Publication Type

Conference Proceeding Article

Publication Date



Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that end, we first provide a general model to represent non-dedicated teams. Second, we provide a proactive approach based on sample average approximation to generate a strategy that works well across different feasible scenarios of agents leaving the team. Furthermore, we also provide a 2-stage approach that provides a 2-stage policy that changes allocation based on observed state of the team. Third, we provide a reactive approach that rearranges the allocated tasks to better adapt to leaving agents. Finally, we provide a detailed evaluation of our approaches on existing benchmark problems.


Artificial intelligence, Intelligent agents, Bench-mark problems, Disaster rescue, Dynamic environments, Priority tasks, Pro-active approach, Sample average approximation, Task allocation Uncertain environments, Human resource management


Artificial Intelligence and Robotics | Computer Engineering

Research Areas



Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017; Melbourne, Australia, 2017 August 19-25

City or Country

Melbourne, Australia

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.