Robust execution of BDI agent programs by exploiting synergies between intentions

Yao, Yuan; Logan, Brian; Thangarajah, John

Abstract

A key advantage the reactive planning approach adopted by BDI-based agents is the ability to recover from plan execution failures, and almost all BDI agent programming languages and platforms provide some form of failure handling mechanism. In general, these consist of simply choosing an alternative plan for the failed subgoal (e.g., JACK, Jadex). In this paper, we propose an alternative approach to recovering from execution failures that relies on exploiting positive interactions between an agent’s intentions. A positive interaction occurs when the execution of an action in one intention assists the execution of actions in other intentions (e.g., by (re)establishing their preconditions). We have implemented our approach in a scheduling algorithm for BDI agents which we call SP. The results of a preliminary empirical evaluation of SP suggest our approach out- performs existing failure handling mechanisms used by state-of-the-art BDI languages. Moreover, the computational overhead of SP is modest.

Citation

Yao, Y., Logan, B., & Thangarajah, J. (2016, February). Robust execution of BDI agent programs by exploiting synergies between intentions. Presented at 30th AAAI conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona USA

• Conference Name: 30th AAAI conference on Artificial Intelligence (AAAI-16)
• Start Date: Feb 12, 2016
• End Date: Feb 17, 2016
• Publication Date: Feb 17, 2016
• Deposit Date: Dec 2, 2015
• Publicly Available Date: Feb 17, 2016
• Peer Reviewed: Peer Reviewed
• Pages: 2558=2564
• Book Title: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
• Public URL: https://nottingham-repository.worktribe.com/output/980448
• Publisher URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12148
• Related Public URLs: http://www.aaai.org/Conferences/AAAI/aaai16.php
• Additional Information: Copyright Association for the Advancement of Artificial Intelligence.
• Contract Date: Dec 2, 2015