AI_Research - beyond-all-reason/springrts_engine_wiki_mirror GitHub Wiki
Here is a list of articles and case studies, most compiled by ex neuron machina..
- Seminar course on "Topics on AI and Computer Game Programming": The lecture slides give a great overview of advances in game AI, with plenty on RTS games
- Forbus et al 2001, "How qualitative spatial reasoning can improve strategy game AIs"
- D. Fetter (project report), "Real-Time Strategy Game Decision Making with Support Vector Machines": A project which tried using SVMs to learn unit production decisions in Warcraft III, based on replays of top players.
- R. Laursen & D. Nielsen 2005, "Investigating small scale combat situations in real time strategy computer games": 164-page Master's thesis on using game trees to determine near-optimal unit behaviors in combat scenarios (unit placement and target selection), which was implemented in the Stratagus warcraft clone. This also gives an overview of the AI techniques used by many popular games.
- N. Imrei, "Reinforcement Learning in Real-Time Strategy Games" ([http://www.csse.monash.edu.au/hons/projects/2004/Nicolas.Imrei/Downloads/FinalPresentation.ppt>final presentation link], thesis available by email): Honours project used reinforcement learning to develop both overall strategies and agent behaviors in RTS games
- S. de Jong, P. Spronck & N. Roos 2005, "Requirements for resource management game AI": This is about how to do resource management in games like Sim City, but it should also be applicable to TA-style games. There's also a 77-page Master's thesis
- Michael Buro's lab: Much of his lab's research is focused on "real-time planning and learning AI with applications to RTS games." They've actually created an open-source RTS called ORTS, in order to experiment with various AI techniques. With any luck, they might even be persuaded to switch their focus to TA Spring, or perhaps try implementing some of their algorithms. Also, it might be worthwhile to use elements of ORTS in TA Spring.
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- M. Chung, M. Buro, and J. Schaeffer 2005, "Monte Carlo Planning in RTS Games"
- M. Buro 2004, "Call for AI Research in RTS Games": Gives a nice overview of the current state of AIs in RTS games
- M. Buro and T. Furtak, "RTS Games and Real-Time AI Research"
- L. MacDonald 2005, a final-project report on Moving Target Search (MTS). Basically, MTS seems to be an extension of something called Learning Real-Time A* (LRTA*).
- N. Mehta et al 2005, "Transfer in Variable-Reward Hierarchical Reinforcement Learning": Develops reinforcement-learning techniques and tests them in a simple RTS