By Byeong Ho Kang, Quan Bai
This publication constitutes the refereed court cases 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 provided including eight invited brief papers have been rigorously reviewed and chosen from 121 submissions. The papers are prepared in topical sections on brokers and multiagent structures; AI purposes and thoughts; huge information; constraint pride, seek and optimisation; wisdom illustration and reasoning; desktop 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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Extra info for AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings
Nguyen et al. a very large number of agents . Another challenge of RL-based algorithms is the ineﬃcient of exploration. Since agents running RL procedure do not have a global knowledge of the whole system, they often require a high exploration times in order to converge to a stable equilibrium. In many application, these behaviours can result in undesirable outcomes [4,7]. This paper develops a new RL procedure that follows the regret-based principles [3,8] to overcome the disadvantage of slow speed and ineﬃcient convergence of standard RL solutions.
Answer set programming: a primer. A. ) Reasoning Web 2009. LNCS, vol. 5689, pp. 40–110. Springer, Heidelberg (2009). 1007/978-3-642-03754-2 2 5. : Simulation-based general game playing. D. thesis, School of Computer Science, Reykjavik University (2012) 6. : General game playing: overview of the AAAI competition. AI Mag. 26(2), 62–72 (2005) 7. : General Game Playing. Synthesis Lectures on Artiﬁcial Intelligence and Machine Learning. Morgan & Claypool Publishers, San Rafael (2014) 8. : Automated Planning: Theory & Practice.
The Desired Outcome problem for P with any of the aims under the ideal semantics is Σ2p -complete. The same complexity holds for O. Theorem 3. The Winning Sequence problem for P with any of the aims under the ideal semantics is Σ3p -complete. Theorem 4. The Winning Strategy problem for P with any of the aims under the ideal semantics is PSPACE-complete. p The complexity of honestly playing strategic argumentation is PTIMEΣ2 = p Δ3 , using Theorem 2. Consequently, we see that strategic argumentation under the ideal semantics is resistant to both collusion and espionage.
AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings by Byeong Ho Kang, Quan Bai