Welcome to the site of
the research group on:

Ontologies,
Reasoning,
  
Components,
Agents and
Simulations

at CIn-UFPE.


Research Themes

People

Publications

Bi-Weekly Seminars

Bibliography

Software Tools

Links

Contact: 
orcas@cin.ufpe.br

Overview

Our research deals with the cross-fertilization between Software Engineering (SE) and Artificial Intelligence (AI). We are primarily interested in developing methodologies, CASE tools and component frameworks that speed-up the engineering of quality intelligent systems. We are also interested in incorporating advanced AI concepts into SE methodologies and tools, whenever practically relevant. The motivation for this research agenda is elaborated here.

On the SE side, we are currently working on Model-Driven Software Engineering (MDSE), Formal Software Engineering (FSE), Agent-Oriented Software Engineering (AOSE),  Component-Based Software Engineering (CBSE), Product Line Software Engineering (PLSE) and Service-Oriented Software Engineering (SOSE).

On the AI side, we are currently working on Multi-Agent Systems (MAS), Object-Oriented Logic Programming (OOLP), Inductive Logic Programming (ILP), Abductive Logic Programming (ALP), Constraint Logic Programming (CLP) and Markovian Logic Programming (MLP).

On both these sides and across them, our main interest lies in the synergetic integration of these paradigms in practical, user-friendly environments. We see such conceptual and technological synthesis as an important but often overlooked complement to the numerous research efforts that aim at specialized, in-depth advances in each paradigm. Our long term integration agenda is elaborated here

Our effort is carried out along two distinct application tracks: multi-agent simulation software and natural language generation software. In both cases, the domain of our case studies are related to digital entertainment, whether games or entertainment web sites. The specific projects that we are working on related to multi-agent simulation are described here. Those related to natural language generation are described here

 

 

Last updated: 15/04/2004 by Jacques Robin.