  This article presents an agent architecture for controlling an autonomous
agent in stochastic environments. The architecture combines the partially
observable Markov decision process (POMDP) model with the
belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent
architecture takes the best features from the two approaches, that is, the
online generation of reward-maximizing courses of action from POMDP theory, and
sophisticated multiple goal management from BDI theory. We introduce the
advances made since the introduction of the basic architecture, including (i)
the ability to pursue multiple goals simultaneously and (ii) a plan library for
storing pre-written plans and for storing recently generated plans for future
reuse. A version of the architecture without the plan library is implemented
and is evaluated using simulations. The results of the simulation experiments
indicate that the approach is feasible.
