BESAI is a mainly-European working group of Artificial Intelligence scientists and Environmental Science scientists who try to create an interdisciplinary knowledge for the solution of environmental problems and the development of better AI techniques. In fact, in any software system for real-world applications, several sources of information ought to be combined in a common framework to provide a realistic and useful knowledge base.

The issues and techniques BESAI works on are:

        Basic Environmental issues:

• Aquatic Environmental Sciences
  • Marine Pollution
  • Water Management
  • Waste Management
• Atmospheric Environmental Sciences
  • Air Pollution
  • Meteorological Prediction
  • Waste Management
• Terrestrial Environmental Sciences
  • Forest Management
  • Waste Management
• Environmental-Process Supervision
• Biodiversity
• Ecological-System Modeling and Simulation
• Environmental-Crime Detection
• Environmental Data Base Management
• Environmental Impact Assessment
• Global Climatic Change
• Natural Resource Management
• Remote Sensing
• System Ecology

        Artificial Intelligence approaches and techniques:

• Autonomous Agents
• Belief, Bayesian and Possibilistic Networks
• Case-Based Reasoning
• Constraint-Based Reasoning
• Distributed Problem Solving
• Genetic Algorithms
• Heuristic Search
• Image Processing
• Internet-Based Information Systems
• Knowledge-Based Systems
• Knowledge Discovery and Data Mining
• Machine Learning
• Ontologies
• Optimization in Engineering Design
• Robotics
• Signal Processing

The first and founding workshop of BESAI (ECAI'98-BESAI) was within the European Conference on Artificial Intelligence (ECAI'98), held in Brighton, U.K. (August, 1998).
You can find here the papers presented at the workshop.

That was the occasion for binding common interests and research lines. Through cooperation we will try to get results that are more than a mere sum of members' efforts. Funding stuff is left to each research group, even if the creation of large EU-funded projects is likely.

Updated January 31, 2000