Cited By
View all- Gajcin JJeromela JDusparic I(2024)Semifactual Explanations for Reinforcement LearningProceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3688324(167-175)Online publication date: 24-Nov-2024
A Structured Prompting based on Belief-Desire-Intention Model for Proactive and Explainable Task Planning
Pages 375 - 377
Abstract
We investigate the potential of the belief-desire-intention (BDI) model for enhancing proactive action planning and transparency in large language models (LLMs). Our proposed method, BDIPrompting, integrates the knowledge representation framework of the BDI model into prompt design. This allows agents to generate motivational and goal-directed service plans proactively while offering human users insights into the rationale behind the decision-making process. Through preliminary experiments with OpenAI’s GPT-4, we highlight the effectiveness of our approach in planning motivational actions and providing improved explanations during human-agent interactions.
References
[1]
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, 2023. Do as i can, not as i say: Grounding language in robotic affordances. In Conference on Robot Learning. PMLR, 287–318.
[2]
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International Conference on Machine Learning. PMLR, 9118–9147.
[3]
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, 2023. Inner Monologue: Embodied Reasoning through Planning with Language Models. In Conference on Robot Learning. PMLR, 1769–1782.
[4]
OpenAI. 2023. GPT-4 Technical Report. arxiv:2303.08774 [cs.CL]
[5]
Anand S Rao and Michael P Georgeff. 1997. Modeling rational agents within a BDI-architecture. Readings in agents (1997), 317–328.
[6]
MosaicML NLP Team. 2023. Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs. www.mosaicml.com/blog/mpt-7b Accessed: 2023-05-05.
[7]
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
[8]
Yoshua X ZXhang, Yann M Haxo, and Ying X Mat. 2023. Falcon LLM: A New Frontier in Natural Language Processing. AC Investment Research Journal 220, 44 (2023).
Index Terms
- A Structured Prompting based on Belief-Desire-Intention Model for Proactive and Explainable Task Planning
Recommendations
Genetic Algorithms as a feasible re-planning mechanism for Belief-Desire-Intention Agents
SAICSIT '15: Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information TechnologistsBelief-Desire-Intention (BDI) agents traditionally use a fixed, pre-defined plan library with the assumption that the environments in which they operate remain unchanged. Often agents have to operate in changing environments with the results that plans ...
Domain‐specific modelling language for belief–desire–intention software agents
Development of software agents according to belief–desire–intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi‐agent systems (MAS). Hence, here, a domain‐specific modelling language (DSML), called ...
A belief-desire-intention agent architecture for partner selection in peer-to-peer live video streaming applications
In peer-to-peer P2P video streaming systems, one of the most challenging parts is to schedule video data dissemination, that is, each peer should carefully select the partners it receives video from and the partners it sends data to. We believe that an ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
506 pages
ISBN:9798400708244
DOI:10.1145/3623809
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 December 2023
Check for updates
Author Tags
Qualifiers
- Poster
- Research
- Refereed limited
Funding Sources
- Ministry of Science and ICT
Conference
HAI '23
Acceptance Rates
Overall Acceptance Rate 121 of 404 submissions, 30%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 162Total Downloads
- Downloads (Last 12 months)162
- Downloads (Last 6 weeks)23
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
Cited By
View all- Gajcin JJeromela JDusparic I(2024)Semifactual Explanations for Reinforcement LearningProceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3688324(167-175)Online publication date: 24-Nov-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format