Behavioral design to model a reactive decision of an expert in geothermal wells

Abstract

Software design based on agents represents a new perspective for computer science and more specifically, for Artificial Intelligence. It is a new theory that has innovated the analysis, design and implementation of system software. The design of agents poses problems related with: (1) autonomous decision-making process, (2) co-ordination, (3) negotiation, and (4) handling of mental states and communication. In a reactive multi-agent system, the group of agents is subject continually to local changes. These changes are designed by means of behavior rules whose results are influenced by the behavior of the rest of the agents. The design of these rules is inspired by the biological or cognitive sciences. Particularly, the design of cognitive rules corresponds with the principle of rationality; its perspective is focused on the interaction among the agents. One of the objectives of artificial intelligence refers to the development of systems that ease or increase the level of comfort in the daily life of humans. Such is the case for tasks with permanent focus on the input data in convergent methods or systems that help in the decision-making process involved in costly processes. In this paper we propose a design’s of the expert’s decision-making process trough the use of a cognitive model, and fuzzy sets to model the agents’ reactive deliberative process. Software system helps human expert in the estimation of the static formation temperatures. Furthermore, we will present an example based on a behavior developed from an expert in the field of geothermal sciences. The formulation of the human expert knowledge includes uncertainty, which is expressed in terms of fuzzy rules. An attempt to estimate formation temperatures from logged temperatures was solved whit this methodology based on reactive decision model. Thus, mathematically speaking an inverse problem is solved in this way. This paper describes and discusses the first experiences that form part of an incremental project whose final objective is to develop an expert system that allows the prediction of the degree of success of the drilling of geothermal wells.