An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization

Antonio L. Alfeo, Francesco P. Appio, Mario G. C. A. Cimino, Alessandro Lazzeri, Antonella Martini, Gigliola Vaglini

2016

Abstract

Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results.

References

  1. Avvenuti, M., Cesarini, D., and Cimino, M.G.C.A., 2013, 'MARS, a Multi-Agent System for Assessing Rowers Coordination via Motion-Based Stigmergy', Sensors, vol. 13, pp. 12218-12243.
  2. Ciaramella, A., Cimino, M.G.C.A., Lazzerini, B., and Marcelloni, F., 2010, 'Using Context History to Personalize a Resource Recommender via a Genetic Algorithm', in Proceeding of the International Conference on Intelligent Systems Design and Applications, ISDA'10, IEEE, pp. 965-970.
  3. Cimino, M.G.C.A., Lazzeri, A., and Vaglini, G., 2015, 'Improving the Analysis of Context-Aware Information via Marker-Based Stigmergy and Differential Evolution', Artificial Intelligence and Soft Computing, vol. 9210, pp. 341-352.
  4. Cimino, M.G.C.A., Lazzerini, B., Marcelloni, F., and Pedrycz, W, 2014, 'Genetic interval neural networks for granular data regression', Information Sciences, vol. 257, pp. 313-330.
  5. Foray, D., 2013, 'The economic fundamentals of Smart Specialisation', Ekonomiaz, vol. 83, pp. 55-82.
  6. Jin, B., Ge, Y., Zhu, H., Guo, L., Xiong, H., and Zhang, C., 2014, 'Technology Prospecting for High Tech Companies through Patent Mining', in Proceedings of the International Conference of Data Mining (ICDM), IEEE, pp. 220-229.
  7. Mallipeddi, R., Suganthan, P.N., Pan, Q.K. and Tasgetiren, M., 2011, 'Differential evolution algorithm with ensemble of parameters and mutation strategies,78 Applied Soft Computing, vol. 11, pp.1679-1696.
  8. McCann, P., and Ortega-Argilés, R., 2013, 'Smart specialization, regional growth and applications to European union cohesion policy', Regional Studies, vol. 49, pp. 1291-1302.
  9. Mezura-Montes, E., Velázquez-Reyes, J., and Coello, C.A., 2006, 'A comparative study of differential evolution variants for global optimization,78 Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM, pp. 485-482.
  10. Organization for Economic Co-operation and Development (OECD), 2013, Innovation-driven growth in regions: the role of Smart Specialization. OECD, Paris.
  11. Parunak, V. H., 2006, 'A survey of environments and mechanisms for human-human stigmergy', Environments for Multi-Agent Systems II, Springer Berlin Heidelberg, pp. 163-186.
  12. Zaharie, D., 2007, 'A comparative analysis of crossover variants in differential evolution', Proceedings of IMCSIT, 2nd International Symposium Advances in Artificial Intelligence and Applications, pp. 171-181.
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Paper Citation


in Harvard Style

Alfeo A., Appio F., Cimino M., Lazzeri A., Martini A. and Vaglini G. (2016). An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 497-502. DOI: 10.5220/0005645204970502


in Bibtex Style

@conference{icpram16,
author={Antonio L. Alfeo and Francesco P. Appio and Mario G. C. A. Cimino and Alessandro Lazzeri and Antonella Martini and Gigliola Vaglini},
title={An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={497-502},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005645204970502},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Adaptive Stigmergy-based System for Evaluating Technological Indicator Dynamics in the Context of Smart Specialization
SN - 978-989-758-173-1
AU - Alfeo A.
AU - Appio F.
AU - Cimino M.
AU - Lazzeri A.
AU - Martini A.
AU - Vaglini G.
PY - 2016
SP - 497
EP - 502
DO - 10.5220/0005645204970502