Intention Progression under Uncertainty

Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
EditorsChristian Bessiere
Pages10-16
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - 2020
Externally publishedYes

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