Action selection and task sequence learning for hybrid dynamical cognitive agents

作者：Aaron Eric^*; Admoni Henny

来源：Robotics and Autonomous Systems, 2010, 58(9): 1049-1056.

DOI：10.1016/j.robot.2010.05.006

• Hybrid reactive-deliberative agents
• Dynamical intention
• Hybrid dynamical systems
• BDI modeling
• Learning

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief-desire-intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods-such as Hebbian strengthening of links between co-active elements, and belief-intention learning of task-specific relationships-modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.

• 出版日期2010-9-30