0000000000358537
AUTHOR
Torsten Panholzer
Automated Creation of Expert Systems with the InteKRator Toolbox
Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanation…
Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes
Hospital processes are getting more and more complex, starting from the creation of therapy plans over intra-hospital transportation up to the coordination of patients and staff members. In this paper, multi-agent simulations will be used to optimize the coordination of different kinds of individuals (like patients and doctors) in a hospital process. But instead of providing results in form of optimized schedules, here, behavioral rules for the different individuals will be learned from the simulations, that can be exploited by the individuals to optimize the overall process. As a proof-of-concept, the approach will be demonstrated in different variants of a hospital optimization scenario, …