Action selection and task sequence learning for hybrid dynamical cognitive agents
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 beliefdesireintention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methodssuch as Hebbian strengthening of links between co-active elements, and beliefintention learning of task-specific relationshipsmodify 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.
learning dynamical intention hybrid dynamical systems hybrid reactivedeliberative agents bdi modeling