Foundations of Artificial Intelligence (SCSB1311) Unit II PDF

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE (SCSB1311) UNIT – II INFORMED SEARCH AND GAME PLAYING Informed search Informed search is a type of search algorithm in artificial intelligence that uses additional information or heuristics to make more accurate decisions abo...

FOUNDATIONS OF ARTIFICIAL INTELLIGENCE (SCSB1311) UNIT – II INFORMED SEARCH AND GAME PLAYING Informed search Informed search is a type of search algorithm in artificial intelligence that uses additional information or heuristics to make more accurate decisions about which paths to explore first. These heuristics provide estimates of how close a given state is to the goal, guiding the search toward more promising solutions. Informed search is particularly useful in solving complex problems efficiently, as it can significantly reduce the search space and improve the speed of finding solutions. Informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost, how to reach to goal node, etc. This knowledge helps agents to explore less to the search space and find more efficiently the goal node. The informed search algorithm is more useful for large search space. Informed search algorithm uses the idea of heuristic, so it is also called Heuristic search. Fig: 1 Heuristics Function Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time. Heuristic function estimates how close a state is to the goal Heuristic function is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.. It looks at the current situation of the agent (where it is right now). Heuristic function is given as:.Then, it gives an estimate of how close or far the agent is from reaching its goal.. It helps the agent make smarter decisions by choosing the path that h(n)