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?

Consider the following statements: struct nametype{string first; string last; }; struct coursetype{string name; int callnum; int credits; char grade; }; struct studenttype{nametype name; double gpa; coursetype course; }; studenttype student; studenttype classlist[100]; coursetype course; nametype name; mark the following statements as valid or invalid. if a statement is invalid, explain why.a.) student.course.callnum = "csc230"; b.) cin > > student.name; c.) classlist[0] = name; d.) classlist[1].gpa = 3.45; e.) name = classlist[15].name; f.) student.name = name; g.) cout < < classlist[10] < < endl; h.) for (int j = 0; j < 100; j++)classlist[j].name = name; i.) classlist.course.credits = 3; j.) course = studenttype.course;

In microsoft word, when you highlight existing text you want to replace, you're in              a.  advanced mode.    b.  automatic mode.    c.  basic mode.    d.  typeover mode

Learning sign language is an example of a(n) learning sign language is an example of a(n)