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Intention Interleaving Via Classical Replanning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Intention Interleaving Via Classical Replanning. / Xu, Mengwei; McAreavey, Kevin; Bauters, Kim; Liu, Weiru.

2019 31st International Conference on Tools with Artificial Intelligence. Institute of Electrical and Electronics Engineers (IEEE), 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Xu, M, McAreavey, K, Bauters, K & Liu, W 2019, Intention Interleaving Via Classical Replanning. in 2019 31st International Conference on Tools with Artificial Intelligence. Institute of Electrical and Electronics Engineers (IEEE), International Conference on Tools with Artificial Intelligence, Portland, United States, 4/11/19.

APA

Xu, M., McAreavey, K., Bauters, K., & Liu, W. (Accepted/In press). Intention Interleaving Via Classical Replanning. In 2019 31st International Conference on Tools with Artificial Intelligence Institute of Electrical and Electronics Engineers (IEEE).

Vancouver

Xu M, McAreavey K, Bauters K, Liu W. Intention Interleaving Via Classical Replanning. In 2019 31st International Conference on Tools with Artificial Intelligence. Institute of Electrical and Electronics Engineers (IEEE). 2019

Author

Xu, Mengwei ; McAreavey, Kevin ; Bauters, Kim ; Liu, Weiru. / Intention Interleaving Via Classical Replanning. 2019 31st International Conference on Tools with Artificial Intelligence. Institute of Electrical and Electronics Engineers (IEEE), 2019.

Bibtex

@inproceedings{6b72a035d9cd4d1d8d4f6ca5cc16c404,
title = "Intention Interleaving Via Classical Replanning",
abstract = "The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions.",
keywords = "BDI Agents, Intention Interleaving, Planning",
author = "Mengwei Xu and Kevin McAreavey and Kim Bauters and Weiru Liu",
year = "2019",
month = "8",
day = "13",
language = "English",
booktitle = "2019 31st International Conference on Tools with Artificial Intelligence",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Intention Interleaving Via Classical Replanning

AU - Xu, Mengwei

AU - McAreavey, Kevin

AU - Bauters, Kim

AU - Liu, Weiru

PY - 2019/8/13

Y1 - 2019/8/13

N2 - The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions.

AB - The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions.

KW - BDI Agents

KW - Intention Interleaving

KW - Planning

M3 - Conference contribution

BT - 2019 31st International Conference on Tools with Artificial Intelligence

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -