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AAAI
2012

Learning from Demonstration for Goal-Driven Autonomy

7 years 21 days ago
Learning from Demonstration for Goal-Driven Autonomy
Goal-driven autonomy (GDA) is a conceptual model for creating an autonomous agent that monitors a set of expectations during plan execution, detects when discrepancies occur, builds explanations for the cause of failures, and formulates new goals to pursue when planning failures arise. While this framework enables the development of agents that can operate in complex and dynamic environments, implementing the logic for each of the subtasks in the model requires substantial domain engineering. We present a method using case-based reasoning and intent recognition in order to build GDA agents that learn from demonstrations. Our approach reduces the amount of domain engineering necessary to implement GDA agents and learns expectations, explanations, and goals from expert demonstrations. We have applied this approach to build an agent for the real-time strategy game StarCraft. Our results show that integrating the GDA conceptual model into the agent greatly improves its win rate.
Ben George Weber, Michael Mateas, Arnav Jhala
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Ben George Weber, Michael Mateas, Arnav Jhala
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