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Explaining a Staff Rostering Problem by Mining Trajectory Variance Structures

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Artificial Intelligence XL (SGAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14381))

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Abstract

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behaviour and the quality of solutions. In this paper, we apply our method of search trajectory mining through decomposition to the solutions created by a Genetic Algorithm—a non-deterministic, population-based metaheuristic. We complement this method with the use of One-Way ANOVA statistical testing to help identify explanatory features found in the search trajectories—subsets of the set of optimization variables having both high and low influence on the search behaviour of the GA and solution quality. This allows us to highlight these to an end-user to allow for greater flexibility in solution selection. We demonstrate the techniques on a real-world staff rostering problem and show how, together, they identify the personnel who are critical to the optimality of the rosters being created.

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References

  1. Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1), 189 (2019)

    Article  Google Scholar 

  2. Brownlee, A.E., Wright, J.A., He, M., Lee, T., McMenemy, P.: A Novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements. Appl. Soft Comput. 96, 106650 (2020)

    Article  Google Scholar 

  3. Hall, A., Ordish, J., Mitchell, C., Richardson (nee Murfet), H.: Black box medicine and transparency - interpretability by design framework. Technical report MSR-TR-2020-53, PHG Foundation (2020). https://www.microsoft.com/en-us/research/publication/black-box-medicine-and-transparency-interpretability-by-design-framework/

  4. European Commission: Ethics Guidelines for Trustworthy AI (2021). https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

  5. Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://www.sciencedirect.com/science/article/pii/S1566253519308103

  6. Dwivedi, R., et al.: Explainable AI (XAI): core ideas, techniques, and solutions. ACM Comput. Surv. 55(9) (2023). https://doi.org/10.1145/3561048

  7. Wright, J., Wang, M., Brownlee, A., Buswell, R.: Variable convergence in evolutionary optimization and its relationship to sensitivity analysis. In: Building Simulation and Optimization 2012, Loughborough, UK, pp. 102–109 (2012). https://repository.lboro.ac.uk/articles/conference_contribution/Variable_convergence_in_evolutionary_optimization_and_its_relationship_to_sensitivity_analysis/9438080

  8. Cortez, P., Embrechts, M.J.: Using sensitivity analysis and visualization techniques to open black box data mining models. Inf. Sci. 225, 1–17 (2013)

    Article  Google Scholar 

  9. Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol. Comput. 1(2), 61–70 (2011)

    Article  Google Scholar 

  10. Wallace, A., Brownlee, A.E.I., Cairns, D.: Towards explaining metaheuristic solution quality by data mining surrogate fitness models for importance of variables. In: Bramer, M., Ellis, R. (eds.) SGAI-AI 2021. LNCS (LNAI), vol. 13101, pp. 58–72. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91100-3_5

    Chapter  Google Scholar 

  11. Singh, M., Brownlee, A.E.I., Cairns, D.: Towards explainable metaheuristic: mining surrogate fitness models for importance of variables. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2022, pp. 1785–1793. Association for Computing Machinery, New York (2022)

    Google Scholar 

  12. Duygu Arbatli, A., Levent Akin, H.: Rule extraction from trained neural networks using genetic algorithms. Nonlinear Anal.: Theory Methods Appl. 30(3), 1639–1648 (1997)

    Article  MATH  Google Scholar 

  13. Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2020)

    Google Scholar 

  14. Fyvie, M., McCall, J.A.W., Christie, L.A., Brownlee, A.E.: Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. In: Genetic and Evolutionary Computation Conference Companion (GECCO 2023 Companion), 15–19 July 2023, Lisbon, Portugal (2023)

    Google Scholar 

  15. Dimitropoulaki, M., Kern, M., Owusu, G., McCormick, A.: Workforce rostering via metaheuristics. In: Bramer, M., Petridis, M. (eds.) SGAI 2018. LNCS (LNAI), vol. 11311, pp. 277–290. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04191-5_25

    Chapter  Google Scholar 

  16. Reid, K.N., et al.: A hybrid metaheuristic approach to a real world employee scheduling problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 1311–1318. Association for Computing Machinery, New York (2019)

    Google Scholar 

  17. Fyvie, M., McCall, J.A., Christie, L.A., Zăvoianu, A.C., Brownlee, A.E., Ainslie, R.: Explaining a staff rostering problem by mining trajectory variance structures definition (2023). https://github.com/rgu-subsea/mfyvie_sgai2023_varstruct.git

  18. Blank, J., Deb, K.: Pymoo: multi-objective optimization in python. IEEE Access 8, 89497–89509 (2020)

    Article  Google Scholar 

  19. Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. 4, 1–28 (2014)

    Google Scholar 

  20. Deb, K., Sindhya, K., Okabe, T.: Self-adaptive simulated binary crossover for real-parameter optimization. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 1187–1194. Association for Computing Machinery, New York (2007)

    Google Scholar 

  21. Fyvie, M., McCall, J.A.W., Christie, L.A.: Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. In: Bramer, M., Ellis, R. (eds.) SGAI-AI 2021. LNCS (LNAI), vol. 13101, pp. 89–102. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91100-3_7

    Chapter  Google Scholar 

  22. Pagés, J.: Multiple Factor Analysis by Example Using R, 1 edn. Chapman and Hall/CRC (2014)

    Google Scholar 

  23. Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in Science Conference 2010 (2010)

    Google Scholar 

  24. Dunn, O.J.: Multiple comparisons among means. J. Am. Stat. Assoc. 56(293), 52–64 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  25. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  26. Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. (TOIS) 28(4), 1–38 (2010)

    Article  Google Scholar 

  27. Sarica, A., Quattrone, A., Quattrone, A.: Introducing the rank-biased overlap as similarity measure for feature importance in explainable machine learning: a case study on Parkinson’s disease. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds.) BI 2022. LNCS, vol. 13406, pp. 129–139. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-15037-1_11

    Chapter  Google Scholar 

  28. Raikar, K.: How to objectively compare two ranked lists in python (2023). https://towardsdatascience.com/how-to-objectively-compare-two-ranked-lists-in-python-b3d74e236f6a

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Authors and Affiliations

  1. The Robert Gordon University, Garthdee Road, Aberdeen, UK

    Martin Fyvie, John A. W. McCall, Lee A. Christie & Alexandru-Ciprian Zăvoianu

  2. University of Stirling, Stirling, UK

    Alexander E. I. Brownlee

  3. The BT Group, Adastral Park, Ipswich, UK

    Russell Ainslie

Authors
  1. Martin Fyvie
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  2. John A. W. McCall
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  3. Lee A. Christie
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  4. Alexandru-Ciprian Zăvoianu
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  5. Alexander E. I. Brownlee
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  6. Russell Ainslie
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Corresponding author

Correspondence to Martin Fyvie .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. DFKI: German Research Center for Artificial Intelligence, Oldenburg, Germany

    Frederic Stahl

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Fyvie, M., McCall, J.A.W., Christie, L.A., Zăvoianu, AC., Brownlee, A.E.I., Ainslie, R. (2023). Explaining a Staff Rostering Problem by Mining Trajectory Variance Structures. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_27

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  • DOI: https://doi.org/10.1007/978-3-031-47994-6_27

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  • Print ISBN: 978-3-031-47993-9

  • Online ISBN: 978-3-031-47994-6

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