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Plan Acquisition in a BDI Agent Framework Through Intentional Learning

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Multiagent System Technologies (MATES 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10413))

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Abstract

Inspired by the theory of practical reasoning, Belief-Desire-Intention (BDI) agents are perhaps the most well-known type and architecture of cognitive agents. Such agents can reason about their environment and perform complex plans to bring about their objectives and goals. Within the context of ever-changing environments though, one desirable feature for agents is that of learning, implemented in BDI agents as Intentional Learning, a framework focused on the monitoring of the mental states to include learning as part of the agent goals. In this paper, we consider and develop intentional learning within the Jason BDI framework for agents focused on a plan acquisition strategy addressing the cases of learning plans composed of one action, sequences or a repetition of actions that allow an agent to improve its behaviour at run-time. This is done at the pure BDI agent level, the repertoire of plans is directly updated without using external planning tools.

We take as a testbed the simple vacuum cleaning environment and how new plans are acquired for accomplishing tasks of different level of complexity: escape from tunnel-like paths and wall-following. Furthermore, we integrate in a novel way the use of NetLogo as an environment to locate Jason agents, maintaining a clear delineation between decision making and action in the environment with the decision-making firmly anchored within the BDI agent’s reasoning cycle.

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Notes

  1. 1.

    http://ccl.northwestern.edu/netlogo/.

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Acknowledgment

We would like to thank to the project no. 48510384 of SEP-DSA/UAM-169, for the support provided to this work.

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Wulfrano Arturo Luna Ramirez & Maria Fasli

Authors
  1. Wulfrano Arturo Luna Ramirez
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  2. Maria Fasli
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Correspondence to Wulfrano Arturo Luna Ramirez .

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Editors and Affiliations

  1. University of Trier, Trier, Germany

    Jan Ole Berndt

  2. University of Vienna, Vienna, Austria

    Paolo Petta

  3. Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Essen, Nordrhein-Westfalen, Germany

    Rainer Unland

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Luna Ramirez, W.A., Fasli, M. (2017). Plan Acquisition in a BDI Agent Framework Through Intentional Learning. In: Berndt, J., Petta, P., Unland, R. (eds) Multiagent System Technologies. MATES 2017. Lecture Notes in Computer Science(), vol 10413. Springer, Cham. https://doi.org/10.1007/978-3-319-64798-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-64798-2_11

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