IJCAI 2024poster0 citations

Intention Progression with Temporally Extended Goals

Yuan Yao, Natasha Alechina, Brian Logan

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 RL policies to be freely mixed in an agent program, allowing the development of agents with `neuro-symbolic' architectures.

Agent-based and Multi-agent Systems: MAS: Agent theories and modelsAgent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools
BibTeX
@inproceedings{ijcai2024p33,
  title     = {Intention Progression with Temporally Extended Goals},
  author    = {Yao, Yuan and Alechina, Natasha and Logan, Brian},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {292--301},
  year      = {2024},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2024/33},
  url       = {https://doi.org/10.24963/ijcai.2024/33},
}
Intention Progression with Temporally Extended Goals · IJCAI 2024