Distributed Artificial Intelligence Techniques in Environmental Problem Solving

Abstract

The paper proposes use of artificial intelligence techniques through a distributed multi-agent architecture to environmental problems. In particular it is argued that machine learning techniques based on neuro-fuzzy knowledge representations, combined with heuristics are suitable for many environmental applications, while the distributed problem solving paradigm can handle effectively noisy environmental data collected through a distributed monitoring network. Performance robustness can be achieved through the proposed architecture. The developed techniques have been tested using air quality monitoring data from Athens, Greece.