Intention-Aware Multiagent Scheduling

Dann, Michael; Thangarajah, John; Yao, Yuan; Logan, Brian

Abstract

The Belief Desire Intention (BDI) model of agency is a popular and mature paradigm for designing and implementing multiagent systems. There are several agent implementation platforms that follow the BDI model. In BDI systems, the agents typically have to pursue multiple goals, and often concurrently. The way in which the agents commit to achieving their goals forms their intentions. There has been much work on scheduling the intentions of agents. However, most of this work has focused on scheduling the intentions of a single agent with no awareness and consideration of other agents that may be operating in the same environment. They schedule the intentions of the single-agent in order to maximise the total number of goals achieved. In this work, we investigate techniques for scheduling the intentions of an agent in a multiagent setting, where an agent is aware (or partially aware) of the intentions of other agents in the environment. We use a Monte Carlo Tree Search (MCTS) based approach and show that our intention-aware scheduler generates better outcomes in cooperative, neutral (selfish) and adversarial settings than the state-of-the-art schedulers that do not consider other agents' intentions.

Citation

Dann, M., Thangarajah, J., Yao, Y., & Logan, B. (2020). Intention-Aware Multiagent Scheduling. In B. An, N. Yorke-Smith, A. El Fallah Seghrouchni, & G. Sukthankar (Eds.), Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 19th International Conference on Autonomous Agents and Multiagent Systems
• Start Date: May 9, 2020
• End Date: May 13, 2020
• Acceptance Date: Feb 26, 2020
• Online Publication Date: May 13, 2020
• Publication Date: May 13, 2020
• Deposit Date: May 1, 2020
• Publicly Available Date: May 13, 2020
• Book Title: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)
• ISBN: 97814503-5184
• Public URL: https://nottingham-repository.worktribe.com/output/4355921
• Publisher URL: http://ifaamas.org/Proceedings/aamas2020/
• Related Public URLs: http://ifaamas.org