Skip to main content

On Admissible Behaviours for Goal-Oriented Decision-Making of Value-Aware Agents

  • Conference paper
  • First Online:
Multi-Agent Systems (EUMAS 2023)

Abstract

The emerging field of value awareness engineering claims that software agents and systems should be value-aware, i.e. they should be able to explicitly reason about the value-alignment of their actions. Values are often modelled as preferences over states or actions which are then extended to plans. In this paper, we examine the effect of different groundings of values depending on context and claim that they can be used to prune the space of courses of actions that are aligned with them. We put forward several notions of such value-admissible behaviours and illustrate them in the domain of water distribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Original definition from Montes and Sierra [9] assumes that the range of all value semantics functions is bounded in \([-1,1]\), so \(f_v(s)\approx \) \(-1,0,+1\) indicates that state s strongly opposes, is neutral or strongly promotes the value v, respectively. This would represent an (unnecessary strict) absolute value promotion metric.

  2. 2.

    Note that the \([-1,1]\)-bounded semantics function used in [9] is defined in terms of the Gini index, i.e., \(f_{eq} = 1 - 2 \cdot GI(s)\). Similarly, the rest of the semantics functions we have enumerated can be bounded to that interval if needed. For this theory, we just consider these functions as quantifiers of value preorders.

  3. 3.

    Council of Europe Parliamentary Assembly Resolution No. 1693 (2009).

  4. 4.

    SDG 6 of the United Nations 2030 Agenda for Sustainable Development https://www.un.org/sustainabledevelopment.

  5. 5.

    https://www.oecd.org/water/Recomendacion-del-Consejo-sobre-el-agua.pdf.

  6. 6.

    In agriculture (https://www.bancomundial.org/es/topic/water-in-agriculture).

  7. 7.

    Statistics on Water Supply and Sanitation Year 2020, see https://www.ine.es/prensa/essa_2020.pdf.

  8. 8.

    Royal Decree 1/2001, of July 20, approving the Revised Text of the Water Law, Article 60.

  9. 9.

    Royal Decree 3/2023, of January 10, establishing the technical-sanitary criteria for the quality of drinking water, its control, and supply, Article 9.

  10. 10.

    Royal Decree 1/2001, of July 20, approving the Revised Text of the Water Law, Article 14.

References

  1. Arnold, T., Kasenberg, D., Scheutz, M.: Value alignment or misalignment - what will keep systems accountable? In: AAAI Workshop on AI, Ethics, and Society (2017)

    Google Scholar 

  2. Christiano, P., Leike, J., Brown, T.B., Martic, M., Legg, S., Amodei, D.: Deep reinforcement learning from human preferences (2023)

    Google Scholar 

  3. Fürnkranz, J., Hüllermeier, E., Cheng, W., Park, S.H.: Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Mach. Learn. 89, 123–156 (2012). https://doi.org/10.1007/s10994-012-5313-8

    Article  MathSciNet  MATH  Google Scholar 

  4. Government, S.: Strategic project for economic recovery and transformation of digitalization of the water cycle. Report 2022. Technical report, Ministry for the Ecological Transition and Demographic Challenge (2022)

    Google Scholar 

  5. Guo, T., Yuan, Y., Zhao, P.: Admission-based reinforcement-learning algorithm in sequential social dilemmas. Appl. Sci. 13(3) (2023). https://doi.org/10.3390/app13031807. www.mdpi.com/2076-3417/13/3/1807

  6. Jiang, J., Lu, Z.: Learning fairness in multi-agent systems. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  7. Lera-Leri, R., Bistaffa, F., Serramia, M., Lopez-Sanchez, M., Rodriguez-Aguilar, J.: Towards pluralistic value alignment: aggregating value systems through \(l_p\)-regression. In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, Richland, SC, pp. 780–788. International Foundation for Autonomous Agents and Multiagent Systems (2022)

    Google Scholar 

  8. Montes, N., Osman, N., Sierra, C., Slavkovik, M.: Value engineering for autonomous agents. CoRR abs/2302.08759 (2023). https://doi.org/10.48550/arXiv.2302.08759

  9. Montes, N., Sierra, C.: Synthesis and properties of optimally value-aligned normative systems. J. Artif. Intell. Res. 74, 1739–1774 (2022). https://doi.org/10.1613/jair.1.13487

    Article  MathSciNet  MATH  Google Scholar 

  10. Ng, A.Y., Russell, S.J.: Algorithms for inverse reinforcement learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 663–670 (2000)

    Google Scholar 

  11. Perello-Moragues, A., Poch, M., Sauri, D., Popartan, L.A., Noriega, P.: Modelling domestic water use in metropolitan areas using socio-cognitive agents. Water 13(8) (2021). https://doi.org/10.3390/w13081024. www.mdpi.com/2073-4441/13/8/1024

  12. Plata-Pérez, L., Sánchez-Pérez, J., Sánchez-Sánchez, F.: An elementary characterization of the Gini index. Math. Soc. Sci. 74, 79–83 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. PricewaterhouseCoopers: La gestión del agua en españa. análisis y retos del ciclo urbano del agua (2018). www.pwc.es/es/publicaciones/energia/assets/gestion-agua-2018-espana.pdf

  14. Rodriguez-Soto, M., Serramia, M., Lopez-Sanchez, M., Rodriguez-Aguilar, J.A.: Instilling moral value alignment by means of multi-objective reinforcement learning. Ethics Inf. Technol. 24, 9 (2022). https://doi.org/10.1007/s10676-022-09635-0

    Article  Google Scholar 

  15. Sierra, C., Osman, N., Noriega, P., Sabater-Mir, J., Perelló, A.: Value alignment: a formal approach. CoRR abs/2110.09240 (2021). arxiv.org/abs/2110.09240

  16. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  17. van der Weide, T.L., Dignum, F., Meyer, J.-J.C., Prakken, H., Vreeswijk, G.A.W.: Practical reasoning using values. In: McBurney, P., Rahwan, I., Parsons, S., Maudet, N. (eds.) ArgMAS 2009. LNCS (LNAI), vol. 6057, pp. 79–93. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12805-9_5

    Chapter  Google Scholar 

Download references

Acknowledgements.

This work has been supported by grant VAE: TED2021-131295B-C33 funded by MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”, by grant COSASS: PID2021-123673OB-C32 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, and by the AGROBOTS Project of Universidad Rey Juan Carlos funded by the Community of Madrid, Spain.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrés Holgado-Sánchez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Holgado-Sánchez, A., Arias, J., Moreno-Rebato, M., Ossowski, S. (2023). On Admissible Behaviours for Goal-Oriented Decision-Making of Value-Aware Agents. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43264-4_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43263-7

  • Online ISBN: 978-3-031-43264-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics