CNS 2019

Conversation is an essential social activity for humans. Intelligent systems that can handle natural language are actually deployed on personal or home devices or embedded in humanoid robots, and try to enable social interactions between computers and humans in order to assist, enable, or entertain. Even though they have significantly improved their abilities in recognizing and synthetizing speech, they often fail to meet expectations of having a real dialogue with humans. Indeed, they essentially try to provide a more or less adequate imitation of how humans converse, bringing the interlocutor into stereotyped conversations, but without developing real understanding of language or learning and using robust and meaningful representations of physical concepts, objects and events of the external world. This workshop is intended to provide an overview of the research being carried out in the areas of Cognitive and Neural systems designed to acquire capabilities for language learning, understanding, production and grounding, interactively or autonomously from data, also operating on portable and embedded devices or with preliminary quantum computing developments. To this aim, the workshop aims to gather researchers with broad expertise in various fields — computer vision and natural language processing, cognitive science and psychology, artificial intelligence and robotics, computational modeling and neuroscience — to discuss their cutting edge work in these exciting areas with original contributions covering the whole range of theoretical and practical aspects, technologies and systems.
Researchers are encouraged to submit original research contributions in all major areas, which include, but not limited to:

* Natural Language Understanding
* Natural Language Generation
* Natural Language Grounding
* Computer and Cognitive Vision
* Conversational Systems/Interfaces
* Human-like Reasoning and Adaptive Behavior
* Computer/Human Interactive Learning
* Computer/Human Interactive Learning
* Search and Information Retrieval
* Reinforcement Learning
* Machine/Deep Learning from huge amounts of heterogeneous data
* Emotional Intelligence
* Neuroscience-Inspired Cognitive Architectures
* Trustworthy and Explainable Artificial Intelligence
* Cognitive and Social Robotics
* Quantum Machine Intelligence
* Intelligence on portable and embedded devices
* Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication, Multimedia