Agent-based computing techniques are expected to play important roles as a tool for future decision-making in forest management and planning. This research aims to explore on the development of an agent-based framework to facilitate decision-making process in forest planning.The objective of the Forest Management and Planning System (FORMAP) is to determine the type of tree species and their sizes to be felled, future standing trees at the next rotation period and formulate a plan for silvicultural treatments. This system integrates information on forest inventory, future projection of standing trees based on simulated growth and yield model, and rules for decision making process. Criteria used to facilitate the decision-making process include the length of the rotation period, species composition, the minimum future stands required and others. An approach to the design and implementation of the FORMAP based on Forest Growth Simulator that have been developed earlier is inserted in a generic behaviour-based architecture is presented, that is based on the Unified Modeling Language (UML). The new methodology, called Construction for Intelligent Agents and Implementation (CIAI) is a step-by-step requirement to code framework for developing multi-agent software that integrates design models
and philosophies from both object-oriented software engineering and multi-agent system using UML notation. The models and phases of CIAI are: systems requirement models; agent interoperability model; agent implementation model; code model; and component model. The methodology has been implemented using Multi-Agent Systems (MAS) and Foundation of Intelligent Physical Agents Operating Systems (FIPA-OS) compliant platform, and the experimental results are been very encouraging. |