Analogical learning in a turn-based strategy game

Research output: Contribution to journalConference article

20 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)853-858
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - Dec 1 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

ASJC Scopus subject areas

  • Artificial Intelligence

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