When an agent gets a task, he has to de ide which task can be performed more efficiently in lesser time.

It finds some input and output locations nearer on State space grid for example in case of printing task then the probability of performing that task will increase.

State space search is unfavorable for solving real-world problems because, it requires complete description of every searched state, also search should be carried out locally.

There can be two ways of representations for a table:

i. Complete world description:

Description is available in terms of an assignment of a value to each previous suggestion. Drawback of this types is that it requires a large amount of space.

ii. Path from an initial state:

It gives the sequence of actions which are used to reach a state from an initial state. Drawing is it is difficult to determine whether two states are same.

When an agent gets a task, he has to decide which task can be performed more efficiently in lesser time.

It finds some input and output locations nearer on State space grid for example in case of printing task then the probability of performing that task will increase.

State space search is unfavorable for solving real-world problems because, it requires complete description of every searched state, also search should be carried out locally.

There can be two ways of representations for a table:

i. Complete world description:

Description is available in terms of an assignment of a value to each previous suggestion. Drawback of this types is that it requires a large amount of space.

ii. Path from an initial state:

It gives the sequence of actions which are used to reach a state from an initial state. Drawing is it is difficult to determine whether two states are same.