Demonstrations of Dynamical Intention for Hybrid Agents

Abstract

Representations of intention shared by reactive and deliberative systems of hybrid agents enable seamless integration of high-level logical reasoning and low-level behavioral response. This thesis presents an architecture for hybrid dynamical cognitive agents (HDCAs), hybrid reactive/deliberative agents with cognitive systems of continuously evolving beliefs, desires, and intentions based on BDI and spreading activation network models. Dynamical intentions support goal-directed behavior in both reactive and deliberative systems of HDCAs: on the reactive level, dynamical intentions allow for continuous cognitive evolution and real-time task re-sequencing; on the deliberative level,
dynamical intentions enable logical reasoning and plan generation. Because intention representations are shared between both systems, reactive behavior and goal-directed deliberation are straightforwardly integrated in HDCAs. Additionally, Hebbian learning on connections in the spreading activation network of beliefs, desires, and intentions trains HDCAs’ reactive systems to respond to typically deliberative-level information. To establish comparability between HDCAs and traditional BDI-based architectures, dynamical intentions are shown to be consistent with the philosophical definition of intention from other BDI models. Simulations of autonomous, embodied HDCAs navigating to complete tasks in a grid city environment illustrate dynamical, intention-based behavior that derives from clean integration of reactive and deliberative systems.