The job of AI is to design the agent program that implements the agent function mapping percents to actions. This will run on some sort of computing device with physical sensors and actuators. We call this the “architecture”.

Agent – architecture + program.

Agent program.

The agent programs take the current precept as input from the sensors and return an action to the actuators. Notice the difference between the agent program, which takes the current percept as input and the agent function, which takes the entire percept history. The agent program takes just the current percept as input because nothing more is available from the environment, if the agent actions depends on the entire percept sequence, the agent will have to remember the percepts. Example: Figure (5) shows a rather trial agent program that keeps track of the percept sequence and then uses it to index into a table of actions to decide what to do. The table represents explicitly the agent function that the agent program embodies. To build a rational agent in this way, we as designers must construct a table that contains the appropriate action for every possible percept sequence.

Figure (5) TABLE – DRIVEN – AGENT program is involved for each new percept and returns an action each time. It keeps track of the percept sequence using its own private data structure.