InteLLigence
 



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Welcome
to the web site of the Intelligent Systems Laboratory (InteLLigence).

The Intelligent Systems Laboratory (InteLLigence) is a unit of the School of Electrical and Computer Engineering, Technical University of Crete, Chania, Crete, Greece.

The role of this laboratory is to educate undergraduate and graduate students in the concepts and techniques of modern Intelligent Systems and to carry out cutting-edge research in this area. Current research work spans many areas, such as Multimedia and Web information systems, Semantic Web, Machine Learning, Robotics, Bioinformatics, Computer vision, Peer-to-Peer computing and Intelligent Agents.

The laboratory is funded by the Technical University of Crete and various Greek and European funding institutions.

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The Intelligent Systems Laboratory (InteLLigence) maintains long experience on information systems technology with emphasis on the management (processing, extraction, dissemination and retrieval) of multimedia information in (multimedia) databases and the Web. As such, information is not considered only from a standalone perspective; its strength lies in its potential association with other modern technologies, such as Web, Semantic Web, Content-based approaches, P2P systems and Cloud Computing. Emphasis is placed on content technologies and evolving context-aware service provision that enable information systems to fulfil the ever-evolving user interests and needs in fast-evolving and distributed environments. The InteLLigence laboratory also maintains expertise on content extraction from multimedia information through the application of natural language processing, text analysis, signal, image and video analysis and their application to content-based access services. Recent research results have led to the design of innovative tools exploiting leading-edge technology in knowledge management (e.g., tools facilitating development of spatio-temporal ontologies, spatio-temporal reasoning and querying).

In addition, the Lab possesses considerable expertise in Artificial Intelligence and Multiagent Systems – and, more specifically, in the intersections of multiagent learning, decision making under uncertainty, and game theory. Lab members have published research papers and developed software systems applying this expertise on various real-world settings, such as online games (e.g., the “Fantasy Football” online community), e-marketplaces and sponsored search settings (see, e.g., our TUCTAC Ad Auctions agent's webpage), and the energy domain. In particular, lab members have in recent years been quite active in research contributing to implementing the vision of the so-called “Smart Grid” – that is, creating robust, intelligent electricity supply & distribution networks, to achieve the highest energy efficiency possible.

Machine Learning research in the lab spans various aspects, such as clustering and classification, however emphasis is placed on reinforcement learning, whereby an autonomous agent learns how to act rationally in an unknown environment through trial and error. Finally, the lab focuses on Autonomous Robotics with an emphasis on probabilistic methods for coping with the uncertainty of the real world and on the development of efficient algorithms and software for on-board information processing. Our lab is home to the RoboCup team “Kouretes” (www.kouretes.gr), which competes in the RoboCup Standard Platform League since 2006 and has won several international distinctions.

InteLLigence is constantly aiming at strengthening its expertise and, and through this, its recognition in the EU arena as a centre of excellence for research. This can be achieved through its collaboration with the EU industry and academia and as well as through its participation in high-calibre EU funded research. InteLLigence, currently participates as partner in the project “RT3S: Real Time Simulation for Safer vascular Stenting” (ICT for patient safety, FP7-STEP, project number 248801, www.rt3s.eu), in the project Fi-Star: “Future Internet Social and Technological Alignment Research” (FI.ICT-2011.1.8, FP7-IP:604691, 2013-2015, www.fi-star.eu) of the EU, and in the project NOPTILUS: autoNomous, self-Learning, OPTImal, and compLete Underwater Systems (FP7, INFSO-ICT:270180, 2011-2015, www.noptilus-fp7.eu). InteLLigence has also participated in many research and R&D projects in the past, such as HIPER-BE97:5084, OPTAG-FP6-STREP:502858, BIOPATTERN (FP6-NoE:508803), in many R&D projects funded by the EU and he Greek government and has coordinated and executed successfully project TOWL: “Time-determined ontology based information system for real time stock market analysis” (FP6-STREP:026896, 2006 - 2008).

The group at InteLLigence laboratory consists of three faculty members, four post-doctoral researchers, six Ph.D. candidates, more than 15 M.Sc. students and several senior undergraduate students pursuing their diploma theses. The group members have published one graduate-level scientific textbook, and hundreds of articles in peer-reviewed, high-quality, international journals and conferences. Several articles have received best paper awards.



Recent News

Best Student Paper award at KSEM-2019!

2019-08-30
The paper "Extracting hidden preferences over partitions in hedonic cooperative games" (http://tiny.cc/y5dzbz ) received the Best Student Paper award of the 12th International Conference on Knowledge Science, Engineering, and Management (https://ksem2019.unipi.gr/)!

We are co-organizing the First AAAI Workshop on AI in Team Sports @AAAI-2020, New York, USA (February 2020)

2019-08-28
Sports is a domain that has grown significantly over the last 20 years to become a key driver of many economies. According to a recent report, the estimated size of the global sports industry is $1.3 trillion, and has an audience of over 1 billion. As the market has grown so has the amount of data that is collected. This means that there are a number of challenging problems in sports to predict and optimise performance but, so far, such problems have largely been dealt with by domain experts (e.g., coaches, managers, scouts, and sports health experts) with basic analytics. The growing availability of datasets in sports presents a unique opportunity for the artificial intelligence (AI) and machine learning (ML) communities to develop, validate, and apply new techniques in the real world. In team sports, real-world data is available over long periods of time, about the same individuals and teams, in a variety of environmental contexts, thereby creating a unique live test-bed for AI and ML techniques. While research in AI for team sports has grown over the last 20 years, it is as yet unclear how they relate to each other or build upon each other as they tend to either focus on specific types of team sports or specific prediction and optimisation problems that are but one part of the whole field. Hence, this workshop will help fuel discussions in the area and bring together the AI and sports analytics communities to encourage new research that will benefit both communities and industry.

Demonstration paper competing for an IBM-funded Best Demonstration Paper Award at PAAMS-2019

2019-03-16
The demonstration paper "AncientS-ABM: A Novel Tool for Simulating Ancient Societies" (https://goo.gl/HTAasa) was accepted for presentation at the 17th International Conference on Prestigious Applications for Multi-Agent Systems and will be competing for a 2,000 euros IBM-funded Best Demonstration Paper Award.


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