A general theme that we might use for a number of scenarios is the design of an intelligent Vanderbilt student-life advisor. One component of such an advisor might be an automated course scheduler that allows a student to submit majors, minors, desired courses, and desired disciplines.
Describe the automated course scheduler task as a state space search problem, including
• the definition of a state in the state space
• the start state
• for each state, a set of actions available to the agent in that state
• an action function that, given a state and an action, returns a new state (basically, a simulator of action effects in the real world)
• a goal specified as a Boolean function, goal(s) that is true when state s satisfies the goal, in which case we say that s is a goal state
• a criterion that specifies the quality of an acceptable solution. For example, any sequence of actions that gets the agent to the goal state may be acceptable, or there may be costs associated with actions and the agent may be required to find a sequence that has minimal total cost. A solution that is best according to some criterion is called an optimal solution. We do not always need an optimal solution, for example, we may be satisfied with any solution that is within 10% of optimal.