Intention Progression with Temporally Extended Goals

Yuan Yao*, Natasha Alechina, Brian Logan

*Corresponding author for this work

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

Abstract

The Belief-Desire-Intention (BDI) approach to agent development has formed the basis for much of the research on architectures for autonomous agents. A key advantage of the BDI approach is that agents may pursue multiple intentions in parallel. However, previous approaches to managing possible interactions between concurrently executing intentions are limited to interactions between simple achievement goals (and in some cases maintenance goals). In this paper, we present a new approach to intention progression for agents with temporally extended goals which allow mixing reachability and invariant properties, e.g., “travel to location A while not exceeding a gradient of 5%”. Temporally extended goals may be specified at run-time (top-level goals), and as subgoals in plans. In addition, our approach allows human-authored plans and plans implemented as reinforcement learning policies to be freely mixed in an agent program, allowing the development of agents with 'neuro-symbolic' architectures.

Original languageEnglish
Title of host publicationProceedings of the 33rd International Joint Conference on Artificial Intelligence
Subtitle of host publicationMain Track
EditorsKate Larson
Pages292-301
Number of pages10
ISBN (Electronic)9781956792041
DOIs
Publication statusPublished - 9 Aug 2024
Event33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of
Duration: 3 Aug 20249 Aug 2024
https://ijcai24.org/

Conference

Conference33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Abbreviated titleIJCAI 202
Country/TerritoryKorea, Republic of
CityJeju
Period3/08/249/08/24
Internet address

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