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2021 | OriginalPaper | Chapter

An Agent Architecture for Knowledge Discovery and Evolution

Authors : Tezira Wanyana, Deshendran Moodley

Published in: KI 2021: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.

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Adam, C., Gaudou, B.: BDI agents in social simulations: a survey. Knowl. Eng. Rev. 31(3), 207–238 (2016)CrossRef

Adeleke, J.A.: A semantic sensor web framework for proactive environmental monitoring and control. Ph.D. thesis, University of KwaZulu-Natal, Durban (ukzn) (2017)

Bordini, R.H., El Fallah Seghrouchni, A., Hindriks, K., Logan, B., Ricci, A.: Agent programming in the cognitive era. Auton. Agent. Multi-Agent Syst. 34(2), 1–31 (2020). https://doi.org/10.1007/s10458-020-09453-yCrossRef

Bratman, M.E., Israel, D.J., Pollack, M.E.: Plans and resource-bounded practical reasoning. Comput. Intell. 4(3), 349–355 (1988)CrossRef

Coetzer, W., Moodley, D., Gerber, A.: A knowledge-based system for generating interaction networks from ecological data. Data Knowl. Eng. 112, 55–78 (2017)CrossRef

Elhadj, H.B., Sallabi, F., Henaien, A., Chaari, L., Shuaib, K., Al Thawadi, M.: Do-care: a dynamic ontology reasoning based healthcare monitoring system. Future Gener. Comput. Syst. 118, 417–431 (2021)CrossRef

Fagundes, M.S., Vicari, R.M., Coelho, H.: Deliberation process in a BDI model with Bayesian networks. In: Ghose, A., Governatori, G., Sadananda, R. (eds.) PRIMA 2007. LNCS (LNAI), vol. 5044, pp. 207–218. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01639-4_18CrossRef

Gil, Y., et al.: Automated hypothesis testing with large scientific data repositories. In: Proceedings of the Fourth Annual Conference on Advances in Cognitive Systems (ACS), vol. 2, p. 4 (2016)

Gil, Y., et al.: Towards continuous scientific data analysis and hypothesis evolution. In: AAAI, pp. 4406–4414 (2017)

10.

Haig, B.D.: An abductive theory of scientific method. In: Method Matters in Psychology. SAPERE, vol. 45, pp. 35–64. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01051-5_3CrossRef

11.

Haig, B.D.: The importance of scientific method for psychological science. Psychol. Crime Law 25(6), 527–541 (2019)CrossRef

12.

Han, J., Kamber, M., Pei, J.: Data mining concepts and techniques third edition. Morgan Kaufmann Ser. Data Manag. Syst. 5(4), 83–124 (2011)

13.

Hristoskova, A., Sakkalis, V., Zacharioudakis, G., Tsiknakis, M., De Turck, F.: Ontology-driven monitoring of patient’s vital signs enabling personalized medical detection and alert. Sensors 14(1), 1598–1628 (2014)CrossRef

14.

King, R.D., Rowland, J., Aubrey, W., Liakata, M., Markham, M., Soldatova, L.N., Whelan, K.E., Clare, A., Young, M., Sparkes, A., et al.: The robot scientist adam. Computer 42(8), 46–54 (2009)CrossRef

15.

King, R.D., et al.: The automation of science. Science 324(5923), 85–89 (2009)CrossRef

16.

King, R.D., et al.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427(6971), 247–252 (2004)CrossRef

17.

Klapiscak, T., Bordini, R.H.: JASDL: a practical programming approach combining agent and semantic web technologies. In: Baldoni, M., Son, T.C., van Riemsdijk, M.B., Winikoff, M. (eds.) DALT 2008. LNCS (LNAI), vol. 5397, pp. 91–110. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-93920-7_7CrossRef

18.

Montagna, S., Mariani, S., Gamberini, E., Ricci, A., Zambonelli, F.: Complementing agents with cognitive services: a case study in healthcare. J. Med. Syst. 44(10), 1–10 (2020)CrossRef

19.

Moreira, Á.F., Vieira, R., Bordini, R.H., Hübner, J.F.: Agent-oriented programming with underlying ontological reasoning. In: Baldoni, M., Endriss, U., Omicini, A., Torroni, P. (eds.) DALT 2005. LNCS (LNAI), vol. 3904, pp. 155–170. Springer, Heidelberg (2006). https://doi.org/10.1007/11691792_10CrossRef

20.

Muhammad, F., et al.: A novelty-centric agent architecture for changing worlds. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 925–933 (2021)

21.

Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. KR 91, 473–484 (1991)

22.

Rao, A.S., Georgeff, M.P., et al.: BDI agents: from theory to practice. In: Icmas, vol. 95, pp. 312–319 (1995)

23.

Savaglio, C., Ganzha, M., Paprzycki, M., Bădică, C., Ivanović, M., Fortino, G.: Agent-based internet of things: state-of-the-art and research challenges. Future Gener. Comput. Syst. 102, 1038–1053 (2020)CrossRef

24.

Sen, A., Sterner, B., Franz, N., Powel, C., Upham, N.: Combining machine learning & reasoning for biodiversity data intelligence. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 14911–14919 (2021)

25.

Silva, D.G., Gluz, J.C.: AgentSpeak (PL): a new programming language for BDI agents with integrated Bayesian network model. In: 2011 International Conference on Information Science and Applications, pp. 1–7. IEEE (2011)

26.

Sondes, T., Elhadj, H.B., Chaari, L.: An ontology-based healthcare monitoring system in the internet of things. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 319–324. IEEE (2019)

27.

Tiddi, I., d’Aquin, M., Motta, E.: An ontology design pattern to define explanations. In: Proceedings of the 8th International Conference on Knowledge Capture, pp. 1–8 (2015)

28.

Tiddi, I., d’Aquin, M., Motta, E.: Dedalo: looking for clusters explanations in a labyrinth of linked data. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 333–348. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_23CrossRef

29.

Toussaint, W.: Evaluation of clustering techniques for generating household energy consumption patterns in a developing country. Master’s thesis, Faculty of Science, University of Cape Town (2019)

30.

Toussaint, W., Moodley, D.: Comparison of clustering techniques for residential load profiles in South Africa. In: Davel, M.H., Barnard, E. (eds.) Proceedings of the South African Forum for Artificial Intelligence Research Cape Town, South Africa, 4–6 December 2019. CEUR Workshop Proceedings, vol. 2540, pp. 117–132. CEUR-WS.org (2019)

31.

Toussaint, W., Moodley, D.: Automating cluster analysis to generate customer archetypes for residential energy consumers in South Africa. arXiv preprint arXiv:2006.07197 (2020)

32.

Wanyana, T., Moodley, D., Meyer, T.: An ontology for supporting knowledge discovery and evolution. In: Gerber, A. (ed.) Southern African Conference for Artificial Intelligence Research (SACAIR), pp. 206–221 (2020)

Title: An Agent Architecture for Knowledge Discovery and Evolution
Authors: Tezira Wanyana
Deshendran Moodley
Publisher: Springer International Publishing
Book: KI 2021: Advances in Artificial Intelligence
Print ISBN: 978-3-030-87625-8

Electronic ISBN: 978-3-030-87626-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-87626-5_18

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