Pseudo-code for the Simulated-Annealing algorithm is given below, note that in the version of the algorithm given, we wish to maximize the objective function (ie. walk uphill). function SIMULATED-ANNEALING( problem, schedule) returns a solution state inputs: problem, a problem schedule, a mapping from time to "temperature" local variables: T. a "temperature controlling the probability of downward steps current -MAKE-NODE(problem INITIAL STATE) for t=1 to do T-schedule(t) if T=0 then return current next a randomly selected successor of current AE-next.VALUE-current.VALUE if AE>0 then current--nert else current-next only with probability T (1) Describe the idea behind the Simulated-Annealing algorithm. Be sure to briefly explain the role of each component in the algorithm. I (1) Indicate how you could change the Simulated-Annealing algorithm so that it implements a "strict" version of hill-climbing, Text (ii) With regards to Simulated-Annealing, what is the probability of accepting the following moves? Assume the problem is trying to maximize the objective function. (If you don't have a calculator, you can leave your answers in the form of mathematical expressions) Current Evaluation 16 25 24 Nighborhood 13 13 15 Tamper 30 25 276