Abstract
Assumption-based Argumentation (ABA) has received a lot of attention and research attribute to its computability and interpretability. However, it is difficult for users to use ABA to represent the practical problems, especially for the data of problems involving natural language text/processing. Furthermore, since most arguments are derived by rules and some commonsense arguments are implied, massive rules need to be generated during representing practical problems by ABA, which reduces execution efficiency. Since the technologies of knowledge extraction from text and knowledge reasoning of knowledge graph are getting more and more efficient, it is possible to solve the above problems by using knowledge graph with ABA. In this paper, we propose a labeled RDF triple representation to enhance the semantics of triple and concise the process of triples extraction, and an abstract labeled knowledge-based decision making framework based on labeled triples for decision making. In order to apply the framework to the specific scene, we first give a detailed description of the definition and usage of labels in medical argumentation. Then we define an active decision framework for detecting the available decisions, and an optimal decision framework for computing the “best” decisions. Both of them build on argumentation-based computational mechanisms. Finally, we compute and explain the selected decisions by experimenting with the real argumentation record and demonstrate the applicability and rationality of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
As described in [4], \(\tau \notin \mathcal{L}\) stands for “true” and is used to represent the empty body of rules.
References
Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artif. Intell. 173(3–4), 413–436 (2009)
Fox, J., Krause, P., EIvang-Gøransson, M.: Argumentation as a general framework for uncertain reasoning. In: Uncertainty in Artificial Intelligence, pp. 428–434. Morgan Kaufmann (1993)
Cyras, K., Schulz, C., Toni, F., et al.: Assumption-based argumentation: disputes, explanations, preferences (2017)
Dung, P.M., Kowalski, R.A., Toni, F.: Assumption-based argumentation. In: Simari, G., Rahwan, I. (eds.) Argumentation in Artificial Intelligence, pp. 199–218. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-98197-0_10
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)
Dong, X., Gabrilovich, E., Heitz, G., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601–610 (2014)
Baroni, P., Toni, F., Verheij, B.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games: 25 years later. Argument Comput. 11(1–2), 1–14 (2020)
Chen, X., Jia, S., Xiang, Y.: A review: knowledge reasoning over knowledge graph. Expert Syst. Appl. 141, 112948 (2020)
Fan, X., Craven, R., Singer, R., Toni, F., Williams, M.: Assumption-based argumentation for decision-making with preferences: a medical case study. In: Leite, J., Son, T Cao, Torroni, P., van der Torre, L., Woltran, S. (eds.) CLIMA 2013. LNCS (LNAI), vol. 8143, pp. 374–390. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40624-9_23
Fan, X., Toni, F.: Decision making with assumption-based argumentation. In: Black, E., Modgil, S., Oren, N. (eds.) TAFA 2013. LNCS (LNAI), vol. 8306, pp. 127–142. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54373-9_9
Matt, P.-A., Toni, F., Vaccari, J.R.: Dominant decisions by argumentation agents. In: McBurney, P., Rahwan, I., Parsons, S., Maudet, N. (eds.) ArgMAS 2009. LNCS (LNAI), vol. 6057, pp. 42–59. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12805-9_3
Black, E., Atkinson, K.: Choosing persuasive arguments for action. In: AAMAS 2011, The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 3. International Foundation for Autonomous Agents and Multiagent Systems, pp. 905–912 (2011)
Fan, X., Toni, F.: Assumption-based argumentation dialogues. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)
Fan, X., Toni, F., Mocanu, A., et al.: Dialogical two-agent decision making with assumption-based argumentation. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 533–540 (2014)
Zeng, Z., Fan, X., Miao, C., et al.: Context-based and explainable decision making with argumentation. In: AAMAS, pp. 1114–1122 (2018)
Wakaki, T.: Assumption-based argumentation equipped with preferences and its application to decision making, practical reasoning, and epistemic reasoning. Comput. Intell. 33(4), 706–736 (2017)
Pan, J.Z.: Resource description framework. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 71–90. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_3
Sun, Z., Yang, J., Zhang, J., et al.: Recurrent knowledge graph embedding for effective recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 297–305 (2018)
Ernst, P., Meng, C., Siu, A., et al.: Knowlife: a knowledge graph for health and life sciences. In: 2014 IEEE 30th International Conference on Data Engineering. IEEE, pp. 1254–1257 (2014)
Chang, C.F., Miller, A., Ghose, A.: Mixed-initiative argumentation: group decision support in medicine. In: Kostkova, P. (ed.) eHealth 2009. LNICSSITE, vol. 27, pp. 43–50. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11745-9_8
Gartner, D., Toni, F.: CaSAPI: a system for credulous and sceptical argumentation. Proc. ArgNMR 80–95 (2007)
Hu, F., Lakdawala, S., Hao, Q., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf Technol. Biomed. 13(4), 656–663 (2009)
Al Qassas, M., Fogli, D., Giacomin, M., et al.: Analysis of clinical discussions based on argumentation schemes. Procedia Comput. Sci. 64, 282–289 (2015)
Acknowledgements
This work has been supported by National Key Research and Development Program of China under grant 2016YFB1000902, NSFC Project 61621003, 61872352, 62006125.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, C., Li, Y., Fei, C., Huang, X. (2022). Labeled Knowledge-Based Decision Making with Assumption-Based Argumentation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-10983-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10982-9
Online ISBN: 978-3-031-10983-6
eBook Packages: Computer ScienceComputer Science (R0)