Case-Based Goal Selection Inspired by IBM's Watson

IBM’s Watson uses a variety of scoring algorithms to rank candidate answers for natural language questions. These scoring algorithms played a crucial role in Watson’s win against human champions in Jeopardy!. We show that this same technique can be implemented within a real-time strategy (RTS) game playing goal-driven autonomy (GDA) agent. Previous GDA agents in RTS games were forced to use very compact state representations. Watson’s scoring algorithms technique removes this restriction for goal selection, allowing the use of all information available in the game state. Unfortunately, there is a high knowledge engineering effort required to create new scoring algorithms. We alleviate this burden using case-based reasoning to approximate past observations of a scoring algorithm system. Our experiments in a realtime strategy game show that goal selection by the CBR system attains comparable in-game performance to a baseline scoring algorithm system.