Uninterrupted learning agent using finite state machine

In a multi-agent system (MAS), an issue arises as agent may possibly encounter a new event, an event that has never been perceived by the agent or was never anticipated by a human developer during the agent implementation. The ability to recognise an event as being on a new class, without having to shut the system down, would significantly enhance the agent's learning potentials. This paper presents the means for allowing an agent to recognise new events without having to interrupt its services. Our scheme is based on a dynamic construction of finite-state machines (FSM) from the general regularities implicitly embedded in the data. We have extended additional functionality in FSM transition function to enhance the recognition. Our experiment has shown that our extended FSM can enhance the learning process and allows the system to continue working without system interruption.