Titlebook: Agents and Artificial Intelligence; 12th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2021 Springer

完整

Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation for planning applications. Our experiments show that the technique is competitive with state-of-the-art domain-independent heuristic. We also introduce a theoretical framework to formally analyze behaviour of learned heuristics. We state and prove several theorems that establish bounds on the worst-case performance of learned heuristics.

音乐戏剧

Trading Bias for Expressivity in Artificial Learninghey cannot also be highly expressive. Conversely, maximally expressive algorithms necessarily have performance no better than uniform random guessing. We establish that necessary trade-offs exist in trying to design flexible yet strongly performing learning systems.

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ETHER

薄膜

gerrymander

Archiving the Memory of the Holocaust parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.

过于光泽

Shengyu Jian,Xin Chen,Jiaping Yaneviously published results, with simple strategies such as SHVR outperforming more complex adaptive algorithms such as AA. We also show that adding a . parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a sense of urgency that significantly improves profitability.

Antecedent

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