This paper provides a general mechanism and a solid theoretical basis for performing planning within Belief-Desire-Intention (BDI) agents. BDI agent systems have emerged as one of the most widely used approaches to implementing intelligent behaviour in complex dynamic domains, in addition to which they have a strong theoretical background. However, these systems either do not include any built-in capacity for "lookahead" type of planning or they do it only at the implementation level without any precise defined semantics. In some situations, the ability to plan ahead is clearly desirable or even mandatory for ensuring success. Also, a precise definition of how planning can be integrated into a BDI system is highly desirable. By building on the underlying similarities between BDI systems and Hierarchical Task Network (HTN) planners, we present a formal semantics for a BDI agent programming language which cleanly incorporates HTN-style planning as a built-in feature. We argue that the resulting integrated agent programming language combines the advantages of both BDI agent systems and hierarchical offline planners.

Agent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work.