Get reliable rangeland science

Polycultural agroecosystems, such as rangelands, are too complex and poorly understood to permit precise numerical simulation. Management decisions that depend on behavioral predictions of such ecosystems therefore require a variety of knowledge sources and reasoning techniques. Our approach to designing a computer system that provides advice concerning such ecosystems is to incorporate various reasoning paradigms and apply whatever paradigm is most appropriate to each task arising in the advice process. This approach is based on a particular process description of expert human problem solving that uses four different reasoning paradigms: model-based reasoning (MBR); case-based reasoning (CBR); rule-based reasoning (RBR); and statistical reasoning. The process description is implemented in CAse-based Range Management Adviser (CARMA), a computer system for advising ranchers about the best response to rangeland grasshopper infestations. CARMA attempts to emulate the human ability to integrate multiple knowledge sources and reasoning techniques in a flexible and opportunistic fashion. The goal of this approach is to enable computer systems to optimize the use of the diverse and incomplete knowledge sources and to produce patterns of reasoning that resemble those of human decision-makers.