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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-030-26354-6_3
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