Artificial Intelligence 5th RAAI Summer School, Dolgoprudny, Russia, July 4-7, 2019, Tutorial Lectures /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Osipov, Gennady S. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Panov, Aleksandr I. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Yakovlev, Konstantin S. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Lecture Notes in Artificial Intelligence ; 11866
• Θέματα: Artificial intelligence.; Computers.; Software engineering.; Artificial Intelligence.; Information Systems and Communication Service.; Software Engineering/Programming and Operating Systems.; Theory of Computation.

This volume contains selected tutorial and young scientist school papers of the 5th RAAI Summer School on Artificial Intelligence, held in July 2019 at Institute of Physics and Technology (MIPT) campus in Dolgoprudny, a suburb of Moscow, Russia. The 11 chapters in this volume present papers focusing...

Πίνακας περιεχομένων:

• Hybrid Intelligent Systems Based on Fuzzy Logic and Deep Learning
• Data Science: Similarity, Dissimilarity and Correlation Functions,- Mathematical Foundation of Cognitive Computing Based Artificial Intelligence
• A Review of Motivational Systems and Emotions in Cognitive Architectures and Systems
• Selected Challenges in Grammar-based Text Generation from the Semantic Web
• Multi-Agent Path Finding - An Overview
• The Use of Reinforcement Learning in the Task of Moving Objects with the Robotic Arm
• Ontology Models in Intelligent System Engineering: A Case of the Knowledge-intensive Application domain
• Automated Acquisition, Representation and Processing of Temporal Knowledge in Dynamic Intelligent Systems
• Natural Language Processing with DeepPavlov Library and Additional Semantic Features
• Toward Faster Reinforcement Learning for Robotics: Using Gaussian Processes. .