Statistical Language Learning
Concurrency and Automata on Infinite Sequences
Proceedings of the 5th GI-Conference on Theoretical Computer Science
S-assess: a library for behavioral self-assessment
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Learning executable agent behaviors from observation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Development environments for autonomous mobile robots: A survey
Autonomous Robots
Robot introspection through learned hidden Markov models
Artificial Intelligence
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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).