Titlebook: Artificial Intelligence; 29th Benelux Confere Bart Verheij,Marco Wiering Conference proceedings 2018 Springer International Publishing AG,

得意牛

Conference proceedings 2018lands, in November 2017.?.The 11 full papers presented in this volume were carefully reviewed and selected from 30 submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent i

骯臟

Learning-Based Diagnosis and Repair,it does not depend on collaboration with other agents. The proposed learning based diagnosis approach may therefore provide an incentive to collaborate in the execution of tasks, and in diagnosis if tasks are executed in a suboptimal way.

善于

Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments, consultation with the domain expert. As the knowledge elicitation involved is known to be time-consuming, it is of value to (partly) automate this step. We propose a refinement of the heuristic to this end, which specifies the directions in which arcs are to be set given specific conditions on structured arguments.

legitimate

,Beitr?ge zur Kenntnis des Fleischextraktes,he need to react requires more than the agents can contribute to acting, rendering the reciprocation imperfect. We prove that even then, each interaction converges and the corresponding game has an equilibrium.

Resection

,über den Wassergehalt im Schweineschmalz,ely on slightly different brain areas. While the accuracy of decision decoding can still be improved, these initial studies demonstrate the power of decoding analyses for testing computational models of cognition.

暗指

Fibrillation

Tracking Perceptual and Memory Decisions by Decoding Brain Activity,ely on slightly different brain areas. While the accuracy of decision decoding can still be improved, these initial studies demonstrate the power of decoding analyses for testing computational models of cognition.

農(nóng)學(xué)

翻動

ynamically establishing the size of an ensemble and for weighting the individual models. The performance is evaluated using three base-learners and 16 datasets. We show that our algorithms consistently outperform single optimized models and can outperform or match the performance of state of the art ensemble generation techniques.

伸展

work (LSTM) that can find time-dependent relationships. By combining a CNN with an LSTM we observe a strong performance improvement. The technique that performs best is a multi-layer perceptron, with as input the bag-of-words model.