In this section, we implement CTM learning for the collaborative editing systems. We have world assumption as following:

Human agent

An editor, a system designer or any kind of users of editing systems who interact with the editing sytems for correcting the documents

Assistance agent

An intelligence agent observes the user interaction and helps the users.

Environment

TConnect Editor v0, the enviroment types is defined:

• Fully observable (vs. partially observable)
• Deterministic (vs. stochastic)
• Episodic (vs. sequential)
• Static (vs. dynamic)
• Discrete (vs. continuous)
• Single agent (vs. multiagent)

Our environment is semi-dynamic, partially observable, stochastic, sequential, discrete and multiagent.

We assume that the human agents could be different based on their personalization. Four basic types in order of increasing generality:

• Simple reflex agents
• Reflex agents with state
• Goal-based agents
• Utility-based agents
• All these can be turned into learning agents

Obviously, in the 1st case, if the user acts as simple reflex agent. The A-Model in CTM method provides a very good way to learn the conditions of user action.