Artificial Intelligence Local Beam Search In this algorithm, it holds k number of states at any given time. At the start, these states are generated randomly. The successors of these k states are computed with the help of objective function. If any of these successors is the maximum value of the objective function, then the algorithm stops. Otherwise the (initial k states and k number of successors of the states = 2k) states are placed in a pool. The pool is then sorted numerically. The highest k states are selected as new initial states. This process continues until a maximum value is reached. function BeamSearch( problem, k), returns a solution state. start with k randomly generated states loop generate all successors of all k states if any of the states = solution, then return the state else select the k best successors

When the metal cools, its new structure is seized, and the metal retains its newly obtained properties. In simulated annealing process, the temperature is kept variable. We initially set the temperature high and then allow it to cool' slowly as the algorithm proceeds. When the temperature is high, the algorithm is allowed to accept worse solutions with high frequency. Start 5. Initialize k = 0; L = integer number of variables; 6. From i -> j, search the performance difference . 7. If <= 0 then accept else if exp(-/T(k)) > random(0,1) then accept; 8. Repeat steps 1 and 2 for L(k) steps. 9. k = k + 1; Repeat steps 1 through 4 till the criteria is met.

End Travelling Salesman Problem In this algorithm, the objective is to find a low-cost tour that starts from a city, visits all cities en-route exactly once and ends at the same starting city. Start Find out all (n -1)! Possible solutions, where n is the total number of cities. 25 Artificial Intelligence Determine the minimum cost by finding out the cost of each of these (n -1)! solutions. Finally, keep the one with the minimum cost. end 26 6. Fuzzy Logic Systems Artificial Intelligence Fuzzy Logic Systems (FLS) produce acceptable but definite output in response to incomplete, ambiguous, distorted, or inaccurate (fuzzy) input.

What is Fuzzy Logic? Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to humans YES or NO. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as: CERTAINLY YES POSSIBLY YES CANNOT SAY POSSIBLY NO CERTAINLY NO The fuzzy logic works on the levels of possibilities of input to achieve the definite output. Implementation It can be implemented in systems with various sizes and capabilities ranging from small micro-controllers to large, networked, workstation-based control systems. It can be implemented in hardware, software, or a combination of both.