Abstract
Multiagent recognition based on argumentation is highly concerned with the utilization of the advantages offered by argument games for the purpose of justifying and explaining the decision results of intelligent systems. However, arguing agents for collaborative recognition tasks are often encountered with disagreement due to different abstract levels of object categories. To cope with this category level inconsistent problem in argumentation based multiagent recognition, we propose CALL, a context-aware layered learning method for conflict resolution among multiple agents. Context-awareness is explored, in this paper, to investigate how structured contextual knowledge can facilitate dynamic arguments constructing in argumentation. The main contribution provided by the proposed method is that it not only can achieve natural conflict resolution in multiagent collaborative recognition systems, but also give consistent explanations with easily assimilated reasoning of multi-party argument games. Preliminary experimental results demonstrate the effectiveness of our method with significant improvements over state-of-the-art, especially in presence of noise.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rida, I., et al.: Palmprint recognition with an efficient data driven ensemble classifier. Pattern Recognit. Lett., 1–10 (2018)
Ke, R., et al.: Real-time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow. IEEE Trans. Intell. Transp. Syst. 20, 1–11 (2018)
Pourtaheri, Z.K., Zahiri, S.H., Razavi, S.M.: Stability investigation of multi-objective heuristic ensemble classifiers. Int. J. Mach. Learn. Cybern. 2, 1–13 (2018)
Thimm, M., Kersting, K.: Towards argumentation-based classification. In: Logical Foundations of Uncertainty and Machine Learning, Workshop at IJCAI, vol. 17 (2017)
Conţiu, Ş., Groza, A.: Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Syst. Appl. 64, 269–286 (2016)
Zeng, Z., et al.: Context-based and explainable decision making with argumentation. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multi-agent Systems (2018)
Hao, Z., Yao, L., Liu, B., Wang, Y.: Arguing prism: an argumentation based approach for collaborative classification in distributed environments. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014. LNCS, vol. 8645, pp. 34–41. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10085-2_3
Silla, C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Disc. 22(1-2), 31–72 (2011)
Le, T., et al.: On expressiveness and uncertainty awareness in rule-based classification for data streams. Neurocomputing 265, 127–141 (2017)
Fan, X., et al.: A first step towards explained activity recognition with computational abstract argumentation. In: 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE (2016)
Zeng, Z., et al.: Building more explainable artificial intelligence with argumentation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Wardeh, M., Coenen, F., Capon, T.B.: PISA: a framework for multiagent classification using argumentation. Data Knowl. Eng. 78, 34–57 (2012)
Liu, B., et al.: Combining ontology and reinforcement learning for zero-shot classification. Knowl.-Based Syst. 144, 42–50 (2018)
Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36, 453–465 (2014)
Noor, M.H.M., et al.: Enhancing ontological reasoning with uncertainty handling for activity recognition. Knowl.-Based Syst. 114, 47–60 (2016)
Hao, Z., Liu, B., Wu, J., Yao, J.: Exploiting ontological reasoning in argumentation based multi-agent collaborative classification. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS (LNAI), vol. 9011, pp. 23–33. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15702-3_3
Yao, L., et al.: Evaluating the valuable rules from different experience using multiparty argument games. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 02. IEEE Computer Society (2012)
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), 321–358 (1995)
Acknowledgements
This work is supported by Postdoctoral Science Foundation of China (Grant No. 2018M643187), National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hao, Zy., Liu, T., Yang, C., Chen, X. (2019). Context-Aware Layered Learning for Argumentation Based Multiagent Collaborative Recognition. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-26354-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26353-9
Online ISBN: 978-3-030-26354-6
eBook Packages: Computer ScienceComputer Science (R0)