Knowledge-Based and Simulation Models for Managing Predictive Knowledge
Along with hypertext systems, knowledge-based systems (KBSs) are key knowledge management tools (see table below). KBSs grew out of the science of symbolic logic and were adapted for practical problem solving by scientists in the field of artificial intelligence (AI). Researchers in AI realized that much of what human experts know cannot be easily formulated into a system of mathematical equations. In structure, a KBS may consist of many components (see figure below), but at the heart of any KBS is its knowledge-base. Knowledge from experts, or other expert sources (e.g., literature), is codified into logical statements (the knowledge base), which can be manipulated for problem-solving purposes. Knowledge-based systems sacrifice even more generality than hypertext systems. KBSs are more costly to develop than hyperdocuments and they cannot capture the breadth of knowledge that hyperdocuments can. KBSs, however, are more precise (less ambiguous) and more compactly represent knowledge than hyperdocuments. Furthermore, KBSs increase problem-solving power by supporting automated reasoning about the domain of interest. (Table:Language Based Knowledge Representation Methods)
Despite our most strenuous efforts to quantify important ecological processes in simulation model form, by far the larger body of what we know can only be expressed qualitatively, comparatively, and inexactly. Most often this qualitative knowledge has been organized over long years of professional practice by human experts. While KBSs allow us to capture some of this qualitative, experience-based judgment, it is still not possible to capture the full range and flexibility of knowledge and reasoning ability of human experts in knowledge-based software. We have learned, however, in many cases how to capture and use that portion of expertise that the human expert considers routine. Many KBSs have been developed for the agriculture and forestry domains and have been published in the scientific journals AI Applications, Computers and Electronics in Agriculture, and others. An extensive presentation of KBSs and how to develop them specifically for natural resource management purposes can be found in a 1996 book by Schmoldt and Rauscher.
Simulation models are the information technology of choice when knowledge can be expressed mathematically. In the last 20 years, an impressive amount of mathematical simulation software has been developed for all aspects of natural resource management. Schuster et al. (1993) conducted a comprehensive inventory of simulation models available to support forest planning and natural resource management. They identified and briefly described 250 software tools. Jorgensen et al. (1996) produced another compendium of ecological models that incorporate an impressive amount of ecosystem theory and data. There is no way to even begin to cover all the available simulation software useful for natural resource management.
Both knowledge-based and simulation models for natural resource management are typically developed independently of one another. As a result, they are large, monolithic, stand-along systems that function like ?little islands of automation? unable to easily communicate with each other. Although collectively, the existing knowledge-based and mathematical simulation models could potentially address many of the predictive forecasting needs of managers, this potential is likely to go unrealized until effective software communication standards are introduced and followed. Natural resource managers need integrated suites of software tools to provide comprehensive coverage for the range of biological predictions needed. Although such interoperability standards have received considerable attention outside the realm of natural resource management, they have, until recently, been largely ignored in natural resource management software development.
Encyclopedia ID: p1641
