Infrastructures for implementing agent architectures are currently unaware of what tasks the implemented agent is performing. Such knowledge would allow the infrastructure to improve the agent's autonomy and reliability. For example, the infrastructure could detect abnormal system states, predict likely faults and take preventive measures ahead of time, or balance system load based on predicted computational needs. In this paper we introduce a learning algorithm to automatically discover a state-transition model of the agent's behavior. The algorithm monitors the communication between architectural components, in the form of function calls, and finds the frequencies at which various functions are polled. It then determines the states according to what polling frequencies are active at any time. The two main novel features of the algorithm are that it is completely unsupervised (it requires no human input) and task-agnostic (it can be applied to any new task or architecture with minimal effort).
@inproceedings{berzanscheutz12aamas,
title={What am I doing? Automatic Construction of an Agent's State-Transition Diagram through Introspection},
author={C. Berzan and M. Scheutz},
year={2012},
booktitle={Proceedings of AAMAS 2012},
url={https://hrilab.tufts.edu/publications/berzanscheutz12aamas.pdf}
}