Recent advances in computer and network technologies enable the provision of many services combining multiple types of information and different computational capabilities. The tasks for these services are executed by allocating them to appropriate collaborative agents, which are computational entities with specific functionality. However, the number of these tasks is huge, and these tasks appear simultaneously, and appropriate allocation strongly depends on the agent's capability and the resource patterns required to complete tasks. Thus, we first propose a task allocation method in which, although the social utility for the shared and required performance is attempted to be maximized, agents also give weight to individual preferences based on their own specifications and capabilities. We also propose a learning method in which collaborative agents autonomously decide the preference adaptively in the dynamic environment. We experimentally demonstrate that the appropriate strategy to decide the preference depends on the type of task and the features of the task reward. We then show that agents using the proposed learning method adaptively decided their preference and could maintain excellent performance in a changing environment.
Iijima, N., Sugiyama, A., Hayano, M., & Sugawara, T. (2017). Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments. In Procedia Computer Science (Vol. 112, pp. 91–98). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.08.177