6 Credits (ECTS), Fall Semester, Spring Semester, This year the course includes two sections, the first one took place in the fall semester (see details down below) and the second one will be in the spring semester.
Schedule (spring semester): Classes are on Fridays 12:00-15:30Classes start February 26th. Class attendance is mandatory. Unless otherwise stated, classes are in classroom A3, Via Ariosto 25.
Section: Human Robot Interaction
Prof. Marc Hanheide (Univ. Lincoln)
In this seminar we will be looking at various sub-areas of human-robot interaction, ranging from general challenges and methodological foundations in HRI research, over different interaction modalities and patterns, to enabling technologies for short and long-term interaction with autonomous robots. The seminar will discuss state of the art algorithms and approaches as well as providing a "bigger picture" of the interlinking concepts of HRI.For the complete course schedule and material go to: Seminars in AI and Robotics
Students should register for 1 reading class in each section of the course. For each reading
class, where the student registered, he/she should prepare a report, due 1 week after
the class and prepared according to these guidelines.
The student will be assigned the role of presenter in one of the chosen reading
classes and the role of discussant in the other one. Each class will
have two presenters and two or more discussants. The presentation should
summarize the content of the paper. The discussant should contribute to the
discussion on the paper by providing his/her personal comments/views on the paper.
In preparing the presentation the student is invited to coordinate with
the presenter colleagues and with the teacher.
The reading class with the students' presentations will take place in the dates of the corresponding seminars by the teacher. The seminar of the teacher will be given in the first time slot, the reading class will be in the second time slot.
Students can register the exam after the acceptance of both reports.
For the registration please book through Infostud. The exam dates are:
Section: Machine Reading
Prof. David Israel (SRI International)
This section was held in the Fall Semester 2015
Class calendar (preliminary)
I happen to think that Artificial Intelligence, and indeed all of Computer Science, are rather strange and atypical sciences and it’s only right and proper that you should have some sense of why I think this. Toward the end of the discussion, we will move to the specific instance of AI approaches to Machine Reading (= Text Understanding).Recommended Readings:
Languages are not simply sets of words, but words are where we shall begin. What kind of thing is the meaning of a word? Could there be a single kind of thing that is the meaning of a word – any word? Even if not, is there a small set of ways that we can represent the meanings of words, a set of ways related in the right ways to represent the relations that we sense between the meanings of words?Recommended readings:
Overview: Ng & Zelle, from above and Turney and Pantel. From frequency to meaning: vector space models of semantics. 2010. JAIR 37: 141-188.
This material is fairly technical and we shall have to figure out how much of it to cover.Recommended readings:
So far we have largely ignored the fact that just as languages are not simply sets of words, so too sentences are not simply strings or sequences of words. While we will not be focusing (much) on syntax in the seminar, something needs to be said to allow us to distinguish between the proper treatment of (i) Marcello kissed Sophia and (ii) Sophia kissed Marcello. And here I am going to be highly partisan and present the world of parsing according to the Stanford NLP group. Further readings on parsing may be recommended; again, fair warning will be given.Recommended readings:
Language is used for many, many purposes; but in this seminar, we will focus on only one (or really on one small family of purposes): to communicate information. Given that focus, it is not surprising that we shall take as a main purpose of Machine Reading systems that they “extract” information from texts. (I admit to never having been happy with this terminology, as it rather suggests – to an American ear – enhanced interrogation techniques; but it has become fairly standard.)Recommended readings:
In the previous session, we discussed old-fashioned hand-crafted Information Extraction systems, with a bridge to the new coming by way of the Mintz et al. paper. In this session, we discuss more recent Machine Learning-based approaches.Recommended readings: