Reasoning support for flexible task resourcing

In many settings, fully automated reasoning about tasks and resources is crucial. This is particularly important in multi-agent systems where tasks are monitored, managed and performed by intelligent agents. For these agents, it is critical to autonomously reason about the types of resources a task may require. However, determining appropriate resource types requires extensive expertise and domain knowledge. In this paper, we propose a means to automate the selection of resource types that are required to fulfil tasks. Our approach combines ontological reasoning and Logic Programming in a novel way for flexible matchmaking of resources to tasks. Using the proposed approach, intelligent agents can autonomously reason about the resources and tasks in various real-life settings and we demonstrate this here through case-studies. Our evaluation shows that the proposed approach equips intelligent agents with flexible reasoning support for task resourcing.