By Tim Baarslag, Mark Hendrikx, Koen Hindriks, Catholijn Jonker (auth.), Michael Thielscher, Dongmo Zhang (eds.)

This publication constitutes the refereed court cases of the twenty fifth Australasian Joint convention on synthetic Intelligence, AI 2012, held in Sydney, Australia, in December 2012. The seventy six revised complete papers awarded have been rigorously reviewed and chosen from 196 submissions. The papers deal with a variety of brokers, purposes, computing device imaginative and prescient, constraints and seek, video game enjoying, details retrieval, wisdom illustration, computer studying, making plans and scheduling, robotics and uncertainty in AI.

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Extra info for AI 2012: Advances in Artificial Intelligence: 25th Australasian Joint Conference, Sydney, Australia, December 4-7, 2012. Proceedings

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Opponent models can aid in identifying these rare acceptable bids, thereby preventing break-offs and unnecessary concessions. Nevertheless, if the opposition is high, then the bids are also relatively closer to the Pareto-optimal frontier, which renders it more difficult for an opponent model to make a significant impact on the negotiation outcome. Despite this effect, we expected that higher opposition would lead to higher performance gain. 262 for the perfect model. Based on these results we are unable to draw a conclusion, which leads us to believe the two mentioned effects cancel each other out, making the other two characteristics of the scenario decisive in the effectiveness of a model.

In the deterministic case we also prove finite error bounds. Optimism has previously been used to design exploration strategies for both discounted and undiscounted MDPs [KS98, SL05, AO06, LH12], though here we define optimistic algorithms for any finite class of environments. M. Thielscher and D. ): AI 2012, LNCS 7691, pp. 15–26, 2012. c Springer-Verlag Berlin Heidelberg 2012 16 P. Sunehag and M. Hutter Related Work. Besides AIXI [Hut05] that was discussed above, [LH11a] introduces an agent which achieves asymptotic optimality in an average sense for the class of all deterministic computable environments.

Universal Articial Intelligence: Sequential Decisions based on Algorithmic Probability. : Discrete MDL predicts in total variation. In: Advances in Neural Information Processing Systems, NIPS 2009, vol. 22, pp. : Near-optimal reinforcement learning in polynomial time. In: Proceedings of the 15nd International Conference on Machine Learning (ICML 1998), pp. : Asymptotically Optimal Agents. , Zeugmann, T. ) ALT 2011. LNCS, vol. 6925, pp. 368–382. : Time Consistent Discounting. , Zeugmann, T. ) ALT 2011.

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