By Byeong Ho Kang, Quan Bai
This e-book constitutes the refereed complaints of the twenty ninth Australasian Joint convention on man made Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016.
The forty complete papers and 18 brief papers provided including eight invited brief papers have been conscientiously reviewed and chosen from 121 submissions. The papers are prepared in topical sections on brokers and multiagent structures; AI purposes and strategies; giant information; constraint pride, seek and optimisation; wisdom illustration and reasoning; computing device studying and information mining; social intelligence; and textual content mining and NLP.
The complaints additionally comprises 2 contributions of the AI 2016 doctoral consortium and six contributions of the SMA 2016.
Read or Download AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings PDF
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Extra resources for AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings
Resistance to corruption of general strategic argumentation. In: Proceedings of International Conference Principles and Practice of Multi-Agent Systems, pp. 61–75 (2016) 15. : Resistance to corruption of strategic argumentation. In: AAAI Conference on Artiﬁcial Intelligence, pp. J. Maher 16. : A characterisation of strategy-proofness for grounded argumentation semantics. In: Boutilier, C. ) IJCAI, pp. 251–256 (2009) 17. : Two approaches to dialectical argumentation: admissible sets and argumentation stages.
Then these problem are examined for, respectively, the ideal, naive, and stage semantics. Their computational complexity is established, which provides the basis for identifying resistance to corruption. 1 Background Abstract Argumentation This work is based on abstract argumentation in the sense of , which addresses the evaluation of a static set of arguments. An argumentation framework A = (S, ) consists of a ﬁnite set of arguments S and a binary relation over S, called the attack (sometimes, defeat) relation.
187–220. Springer, London (2013) 2. : Multiagent reinforcement learning in the iterated prisoner’s dilemma. Biosystems 37(1–2), 147–166 (1996) 3. : A reinforcement procedure leading to correlated equilibrium. , Trockel, W. ) Economics Essays, pp. 181–200. Springer, Berlin (2001). 1007/978-3-662-04623-4 12 4. : Fully distributed learning for global optima. In: Distributed Strategic Learning for Wireless Engineers, pp. 317–359. CRC Press, UK (2012) 5. : Blackwell’s approachability in stackelberg stochastic games: a learning version.