Game AI for a Turn-based Strategy Game with Plan Adaptation and Ontology-based Retrieval
In this paper we present a novel approach for developing adaptive game AI by combining case based planning techniques and ontological knowledge from the game environment. The proposed architecture combines several components: a case-based hierarchical planner (Repair-SHOP), a bridge to connect and reason with Ontologies formalized in Description Logics (DLs) based languages (OntoBridge), a DLs reasoner (Pellet) and a framework to develop Case-Based Reasoning (CBR) systems (jCOLIBRI ). In our ongoing work we are applying this approach to a commercial Civilization clone turn-based strategy game (CTP2) where game AI is in charge of planning the strategies for automated players. Our goal is to demonstrate that ontology-based retrieval will result in the retrieval of strategies that are easier to adapt than those plans returned by other classical retrieval mechanisms traditionally used in case-based planning.