By Byeong Ho Kang, Quan Bai
This ebook constitutes the refereed lawsuits of the twenty ninth Australasian Joint convention on synthetic Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016.
The forty complete papers and 18 brief papers offered including eight invited brief papers have been conscientiously reviewed and chosen from 121 submissions. The papers are equipped in topical sections on brokers and multiagent platforms; AI purposes and options; great information; constraint pride, seek and optimisation; wisdom illustration and reasoning; computer studying and information mining; social intelligence; and textual content mining and NLP.
The court cases additionally includes 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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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. In: Proceedings of the 8th Dutch Conference on Artiﬁcial Intelligence, pp.
Consider an argument a in an argumentation framework and the naive semantics. 1. a is in at least one naive extension iﬀ a is not self-defeating 2. a is in every naive extension iﬀ the only arguments that attack or are attacked by a are self-defeating 3. a is unrejected in every naive extension iﬀ the only arguments that attack a are self-defeating 4. a is uncontested iﬀ a is not self-defeating, and the only arguments that attack a are self-defeating It follows from this lemma that the aim veriﬁcation problem under the naive semantics can be solved in polynomial time for each of the aims.
Blackwell’s approachability in stackelberg stochastic games: a learning version. In: 53rd IEEE Conference on Decision and Control, pp.
AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings by Byeong Ho Kang, Quan Bai