Intention progression under uncertainty

Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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 solver 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 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
PublisherInternational Joint Conferences on Artificial Intelligence
Pages10-16
Number of pages7
ISBN (Electronic)9780999241165
Publication statusPublished - 2020
Externally publishedYes
Event29th International Joint Conference on Artificial Intelligence, IJCAI 2020 - Yokohama, Japan
Duration: 1 Jan 2021 → …

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2021-January
ISSN (Print)1045-0823

Conference

Conference29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Country/TerritoryJapan
CityYokohama
Period1/01/21 → …

ASJC Scopus subject areas

  • Artificial Intelligence

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