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Planning for resource production in real-time strategy games based on partial order planning, search and learning

Generally, a real-time strategy game is characterized by two stages. Initially, it is necessary to collect and produce resources. The next step is related to battles, taking into account the resources that were collected. The resources production stage is a key factor for winning the game. In this study the authors propose a mechanism for producing resources based on planning, supported by artificial intelligence using means-end analysis and scheduling. Emphasis is given to scheduling that uses an algorithm of real-time search and learning. The results show that the proposed system presents a better performance compared to related approaches.