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Hierarchical-granularity holonic modelling

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Abstract

Design criteria for distributed and pervasive intelligent systems, such as Multi Agent Systems (MAS), are generally led by the functional decomposition of the given application-dependent knowledge. Consequently, changes either in the problem semantics or in the granularity level description may have a significant impact on the overall system re-engineering process. In order to tackle better these issues, a novel framework called Hierarchical-Granularity Holonic Model (HGHM) is introduced as a holon-based approach to distributed intelligent systems modelling. A holon is an agent endowed with special features. Seen from the outside, a holon behaves like an intelligent agent; seen from the inside, it appears to be decomposable into other holons. This property allows for modelling complex distributed systems at multiple hierarchical-granularity levels by exploiting the different abstraction layers at which the design process is carried out. The major benefit of the proposed approach against traditional holonic systems and MAS is that the entire HGHM-based architecture can be derived directly from the problem ontology as a hierarchical composition of self-similar, modular blocks. This helps designers focussing more on knowledge representation at different granularity levels which is a very basic process, as in top–down problem decomposition. Starting from the literature on holonic systems, a theoretical model of HGHM is introduced and an architectural model is derived accordingly. Finally, a customized application for the case study of distributed indoor air quality monitoring systems is commented and improvements in terms of system design with respect to well-established solutions are considered.

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Acknowledgments

The authors would like to thank the anonymous reviewers whose proposals and comments helped us improve the overall paper quality.

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Authors and Affiliations

  1. Università degli Studi di Milano, DTI, Via Bramante, 65, 26013, Crema, Italy

    Marco Calabrese, Alberto Amato & Vincenzo Piuri

  2. Politecnico di Bari, DIASS, Via Alcide De Gasperi, 74100, Taranto, Italy

    Vincenzo Di Lecce

Authors
  1. Marco Calabrese
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  2. Alberto Amato
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  3. Vincenzo Di Lecce
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  4. Vincenzo Piuri
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Correspondence to Marco Calabrese.

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Calabrese, M., Amato, A., Lecce, V.D. et al. Hierarchical-granularity holonic modelling. J Ambient Intell Human Comput 1, 199–209 (2010). https://doi.org/10.1007/s12652-010-0013-3

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  • DOI: https://doi.org/10.1007/s12652-010-0013-3

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