How can you improve the learnability of an agent? Take an example where you want to develop an intelligent system to provide career guidance for
students. How can you improve intelligence from Table based agents to Learning agents in
order to improve intelligence to deal with complex career guidance?

Reading characters and strings from the keyboard: consider the following c++ program 1. #include 2. #include 3. using namespace std; 4. mystring1 5. 6. int main() 7. { 8. 9. string mystring1, mystring2; mychar1 10. 11. 12. char mychar1, mychar2; 13. 14. cout< < "enter a string: "; mychar2 15. 16. cin> > mystring1; // 17. cin.get(mychar1); 18. cin> > mychar2; 19. getline(cin,mystring2); mystring2 20. 21. 22. cout<

You will be given two character arrays of the same size, one will contain a number of ships. ships will move around the character array based on which way they are facing and the route they are on. routes are given in the other array. the route consists of '-' and '|' for straight paths, '\' and '/' for curves, and '+' for intersections. there are ships on these routes. ships always face a direction, '^' for up, '> ' for right, 'v' for down, and '< ' for left. any time the ships hit a '\' or a '/' it will turn as you would expect a ship to turn (e.g. a '^' that moves into a '/' will turn right). at an intersection, ships will always continue straight through. all ships move at the same speed, ships take turns moving and all ships move during one 'tick'. the one in the most top left goes first, followed by those to its right, then the ones in the next row. it iterates along the rows and then down the columns. each ship moves one space on its turn moving along the route. your function needs to return the position of the first collision between two ships and the number of ticks before the crash occurred.

The north and south regions had very diferent economies in the 1800s.