Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence

TL;DR

This paper explores hybrid AI systems for environmental modeling, combining multiple techniques to create more advanced, intelligent systems capable of incorporating human decisions and handling complex environmental problems.

Contribution

It introduces the concept of hybrid AI mechanisms for environmental systems, emphasizing their potential to improve complexity and intelligence in modeling.

Findings

Hybrid AI techniques enhance environmental modeling capabilities.

Combining methods allows for more complex and intelligent systems.

Incorporating human decisions improves system adaptability.

Abstract

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which two or more methods can be combined which seems to be a positive effort for creating a more complex; advanced and intelligent system which has the capability to in- cooperate human decisions thus driving the landscape changes.