Multi-Agent Intention Recognition and Progression

Michael Dann, Yuan Yao, Natasha Alechina, Brian Logan, Felipe Meneguzzi, John Thangarajah

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


For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.
Original languageEnglish
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
Number of pages9
ISBN (Electronic)9781956792034
Publication statusPublished - 2023


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