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GenGPT: A Systematic Way to Generate Synthetic Goal-Plan Trees

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Book cover Engineering Multi-Agent Systems (EMAS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13190))

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Abstract

Deciding “what to do next” is a key problem for BDI agents with multiple goals, which is termed the intention progression problem (IPP). A number of approaches to solving the IPP have been proposed in the literature, however, their evaluations are all taken in different forms. The lack of standard benchmarks and testbeds for evaluating the IPP makes it difficult for researchers to contribute to this topic. To foster research around the IPP and BDI agents, this paper proposes a way to generate test cases in the form of goal-plan trees which can be used to represent the agent’s intentions in various agent languages and platforms.

Supported by Zhejiang Provincial Natural Science Foundation of China (LQ19F030009) and National Natural Science Foundation of China (61906169).

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Notes

  1. 1.

    There is a very brief discussion on generating synthetic goal-plan trees in [2].

  2. 2.

    Here, we ignore the changes caused by the execution of other intentions.

  3. 3.

    The source code and a detailed instruction manual can be found at the following url: “https://github.com/yvy714/GenGPT.git”.

  4. 4.

    We omit all these static parameters in the input of the algorithm for legibility.

  5. 5.

    This assumption is reasonable, as there is very few real world plans will affect all the environment variables.

  6. 6.

    l is one of the input parameters.

  7. 7.

    In this paper, we choose to use fixed number of goals, plans and actions for simplicity. However, it is straight forward to change it to a more flexible version with minimal and maximum number of goals, plans and actions.

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Acknowledge

We would like to thank Brian Logan and John Thangarajah for many helpful discussions relating to the work presented here.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Yuan Yao & Di Wu

  2. Department of Computer Science, University of Nottingham Ningbo China, Ningbo, China

    Yuan Yao

Authors
  1. Yuan Yao
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  2. Di Wu
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Correspondence to Yuan Yao or Di Wu .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Natasha Alechina

  2. University of Turin, Turin, Italy

    Matteo Baldoni

  3. Utrecht University, Utrecht, The Netherlands

    Brian Logan

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Yao, Y., Wu, D. (2022). GenGPT: A Systematic Way to Generate Synthetic Goal-Plan Trees. In: Alechina, N., Baldoni, M., Logan, B. (eds) Engineering Multi-Agent Systems. EMAS 2021. Lecture Notes in Computer Science(), vol 13190. Springer, Cham. https://doi.org/10.1007/978-3-030-97457-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-97457-2_21

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  • Online ISBN: 978-3-030-97457-2

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