By Xiaoxin Jing, Dongmo Zhang, Xudong Luo (auth.), Stephen Cranefield, Abhaya Nayak (eds.)
This publication constitutes the refereed lawsuits of the twenty sixth Australasian Joint convention on man made Intelligence, AI 2013, held in Dunedin, New Zealand, in December 2013. The 35 revised complete papers and 19 revised brief papers provided have been rigorously reviewed and chosen from one hundred twenty submissions. The papers are geared up in topical sections as brokers; AI functions; cognitive modelling; computing device imaginative and prescient; constraint pride, seek and optimisation; evolutionary computation; online game enjoying; wisdom illustration and reasoning; computer studying and information mining; ordinary language processing and knowledge retrieval; making plans and scheduling.
Read Online or Download AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings PDF
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Additional resources for AI 2013: Advances in Artificial Intelligence: 26th Australasian Joint Conference, Dunedin, New Zealand, December 1-6, 2013. Proceedings
Decision Science 21, 633–641 (1990) 2. : Knowledge matters–the eﬀect of tactical descriptions on negotiation behavior and outcome. Journal of Personality & Social Psychology 70(6), 1205–1217 (1996) 3. : Eﬃcient mechanisms for bilateral trade. Journal of Economic Theory 29, 265–281 (1983) 4. : Strategic delay in bargaining with two-sided uncertainty. Review of Economic Studies 59, 205–225 (1992) 5. : Mediation: incomplete information bargaining with ﬁltered communication. Journal of Mathematical Economics 39(7), 803–830 (2003) 6.
In the ﬁrst round of the negotiation, both agents play tough with probability 1, regardless of their types. 8. 6. Note that, as the weak-type agents do not play P S with probability 1, full eﬃciency is not achieved. On the other hand, consider a biased surplus division scheme in which the agent who makes a concession in the second round of the negotiation will be rewarded by getting more than half of the obtainable surplus. B. 5% of the surplus, we could get the equilibrium strategy of the weak-type agent to play soft (P S) with probability 1 in the second round.
Ma et al.  extend the model of Strat  to form a framework of security games with ambiguous payoﬀs. Its key point is that a point-valued preference degree for a choice of strategy can be calculated from an interval-valued expected utility that Strat  deﬁnes. However, Ma et al.  do not deal with ambiguous types of the attacker. Based on the ambiguity decision framework [11–13], this paper proposes our ambiguous games model to handle ambiguous information in security games. More speciﬁcally, in our model, the belief about the attacker’ types is ambiguous and modeled by mass functions [9, 10].