Artificial Intelligence

Presentation The course covers the central themes involved in the interaction with intelligent agents through the use of natural language, with emphasis on dialogue and language generation. We will also study planning techniques applied to the theory of speech acts and the use of rhetorical structures, both for controlled dialogues as for dynamic and non-cooperative dialogues. Regarding analysis and generation of language, students will learn robust and incremental techniques capable of dealing with partial, and even ungrammatical discourse, as it's typical of spontaneous dialogues. We will also look at the design of dialogue architectures, and analyze the use of dialogue in "chatbots" and videogames. The course also covers spoken interaction including aspects on automatic speech recognition, automatic speaker recognition, and text-to-speech synthesis. Associated skills The course contributes to the basic and advanced skills and expertise acquired during the master studies on Intelligent Interactive Systems: • The capacity to collect and interpret relevant data in the area of Computer Science and Artificial Intelligence in general and Natural Language-based Human-Computer Interaction in particular in order to be able to assess and comment on relevant topics from the scientific, ethical and social points of view. • The capacity to communicate information, ideas, problems and solutions in the area of Natural Language-based Interaction to general public and NLP scholars alike. • The capacity to apply the acquired skills in order to build operational conversational agent prototypes. Furthermore, the course contributes to transversal skills related to CE1. Solving the mathematical problems which can be set out in the rise in engineering and applying the knowledge on: linear algebra; differential and integral calculus; numerical methods, numerical algorithms, statistics, and optimization. CE8. Mastering the concepts of data programming and programming and data structures, including principles of secure design and defensive programming, program verification and error detection. CE10. Recognizing basic algorithmic procedures and applying them for the resolution of computational problems, analyzing the solutions suitability and complexity. CE11. Solving complex computational problems using the principles and techniques of intelligent systems. Learning outcomes It is expected that the students will obtain knowledge about state-of-the-art NLP techniques and acquire the skills to both integrate publicly available off-the-shelf modules into applications and develop on their own simple applications that use state-of-the-art techniques. In particular: RA.CE1.5 Using knowledge of statistics to solve problems which can be set out in engineering. RA.CE8.3 Designing and using advanced data structures and the most proper suitable algorithms for solving aproblem. RA.CE10.3 Applying basic techniques of artificial intelligence. RA.CE11.2 Solving complex problems using machine learning techniques. RA.CE11.3 Applying advanced intelligent computation techniques for the design and development of intelligent applications.