Introduction:

Philippine coastal areas are threatened by resource depletion, environmental degradation and poverty. With 54% of all municipalities within the coastal zone, an increasing population correspondingly exerts a direct proportional pressure on the coastal resources to produce the required food supply. The vast expanse of the Exclusive Economic Zone (EEZ) offers hope for increased production although much more basic data and knowledge is required. In both coastal and offshore areas, sustainable resource management is a premise that cannot be compromised. Both challenges require knowledge that can be delivered immediately and effectively to the decision-makers and users of the resource.

The delivery of useful knowledge to stakeholders for informed decision making is best suited for approaches in artificial intelligence. Combinations in knowledge base or expert system (KBS) with decision support systems (DSS) and adaptive or learning systems have been shown to be successful in providing solutions to complex, critical and difficult problems, including resource management. The major advantage lies in the ability to present results to the user regardless of the technical background. Hence, fisherfolk, municipal mayors, scientists, and government workers will be using the same knowledge base but interacting with different interfaces to allow them to understand and correspondingly be provided with scientifically-based, rational understandable solutions and recommendations.

Knowledge based expert systems, or simply expert systems, use human knowledge to solve problems that normally would require human intelligence. Books and manuals have a tremendous amount of knowledge but a human has to read and interpret the knowledge for it to be used. Conventional computer programs contain little knowledge other than the basic algorithm for solving that specific problem and the necessary boundary conditions. This program knowledge is often embedded as part of the programming code, so that as the knowledge changes, the program has to be changed and rebuilt. Knowledge based systems collect the small fragments of human know-how into a knowledge base which is used to reason through a problem, using the knowledge that is appropriate. A different problem, within the domain of the knowledge base, can be solved using the same program without reprogramming. The ability of these systems to explain the reasoning process through the back-traces and to handle levels of confidence and uncertainty provides an additional feature that conventional programming does not handle.

Objectives:

 

Methodology:

Knowledge Acquisition Interview with the knowledge experts together with possible end-users were held.

Analysis and verification of existing data sets. Survey of existing data from institutions involved, such as the Guiuan Development Foundation Inc. (GDFI) and databases were conducted.

Design and analysis of the domain While the intention was to formally model the domain using the UML or the Unified Modeling Language, the capability of the software purchased made such modeling unnecessary as the construction of the knowledge base resulted in the automatic construction of the system model.

Knowledge based development involved the use of Acquire, an integrated knowledge acquisition and knowledge base development system to represent the objects and rules consisting the system. Default is forward chaining with an option for backward chaining.

Interface development involves the use of both Acquire 2.1 and Acquire SDK to configure a program by which the user interacts with the system. Implementation was in Visual Basic and integrated in a Web Page.