La Página de Daniel Stolfi



6city (2018 - )

In the 6CITY project (TIN2017-88213-R) the working hypothesis is that many different problems of smart cities (according to EU: economy, mobility, governance, people, living, environment), which are multidisciplinary in nature and ​apparently unrelated, can be solved by looking at their (possibly similar) underlying quantitative and qualitative features, as well as by providing ​advanced algorithms ​that can ​search, optimize and learn ​by themselves (to an extent) for those situations where knowledge of the problem is very limited (as it happens in many real cases). Read more...


MoveON (2015 - 2018)

The MOVEON project (TIN2014-57341-R) makes an ambitious proposal for focused research in challenges related to intelligent transport and smart mobility. We do it from the perspective of building new applications based in solver metaheuristic engines enhanced with methodologies and theories contributed by our team so as to exhibit "holistic intelligence". Read more...


MAXCT (2015)

The MAXCT project (OTRI # 8.06/5.47.4356 - AOP GGI3003IDI) consists of two parallel applications are running to improve the traffic flow in a city: HITUL - Holistic Intelligence for Traffic Urban Lights - system offers an informed support for decision-making at city level, optimizing the planning of the existing traffic lights network; and CTPATH is our second tool in this project. Drivers could receive route advices based in their preferences and city conditions, both powered to reduce the travel times and the carbon footprint. Read more...


RoadME (2012 - 2014)

The roadME project (TIN2011-28194) intends to characterize, design, and evaluate metaheuristic techniques able to solve real world problems, and then, particular applications of a communication network of vehicles (VANET). Our hypothesis is that standard metaheuristics are not able by themselves to address the tight requirements of many complex problems like this one, since we are dealing with execution times of a few seconds, specific user constraints, scalable to very big dimension problems, and robust to work in different scenarios. Read more...


PATIO (2010 - 2012)

The Project PATIO: Collaborative Learning and User Modelling Techniques Applied to Multicultural Integration (TIC-4273), has been developed by the group of Investigation and Application of Artificial Intelligence of the department of Computer Languages and Computation of the University of Málaga, subsidized by the Ministry of Innovation, Science and Business of the Andalusian Regional Government (2008 official announcement for excellence Projects). Read more...

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