micromanagement behaviors that can be of great help for such task. The authors propose a specific framework, for the structure of the bot and interfaces, and demonstrate that the resulting agent is able to beat the built-in StarCraft bot. However, when tested against moderately skilled human players, the agent performs poorly. As far as we understood, this work deals mainly with traditional Artificial Intelligence. The open question now is: can we model some kind of adaptive behavior in this ABL environment?

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About cfernandes81

Carlos M. Fernandes was born in Luanda in 1973 and lives in between Lisbon, Portugal, and Granada, Spain. He graduated (Technical University of Lisbon, 1998) in Electrotechnics Engineering and owns a master degree in the same field since 2002 (Technical University of Lisbon). He is currently pursuing a Ph.d. on Bio-inspired Computing. From 2001 to 2005 he was an assistant at Instituto Politécnico de Setúbal. (He is also a photographer and photography teacher.) Bio-inspired Computing is his major field of research: Genetic Algorithms, Estimation of Distribution Algorithms, Ant Colony Optimization, Particle Swarm Optimization and other metaheuristics. He is particularly interested in the hybridization of Bio-inspired Computing techniques with Self-Organization, Self-Organized Criticality Models and diversity maintenance strategies. In the present, Dynamic Optimization Problems are his mains target for applying such techniques. website: email:

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