Research areas in Computer Science
(research group led by Prof. Nicola Leone).
The Artificial Intelligence group is chaired by Nicola Leone, full professor in Computer Science. It is composed of 3 associate professors, each of them sub-chairing the activities of the group within a specific area of interest, of 5 assistant professors, and of about 10 post doc and phd students. In fact, it is the largest group in the Department. Members of the group have received prestigious international awards, such as the PODS Test-Of-Time Award and the IJCAI-JAIR best paper award.
The areas of interest of the Artificial Intelligence group are the following ones:
Artificial Intelligence and Knowledge Representation
(area chaired by Prof. Giovambattista Ianni)
The development of automated reasoning modules embedded within heterogenous software systems, mobile and traditional devices is nowadays a necessity. Also, in this context, it is important to strive for the best performance, and achieve a satisfactory management of knowledge sources with different semantics. In this respect the group investigates toward the usage and the extension of Answer Set Programming (ASP) in the following respects:
- Extensions of ASP meant to deal with external, dynamically changing environments and/or sources of knowledge;
- Performance improvement of ASP solvers, specifically tailored at incremental evaluation and domain-oriented knowledge sources;
- Deployment of knowledge-based technologies in practical contexts such as when unsupervised agents operating in unknown environments are needed, surveillance and monitoring systems, decision making over large data pools such as environmental data, digital libraries data etc..
Advanced Data and Knowledge Management
(area chaired by Prof. Giorgio Terracina)
A mounting wave of data-intensive and knowledge-based applications such as Data Mining, Data Warehousing, and Online Analytical Processing has created a strong demand for powerful languages and systems. In the literature, research efforts focused both on improving classical database systems and on providing advanced data and knowledge management features. The activities of the group in the latter field mainly focused on the study of models and evaluation paradigms supporting effective reasoning tasks. Current research interests include:
- Database oriented extensions of DLV;
- Development of optimized algorithms for logic program evaluation and querying (such as magic-sets, distributed and parallel evaluation, etc.);
- Data Integration via logic-based approaches;
- Ontology-Based Data Access;
- Process mining, which is a particular machine learning task aiming to discover, monitor and improve real processes by extracting knowledge from the event logs that are made available by today's information systems.
(area chaired by Prof. Gianluigi Greco)
Several concepts and methods of Game Theory and Economics have recently found application in modeling the strategic interactions that occur in systems of intelligent agents, both in non-cooperative contexts (where agents pursue their own individual goals) and in cooperative contexts (where they jointly pursue some common goal). The activities of the group in this field are mainly focused on the study of such models from the computational and the algorithmic viewpoints. Current research interests include:
Solution concepts for non-cooperative games, such as Nash equilibria, as a vehicle to formalize interactions among agents in competitive environments;
Solution concepts for cooperative games, such as the core, as a mathematical model suited to analyze scenarios where agents can collaborate by forming coalitions in order to obtain higher worths than by acting in isolation;
- Resource allocation protocols, such as combinatorial auctions;
- Fair allocation problems and mechanism design.