1.   Partial Computer Homeostasis Using Autonomous Epistemic Agents

       (Poster presented at KCAP 2015).

  1. Cameron Hughes, Ctest Laboratories

  2. Tracey Hughes, Ctest Laboratories

  3. Trevor Watkins, Kent State University

  4. James Dittrich, ASC (Advanced Software Construction)



The proliferation of mobile computing, the Internet of Things, hosting services, and cloud computing has

increased the burden of computer log file analysis for system administrators, network analysts, security

analysts, and large server hosting organizations. This is due to the voluminous amounts of log entries

now produced by these technologies. Since log file analysis is used to monitor and control the overall health

of the computer systems behind these technologies, it has become increasingly important. The spike in the

number of log entries has made real-time log analysis by human effort untenable and automated real-time

log analysis essential. The log analysis process often requires human insight and judgment before a diagnosis

or information synthesis becomes apparent. So while automated log analysis methods are essential, they must

also be knowledge-based to be effective. In this paper, we describe a knowledge-based approach to partial

computer self-regulation that uses autonomous epistemic agents to analyze and diagnose syslog entries in real-

time, using a priori and posteriori knowledge of log file analysis within a hybrid deductive abductive first order

logic model. The epistemic agent uses its a priori knowledge of Unix/Linux-based computer systems in conjunc-

tion with posteriori knowledge extracted from log file entries to uncover negative and positive scenarios and

take advantage of opportunities to regulate a computer system's homeostasis.