Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Multiagent Systems

arXiv:2110.12579v1 (cs)
[Submitted on 25 Oct 2021]

Title:Observable and Attention-Directing BDI Agents for Human-Autonomy Teaming

Authors:Blair Archibald, Muffy Calder, Michele Sevegnani, Mengwei Xu
View PDF
Abstract:Human-autonomy teaming (HAT) scenarios feature humans and autonomous agents collaborating to meet a shared goal. For effective collaboration, the agents must be transparent and able to share important information about their operation with human teammates. We address the challenge of transparency for Belief-Desire-Intention agents defined in the Conceptual Agent Notation (CAN) language. We extend the semantics to model agents that are observable (i.e. the internal state of tasks is available), and attention-directing (i.e. specific states can be flagged to users), and provide an executable semantics via an encoding in Milner's bigraphs. Using an example of unmanned aerial vehicles, the BigraphER tool, and PRISM, we show and verify how the extensions work in practice.
Comments: In Proceedings FMAS 2021, arXiv:2110.11527
Subjects: Multiagent Systems (cs.MA); Human-Computer Interaction (cs.HC); Programming Languages (cs.PL)
Cite as: arXiv:2110.12579 [cs.MA]
  (or arXiv:2110.12579v1 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2110.12579
Journal reference: EPTCS 348, 2021, pp. 167-175
Related DOI: https://doi.org/10.4204/EPTCS.348.12

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Mon, 25 Oct 2021 01:47:53 UTC (20 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.MA
< prev   |   next >
new | recent | 2021-10
Change to browse by:
cs
cs.HC
cs.PL

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)