A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Plan Caching

TL;DR

This paper introduces a hybrid agent architecture combining POMDP and BDI models, enabling online stochastic planning, multiple goal management, and plan reuse for autonomous agents in uncertain environments.

Contribution

It presents a novel hybrid architecture integrating POMDP and BDI features, including plan caching and multi-goal pursuit, with empirical evaluation of its feasibility.

Findings

The architecture supports simultaneous goal pursuit.

Plan caching improves planning efficiency.

Simulation results confirm approach feasibility.

Abstract

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.