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Titlebook: Agents and Artificial Intelligence; 11th International C Jaap van den Herik,Ana Paula Rocha,Luc Steels Conference proceedings 2019 Springer

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Segmentation, Targeting, and Positioningnt weighting functions for approximating the implicit feedback relation weights. Experiments on four real-world datasets show that the proposed model significantly outperforms the state-of-art models. Results also show that selecting the right weighting functions for approximating relation weights significantly improves classification accuracy.
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The Intelligentsia in War and Revolution,hm to Berkeley’s Pac-Man environment. Our algorithm considerably outperforms Deep .-Networks both in terms of learning speed and ultimate performance, showing its potential for boosting existing algorithms. Furthermore, it is robust to the failure of one of its components.
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Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights,nt weighting functions for approximating the implicit feedback relation weights. Experiments on four real-world datasets show that the proposed model significantly outperforms the state-of-art models. Results also show that selecting the right weighting functions for approximating relation weights significantly improves classification accuracy.
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Wide and Deep Reinforcement Learning Extended for Grid-Based Action Games,hm to Berkeley’s Pac-Man environment. Our algorithm considerably outperforms Deep .-Networks both in terms of learning speed and ultimate performance, showing its potential for boosting existing algorithms. Furthermore, it is robust to the failure of one of its components.
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Reports from Contemporary Businessting optimal algorithms for MAPF: search-based CBS, and propositional satisfiability (SAT) - based MDD-SAT and SMT-CBS. These algorithms were modified to tackle various types of conflicts. The major contribution of this paper is a thorough experimental evaluation of CBS, MDD-SAT, and SMT-CBS on various types of relocation problems.
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