Artificial Intelligence and Machine Learning: Communications in Computer and Information Science
Editat de Cristiana Teixeira Santos, Dragan Doder, Sonja de Zwarte, Wilson Silvaen Limba Engleză Paperback – 28 oct 2026
This book constitutes the revised selected papers of the 36th Benelux Conference on Artificial Intelligence, BNAIC/ Benelearn 2024, held in Utrecht, The Netherlands, during November 18-20, 2024.
The 13 full papers included in this volume were carefully reviewed and selected from 58 submissions. They focus on latest research trends in AI-related Technologies, Artificial Intelligence and Machine Learning.
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Specificații
ISBN-10: 3032316057
Pagini: 219
Dimensiuni: 155 x 235 mm
Editura: Springer Nature Switzerland AG
Colecția Communications in Computer and Information Science
Seria Communications in Computer and Information Science
Cuprins
.- Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution.
.- Reinforcement Learning of Action Sequences in Table Football.
.- Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards.
.- Nested Monte-Carlo Search for Scheduling an Automated Guided Vehicle in a Blocking Job-Shop.
.- What To Do Next? A Comparative Study of Human and Rational Decision-Making.
.- Empirical Capacity Model for Self-Attention Neural Networks.
.- Non-Parametric Path Based Model for Taxonomy Induction in Knowledge Graphs.
.- Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division.
.- Generative AI-Based Virtual Assistant Using Retrieval-Augmented Generation: An evaluation study for bachelor projects.
.- Real-time Optimization of Industrial Processes Using Deep Reinforcement Learning.
.- Analyzing the interplay between societal trends and socio-demographic variables with local pattern mining: Discovering exceptional trends in adolescent alcohol use in the Netherlands.
.- Monotone Oblique Decision Trees.
.- High stakes for LLMs: Analysis of Bias in BERT-based Recommender Systems.