Cross-paradigm analysis of autonomous agent architecture

Autonomous agent architectures are design methodologies—collections of knowledge and strategies which are applied to the problem of creating situated intelligence. This article attempts to integrate this knowledge across several architectural traditions, paying particular attention to features which have tended to be selected under the pressure of extensive use in real-world systems. We determine that the following strategies provide significant assistance in the design of autonomous intelligent agents: (i) modularity, which simplifies both design and control; (ii) hierarchically organized action selection, which focusses attention and provides prioritization when different modules conflict; and (iii) parallel environment monitoring which allows a system to be responsive and opportunistic by allowing attention to shift and priorities to be re-evaluated. We offer a review of four architectural paradigms: behaviour-based AI; two- and three-layered systems; belief, desire and intention architectures (particularly PRS); and Soar/ACT-R. By documenting trends within each of these communities towards establishing the components above, we argue that this convergent evolution is strong evidence for the components' utility. We then use this information to recommend specific strategies for researchers working under each paradigm to further exploit the knowledge and experience of the field as a whole.