Abstract
In task allocation a group of agents perform search and discovery of tasks, then allocate themselves to complete those tasks. Tasks are assumed to have a strong signature by which they can be identified. This paper considers task allocation in environments where the definition of a task is weak and can change over time. Specifically, we define tasks as environmental anomalies and present a new optimisation-based task allocation algorithm using anomaly detection to generate a dynamic fitness function. We present experiments in a simulated environment to show that agents using this algorithm can generate a dynamic fitness function using anomaly detection. They can then converge on optima in this function using particle swarm optimisation. The demonstration is conducted in a workplace hazard identification simulation.
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Notes
- 1.
Synapses in our neural network are represented by the weight paths that link out input and output nodes.
References
Australia, S.W.: Australian Workers Compensation Statistics, 2012–13 (2013). http://www.safeworkaustralia.gov.au/sites/SWA/about/Publications/Documents/897/australian-workers-compensation-statistics-2012-13.pdf
Bahn, S.: Workplace hazard identification and management: the case of an underground mining operation. Saf. Sci. 57, 129–137 (2013)
Biggs, H.C., Sheahan, V.L., Dingsdag, D.P.: Improving industry safety culture: the tasks in which safety critical positions holders must be competent. In: 2006 CIB99 International Conference on Global Unity for Safety & Health in Construction, pp. 181–187 (2006)
Blackwell, T.: Particle swarm optimization in dynamic environments. Intelligence (SCI) 51, 29–49 (2007)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS 1995, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43 (1995)
Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 84–88. IEEE (2000)
Fernandez-Marquez, J., Arcos, J.: Adapting particle swarm optimization in dynamic and noisy environments. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics, vol. 18. Springer, New York (2001)
Karimi, J., Nobahari, H., Pourtakdoust, S.H.: A new hybrid approach for dynamic continuous optimization problems. Appl. Soft Comput. J. 12(3), 1158–1167 (2012)
Manuele, F.: Acceptable risk - time for SH&E professionals to adopt the concept. Prof. Saf. 55(May), 30–38 (2010)
Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Sig. Process. 83(12), 2481–2497 (2003)
Markou, M., Singh, S.: Novelty detection: a review - part 2: neural network based approaches. Sig. Process. 83(12), 2499–2521 (2003)
Marsland, S.: Machine Learning: An Algorithmic Perspective, 1st edn. CRC Press, New York (2011)
Marsland, S., Nehmzow, U., Shapiro, J.: A Real-Time Novelty Detector for a Mobile Robot, p. 8 (2000). arXiv preprint cs/0006006
Miljkovic, D.: Review of novelty detection methods. In: MIPRO, 2010 Proceedings of the 33rd International Convention, pp. 593–598 (2010)
Morrison, R.W., De Jong, K.A.: Measurement of population diversity. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 31–41. Springer, Heidelberg (2002)
Shafi, K., Merrick, K.: A curious agent for network anomaly detection. In: The 10th International Conference on Autonomous Agents and Multiagent Systems, vol. 33, pp. 1075–1076 (2011)
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Klyne, A., Merrick, K. (2015). Task Allocation Using Particle Swarm Optimisation and Anomaly Detection to Generate a Dynamic Fitness Function. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_28
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DOI: https://doi.org/10.1007/978-3-319-26350-2_28
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