Intention Progression under Uncertainty

Yao, Yuan; Alechina, Natasha; Logan, Brian; Thangarajah, John

Abstract

A key problem in Belief-Desire-Intention agents is how an agent progresses its intentions, i.e., which plans should be selected and how the execution of these plans should be interleaved so as to achieve the agent’s goals. Previous approaches to the intention progression problem assume the agent has perfect information about the state of the environment. However, in many real-world applications, an agent may be uncertain about whether an environment condition holds, and hence whether a particular plan is applicable or an action is executable. In this paper, we propose SAU, a Monte-Carlo Tree Search (MCTS)-based scheduler for intention progression problems where the agent’s beliefs are uncertain. We evaluate the performance of our approach experimentally by varying the degree of uncertainty in the agent’s beliefs. The results suggest that SAU is able to successfully achieve the agent’s goals even in settings where there is significant uncertainty in the agent’s beliefs.

Citation

Yao, Y., Alechina, N., Logan, B., & Thangarajah, J. (2020). Intention Progression under Uncertainty. In C. Bessiere (Ed.), Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) (10-16). https://doi.org/10.24963/ijcai.2020/2

• Conference Name: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
• Conference Location: Yokohama, Japan
• Start Date: Jan 7, 2021
• End Date: Jan 15, 2021
• Acceptance Date: Apr 30, 2020
• Online Publication Date: Jul 17, 2020
• Publication Date: Jul 17, 2020
• Deposit Date: May 1, 2020
• Publicly Available Date: Jul 17, 2020
• Pages: 10-16
• Book Title: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020)
• DOI: https://doi.org/10.24963/ijcai.2020/2
• Public URL: https://nottingham-repository.worktribe.com/output/4369330
• Publisher URL: https://www.ijcai.org/Proceedings/2020/2
• Related Public URLs: https://ijcai20.org/