The central component of a knowledge based agent is its knowledge base, or KB Informally, a knowledge base is a set of sentences. Each sentence is expressed in a language called a knowledge representation language and represents some assertion about the world.

There must be a way to add new sentences to the knowledge base and a way to query what is known. The standard names for these tasks are TELL and AK respectively. Both tasks may involve inference – i.e., deriving new sentences from old.

Figure shows the outline of knowledge based agent program. Like all our agents it takes a percept as input an returns an action. The agent maintains a knowledge base, KB, which may initially contain some background knowledge. Each time the agent program is called, it does two things. First, it TELL the knowledge base what it perceives. Second, it ASKS the knowledge base what action it should perform. In the process of answering this query, extensive reasoning may be done about the current state of the world, about the outcome of possible action sequences, and so on. Once the action is chosen, the agent records its choice with TELL and executes the action. The second TELL is necessary to let the knowledge base know that the hypothetically action has actually been executed.

Figure, A generic knowledge – based agent.

The details of the representation language are hidden inside two functions that implement the interface between the sensors and actuators and the core representation and reasoning system. MAKE – PRECEPT – SENTENCE takes a precept and a time and returns a sentence asserting that the agent perceived the precept at the given time. MAKE – ACTION – QUERY takes a time as a input and returns a sentence that asks what action should be performed at that time. The details of the inference mechanisms are hidden inside. TELL and ASK, the agent in figure appears quite similar to the agents with internal state described in chapter 2.

Because of the definitions of TELL & ASK, however, the knowledge based agent is not an arbitrary program for calculating actions. It is amenable to a description at the knowledge level, where we need specify only what the agents knows and what its goals are, in order to fix its behavior, example: An automated taxi might have the goal of delivering a passenger to main country and might know that it is in San Francisco and that the Golden Gate Bridge is the only link between the two locations. Then we can expect it to cross the Golden Gate Bridge because it knows that will achieve its goal. Notice that this analysis is independent of how the texi works at the implementation level. It doesn’t matter whether its geographical knowledge is implemented as linked lists or pixel maps, or whether it reasons by manipulating strings of symbols stored in registers or by propagating noisy signals in a network of neurons.

“One can build a knowledge based agent simply by TELLING it what it needs to know.” The agents initial program, before it starts to receive percepts, is built by adding one by one the sentences that represent the designers knowledge of the environment. Designing the representation language to make it easy to express this knowledge in the form of sentences simplifies the construction problem enormously. This is called the declarative approach to system building. The procedural approach encodes desired behaviors directly as program code, minimizing the role of explicit representation and reasoning can result in a much more efficient system.