Towards Case-Based Reasoning in Real-Time Strategy Environments with SEASALT

Jakob Michael Schönborn, Klaus-Dieter Althoff

In: Daniel Trabold, Pascal Welke, Nico Piatkowski (editor). Lernen, Wissen, Daten, Analysen. GI-Workshop-Tage "Lernen, Wissen, Daten, Analysen" (LWDA-2020) September 9-11 Online Pages 154-161 CEUR 2020.


Real-time situations provide numerous different problems to solve. Starting with the requirement of finding a solution inside an acceptable time frame, the problem is to find the right balance between performance and precision of the system. One might not be able to wait multiple minutes for a solution, however, a too quickly made decision might entail a certain risk factor. Thus, it has to be decided when methods such as using a rule-based system is sufficient and when it is rather beneficial to take the cost of using methodologies for knowledge management. We are using StarCraft II as an example for decision making in a real-time environment using incomplete information with a finite set of buildings and units to control. We propose using agents to decide the proportion of command authority between using a rule-based and a case-based reasoning agent. Earlier stages of the game seem to be promising for immediate reactions while later stages of the game require more planning due to the increased rate of information, which have to be processed. By reusing past experiences, case-based reasoning may be able to help improving the planning process.

Weitere Links

LWDA2020_paper_17.pdf (pdf, 7 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz