A Knowledge-Based System for Diagnosis of Mastitis Problems at the Herd Level. 1. Concepts

Abstract

Much specialized knowledge is involved in the diagnosis of a mastitis problem at the herd level. Because of their problem-solving capacities, knowledge-based systems can be very useful to support the diagnosis of mastitis problems in the herd. Conditional causal models with multiple layers are used as a representation scheme for the development of a knowledge-based system for diagnosing mastitis problems. Construction of models requires extensive cooperation between the knowledge engineer and the domain expert. The first layer consists of three overview models: the general overview conditional causal model, the contagious overview conditional causal model, and the environmental overview conditional causal model, giving a causal description of the pathways through which mastitis problems can occur. The conditional causal model for primary udder defense and the conditional causal model for host defense are attached to the overview models at the second layer, and the conditional causal model for deep primary udder defense is attached to the conditional causal model for the primary udder defense at the third layer. Based on quantitative user input, the system determines the qualitative values of the nodes that are used for reasoning. The developed models showed that conditional causal models are a good method for modeling the mechanisms involved in a mastitis problem. The system needs to be extended in order to be useful in practical circumstances. © 1995, American Dairy Science Association. All rights reserved.