76,945 research outputs found
Artificial Intelligence Technology and Ecological Transition -Analysis and Criticism-
Artificial intelligence (AI) refers to an application capable of processing tasks which are currently performed satisfactorily by human beings insofar as they involve high-level mental processes such as perceptual learning or the organization of memory (Marvin Lee Minsky, 1956). Until now, research in this field has shown a difficulty in validating and certifying artificial intelligence systems at the service of decarbonization, ecological and energy transition objectives. In this context, this article focuses on an effective analysis of 05 of today’s most popular AI technologies in the field of environment, Artificial Neural Networks, fuzzy logic, Case-based reasoning, the multi-agent system and the process of natural language. The results show that our analysis can be beneficial for developers to select the appropriate technology for a reliable and successful implementation of artificial intelligence
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences
The predominant knowledge-based approach to automated model construction,
compositional modelling, employs a set of models of particular functional
components. Its inference mechanism takes a scenario describing the constituent
interacting components of a system and translates it into a useful mathematical
model. This paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects. Firstly, it
expands the application domain of compositional modelling to systems that can
not be easily described in terms of interacting functional components, such as
ecological systems. Secondly, it enables the incorporation of user preferences
into the model selection process. These features are achieved by casting the
compositional modelling problem as an activity-based dynamic preference
constraint satisfaction problem, where the dynamic constraints describe the
restrictions imposed over the composition of partial models and the preferences
correspond to those of the user of the automated modeller. In addition, the
preference levels are represented through the use of symbolic values that
differ in orders of magnitude
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