Analogical Learning in a Turn-Based Strategy Game
Thomas R. Hinrichs, Kenneth D. Forbus
A key problem in playing strategy games is learning how to allocate resources effectively. This can be a difficult task for machine learning when the connections between actions and goal outputs are indirect and complex. We show how a combination of structural analogy, experimentation, and qualitative modeling can be used to improve performance in optimizing food production in a strategy game. Experimentation bootstraps a case library and drives variation, while analogical reasoning supports retrieval and transfer. A qualitative model serves as a partial domain theory to support adaptation and credit assignment. Together, these techniques can enable a system to learn the effects of its actions, the ranges of quantities, and to apply training in one city to other, structurally different cities. We describe experiments demonstrating this transfer of learning.