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Automated modeling in process-based qualitative reasoning

Qualitative reasoning (QR) has proved itself useful in applications involving knowledge transfer and acquisition. Building models for these applications is, however, a difficult and lengthy task for the domain experts involved. To relieve the strain placed on the experts and speed up the modeling process, an automated modeling algorithm is desirable. This thesis describes the issues related to automated modeling in QR and introduces a preliminary algorithm. The algorithm particularly focuses on capturing cause-effect reasoning, which is essential for understanding and explaining the behavior of systems. By analyzing several well-established models and their behavior, the algorithm is designed by relating the theory underlying qualitative reasoning to possible output behavior. The algorithm is successfully applied to several systems ranging from simple to fairly complex, and concrete improvements are proposed that would enable the algorithm to handle even more complex systems. Consequently, the result of this thesis is a thorough description of the challenges and prospects of this previously largely unexplored area, but more importantly it introduces a preliminary algorithm with promising results.