Giovambattista Ianni | University of Calabria, Italy | Wednesday, February 19th, 2020 | 14:00 s.t. | room S.1.42

Abstract:
The availability of advanced features for controlling technical aspects of grounding engines and solver modules for the answer set semantics allows great flexibility and enables the possibility to scale in otherwise out of reach applications. Nonetheless, the presence of nonobvious heuristic tuning „knobs“ deepens and widens the gap between knowledge representation technologies and software developers. In this talk, we present our recent introduction of incremental grounding techniques for answer set solvers in the perspective of videogame development, a challenging applicative domain in which design-time fast prototyping and run-time speed are at the highest priority.

Same as in-stream reasoning, videogames require repeated and fast-paced executions of decision-making tasks. In this context, we illustrate an incremental grounding approach for the answer set semantics. We focus on the possibility of maintaining incrementally larger ground logic programs; so-called „overgrounded programs“ can be generated, updated and reused transparently to the user and in combination with deliberately many different sets of inputs. The update burden of overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other.

CV: Giovambattista Ianni is a full professor of Computer Science in the Department of Mathematics and Computer Science at the University of Calabria, Italy. Prof. Ianni’s current research interests include knowledge representation, reasoning, and coupling of hybrid systems. He recently focused his research interests on Artificial Intelligence in videogames with particular attention to the issue of complex and time-consuming incremental reasoning in real-time contexts. He has contributed to the DLV system and the DLVHEX system, especially dealing with the issue of dealing with, often non-symbolic, external information to knowledge bases. He has been involved in several national and international research projects and has been acknowledged with research awards such as the ICLP Test-of-time award 2018 and the Artificial Intelligence Journal Prominent Paper Award 2013.

Björn Thór Jónsson, Associate Professor

IT University of Copenhagen, Denmark & Reykjavik University, Iceland

02.03.2020 | 10.00 | S.2.42

Abstract:

Scalable retrieval of high-dimensional feature vectors is an important component of many applications in multimedia and other fields, but also a very challenging problem. In this talk, we discuss the challenges of high-dimensional indexing at scale, and then present two approximate indexing methods designed for large-scale retrieval. We present results from experiments with the two largest feature collections reported in the literature, 28.5 billion SIFT features on a single server and 42.9 billion SIFT features in a distributed setting, and demonstrate an application with interactive retrieval over the 99.2 million images of the YFCC100M collection.

CV:

Björn Þór Jónsson is an Associate Professor at the IT University of Copenhagen, Denmark, and Reykjavík University, Iceland. Björn works in the broad field of Multimedia Analytics, applying multi-dimensional analysis concepts and techniques to large-scale multimedia collections.

Previously, Björn studied scalability of multimedia retrieval, where he was involved with the two largest feature vector collections reported in the literature. Björn has a special interest in promoting demonstrations, live events, and reproducibility, e.g. serving as Reproducibility Chair for ACM Multimedia 2019 and 2020. He served as general co-chair for MMM 2017 and CBMI 2019, and will co-organize ACM ICMR 2020 and SISAP 2020.

Prof. Daniele Fontanelli (Università degli Studi di Trento) 

24.01.2020 | 09.00 | B04a.1.06 (Lakeside Park)

Abstract:

Service robots are becoming more and more pervasive in modern societies. One of the ever increasing field of application are service robots able to help seniors in their daily duties. Indeed, ageing is generally associated with a decrease in mobility and social interaction: a growing body of research suggests that reduced levels of out-of-home mobility can have widespread, detrimental effects for older adults. With the median age in Europe projected to grow from 37.7 (2003) to 52.3 (2050), the population asking for mobility aids at an affordable price is becoming substantial.

In this talk, we briefly introduce our solution conceived for autonomous mobility: the FriWalk (i.e. Friendly Walker). Stemming from this example, we will present the fundamental problems for autonomous robots, i.e. localization, planning and control, with application-related scenarios. In particular, we will focus on three aspects of the technological solutions: the localisation problem using different low-cost sensing solutions, together with an optimal landmarks placement algorithm; the set of controlled guidance solutions implementing the authority sharing paradigm and modelled as hybrid systems; the activity and reactive planning approaches in actual application scenarios.

CV:

Daniele Fontanelli received the MSc degree in Information Engineering in 2001, and the Ph.D. degree in Automation, Robotics and Bioengineering in 2006, both from the University of Pisa, Italy.  He was a Visiting Scientist with the Vision Lab of the University of California at Los Angeles, US, and an Associate Researcher with the Interdepartmental Research Center „E. Piaggio“, University of Pisa.  From 2008 he joined the University of Trento, Italy, where he is now an Associate Professor at the Department of Industrial Engineering.   He is currently an Associate Editor for the IEEE Transactions on Instrumentation and Measurement and for the IET Science, Measurement & Technology Journal.His research interests include localisation algorithms, service robotics, motion planning, human motion modelling, real-time control and estimation, and resource aware control.