Using ESTA to develop expert system for the Natural Resource Management

Using esta to develop expert system for the Natural Resource Management

AI people to realize the fact that the quality of knowledge highly influences the quality of expert system, which in turn led to the separation of knowledge module from inference and control strategies. This gave a great leap in the expert system technology and resulted in the development of expert system shell. An expert system in the context of expert system shell may be defined as follows:

EXPERT SYSTEM=KNOWLEDGE BASE + EXPERT SYSTEM SHELL (Control &Inference +User Interface).

The separation of the control and inference parts in the knowledge base is a fundamental feature of an expert system shell. ESTA is also an expert system shell developed by PDC (Prolog Development Center), Denmark. By providing with it a knowledge base, as shown in Fig.4, of a specific domain an expert system can be created. Thus

KNOWLEDGE BASE +ESTA= EXPERT SYSTEM.

ESTA system has several advantages over other expert system shell like CLISP. According to PDC, ESTA is easy to use and a great stand-alone environment for constructing advisory and decision making systems.

Building advanced knowledge base with ESTA requires no previous programming experience and it is suitable for many problem domains. ESTA is the perfect tool for structuring of knowledge. It includes explanation facilities for the

Fig.1. Expert system shell to expert system
question being asked and for given advice. Details about ESTA can be found in the reference [15,16] or at the homepage of PDC. The knowledge base of the ESTA. consists of rules, in forward or back chaining, represented in its own syntax. It has all the inbuilt facilities to write the rules that build the knowledge base from if then rules written in high-level language.

1 Knowledge Base Development:
As shown in the Fig.1, for a knowledge engineer expert system shell ESTA works as an excellent tool to create knowledge base and from user side it is used to consult the created knowledge base. Palpably, the task of developing expert system using ESTA, a domain independent module, is centered at developing a domain specific knowledge base, which is a multi-step process. These steps are problem identification, knowledge acquisition and knowledge representation in accordance with the syntax of ESTA.

2 Problem Definition and Expertise Modeling
IRS-1B Collects ground data using solid state LISS (Linear Imaging and Self-scanning Sensor)-I & II sensors in four bands. The used four bands are B1-0.42-0.52(Blue), B2-0.52- 0.59(Green),B3-0.62-0.68(Red), B4-0.77-0.86(near IR).Standard FCC image is produced by assigning blue to B2,green to B3 and red to B4 band. The problem of visual interpretation of FCC image can be considered as a diagnostic problem solving. As mentioned earlier a human expert identifies land use and land cover in the FCC image based on certain symptoms or features (shape size, pattern, tone, texture, shadow, site etc) related to basic picture element. Diagnostic problem solving [17] is knowledge intensive process. Basically in the diagnostic problem solving an expert is exposed to situation and is asked to find what is wrong and how it can be mitigated. A human expert, to does so, recognizes patterns of problems in the data elicited from the situation, provides diagnoses and suggests remedy if there is. Thus in a human expert diagnostic expertise can be characterized as

ability to solicit appropriate situation information
ability to recognize specific patterns and their interaction in the elicited information

Obviously the process of diagnosis requires procedural as well as heuristic diagnostic knowledge. The computer based diagnostic problem solving e.g. one addressed in this paper, requires these cognitive formalities to be implemented. Diagnostic problem solving has been one of the areas of active research in the applied AI right from the evolution of the expert system technology and diagnostic problem solving program has been the first expert system. Since then various theories have been proposed to formalize and capture the notion of diagnostic problem solving expertise and have been implemented in different diagnostic computer programs. Reviewing the related literatures it can be concluded that there is no unique theory of.Map India 2003,6 th Annual International Conference and Exhibition, NewDelhi, India 6 diagnosis, different formalization and approaches have been adopted to solve different problems, however in all theories diagnostic problem solving has been formalized in the process of abduction, deduction and induction in the light of hypothetical reasoning [18]. This has been shown in Fig.2.In the abductive phase human expert generate candidate solution hypothesis from abducted observation using empirically proven association between observation and solution. In the deduction phase necessary consequence are deduced. In induction phase generated hypothesis is rejected or accepted and the whole cycle continues until termination. In the deductive inductive phase deep domain knowledge is exploited to test the candidate hypothesis with respect to diagnostic knowledge and observed findings. All those hypotheses passing the test are accepted as diagnoses and failure in test or test abortion rejects the hypotheses. The important conceptual models of diagnosis proposed so far are have been based on the type of used domain knowledge and are termed as model based approach, heuristic diagnosis, set covering theories etc. The choice of particular diagnostic framework depends on problem domain and knowledge to be used. Model based approach of diagnosis exploits explicit models of problem domains. Such models include knowledge of structural, functional, causal interactions among the modeled objects. Heuristic reasoning uses empirical classification rules elicited from the domain expert. It is logical deduction in data driven approach.

Fig. 2. An epistemological model of diagnostic reasoning
Here we have used hyothetico-deductive approach [19] to formalize knowledge of visual photo interpretation. Based on some basic abstraction system first forms hypothesis about different land use and land cover categories and then goes on search for features in details for a particular category. Here associative knowledge about landforms and features is matched to do classification. This is shown in the Fig.3.

Fig.3: Hypothetico-Deductive diagnosis framework

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