Abstract
In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal.
The original version of this chapter has been revised: In an older version Fig. 6 was represented incorrectly. An erratum to this chapter is available at 10.1007/978-3-319-45856-4_27
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-45856-4_27
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Notes
- 1.
Neighbouring PoIs are those which can be reached from the current agent position without getting through any other PoI.
- 2.
Undesired outcomes are considered as terminal states: if an unexpected situation is encountered, the remaining agents start another planning process upon the resulting environment state.
- 3.
In the country park scenario, primitive actions have at most one non-terminal outcome, but this could not be the case in other different scenarios with multiple stochastic action outcomes.
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Acknowledgments
This work has been funded by EPSRC PACES project (Ref: EP/J012149/1).
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Palomares, I., Bauters, K., Liu, W., Hong, J. (2016). A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_15
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