Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-44 |
| Number of pages | 44 |
| Journal | The Knowledge Engineering Review |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2015 |
Bibliographical note
Published online by Cambridge University Press: 04 September 2013Copyright © Cambridge University Press 2013
Acknowledgements
We would like to thank Michael Luck for valuable input and discussions throughout the process of writing this paper, and Lin Padgham, Sebastian Sardiña, and Michael Luck for supervising our respective PhD theses, which formed the basis for this paper. We would also like to thank Félix Ingrand, Malik Ghallab, and Wamberto Vasconcelos for valuable discussions in the course of writing this paper in its current form. We are grateful to the anonymous reviewers for providing detailed feedback, which has helped improve this paper substantially. Finally, we thank the funding agencies that sponsored our respective PhDs: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (under grant 2315/04-1) for Felipe and the Australian Research Council (under grant LP0882234) for Lavindra.