Prof. Jozo Dujmović
San Francisco State University, San Francisco, CA 94132, USA.
Graded Logic Aggregation
This paper summarizes basic properties of graded logic – a natural soft computing generalization of classical Boolean logic. Using graded logic aggregators we can build evaluation criteria and apply them in decision engineering. This paper is an extended summary that surveys key concepts of graded logic and graded logic aggregation.
Prof. Michio Sugeno
Emeritus Professor, Tokyo Institute of Technology, Japan
Towards Distorted Statistics based on Choquet Calculus
In this study we discuss statistics with distorted probabilities by applying Choquet calculus which we call 'distorted statistics'. To deal with distorted statistics, we consider distorted probability space on the non-negative real line. A (non-additive) distorted probability is derived from an ordinary additive probability by the monotone transformation with a generator. First, we explore some properties of Choquet integrals of non-negative, continuous and differentiable functions with respect to distorted probabilities. Next, we calculate elementary statistics such as the distorted mean and variance of a random variable for exponential and Gamma distributions. In addition, in the case of distorted exponential probability, we define its density function as the derivative of distorted exponential distribution function with respect to distorted Lebesgue measure.
Further, we deal with Choquet calculus of real-valued functions on the real line and explore their basic properties. Then, we consider distorted probability pace on the real line. We also calculate elementary distorted statistics for uniform and normal distributions. Finally, we compare distorted statistics with conventional skew statistics.
Dr. Jordi Nin
BBVA Data \& Analytics, Barcelona, Catalonia, Spain.
Assessing the risk of default propagation in interconnected sectorial financial networks
Systemic risk of financial institutions and sectorial companies relies on
their inter-dependencies. The inter-connectivity of the financial networks
has proven to be crucial to understand the propagation of default, as it
plays a central role to assess the impact of single default events in the
full system. Here, we take advantage of complex network theory to shed light
on the mechanisms behind default propagation. Using real data from the
financial company BBVA, we extract the network of client-supplier
transactions between more than 140,000 companies, and their economic flows.
In this talk , we introduce a basic computational model, inspired by the
probabilities of default contagion, that allow us to obtain the main
statistics of default diffusion given the network structure at individual
and system levels. Achieved results show the exposure of different sectors
to the default cascades, therefore allowing for a quantification and ranking
of sectors accordingly. As we will show, this information is relevant to
propose countermeasures to default propagation in specific scenarios.
Dr. Aida Valls
Universitat Rovira i Virgili, Dept. Computer Engineering and Mathematics, Tarragona
Decision making tools with semantic data to improve tourists' experiences
The offices of management of touristic destinations are interested in providing a more user-centered experience
that takes into account the personal interests of each visitor or group of visitors. Tourism is a key element
of economic wealth of many places, therefore, improving the tourism experience may have a great impact not only
on the visitor but also in the place.
In this kind of field, the objects of analysis are usually touristic activities (such as parks, museums, events,
shopping malls, routes, sports, etc). The amount of options available at each possible destination is usually
very large. Their description includes numerical data but also categorical one, sometimes provided as a list
of keywords. Exploiting the semantics of those words is crucial to understand the tourist's interests and needs.
We will present two decision aiding methods that use domain ontologies to interpret the meaning of the keywords
and help the managers and visitors to improve the touristic experience on a certain place.
Dr. Zoe Falomir
Bremen Spatial Cognition Centre, Universität Bremen, Germany.
Improving Spatial Reasoning in Intelligent Systems: Challenges
Here we tackle research on spatial thinking when facing two challenges:
(i) describing scenes in natural language, and
(ii) reasoning about perspectives for object recognition.
Regarding (i) addressing the following research questions is crucial: which kind of spatial features must intelligent systems use? Is location enough? And which kind of reference frames are suitable for communication? Deictic? Relative to the observer? Relative to the object? And what is the most salient object to describe?
Intelligent systems must have common grounding with humans so that they can align representations and understand each other.
Regarding (ii) addressing the following is decisive:
how can we improve spatial perception in intelligent systems so that they can reason about object perspectives?
Can tests done to people for measuring their spatial skills be used to model spatial logics?
On one side the challenge is to propose approaches to understand space and communicate about it as humans do.
For that, intelligent systems (i.e. robots, tablets) can use computer vision and machine learning algorithms to analyse point clouds, recognise and describe scenes.
On the other side the challenge is to propose approaches which solve spatial tests carried out to measure humans' intelligence and to apply these approaches in intelligent systems (i.e. computer games, robots) so that they can improve their spatial thinking, but also help improve humans' spatial thinking by providing them feedback.