TitleIntegrated learning for goal-driven autonomy
Publication TypeConference Paper
Year of Publication2011
AuthorsJaidee, U, Muñoz-Avila, H, Aha, DW
Conference NameProceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
Date Published07/2011
PublisherAAAI Press
Conference LocationBarcelona, Spain
ISBN Number978-1-57735-515-1

Goal-driven autonomy (GDA) is a reflective model of goal reasoning that controls the focus of an agent's planning activities by dynamically resolving unexpected discrepancies in the world state, which frequently arise when solving tasks in complex environments. GDA agents have performed well on such tasks by integrating methods for discrepancy recognition, explanation, goal formulation, and goal management. However, they require substantial domain knowledge, including what constitutes a discrepancy and how to resolve it. We introduce LGDA, a learning algorithm for acquiring this knowledge, modeled as cases, that and integrates case-based reasoning and reinforcement learning methods. We assess its utility on tasks from a complex video game environment. We claim that, for these tasks, LGDA can significantly outperform its ablations. Our evaluation provides evidence to support this claim. LGDA exemplifies a feasible design methodology for deployable GDA agents.

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Goal reasoning
goal-driven autonomy
case-based reinforcement learning