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Heuristic search algorithms are a class of search algorithms used in machine learning and artificial intelligence that incorporate domain-specific knowledge to guide the search process. Unlike uninformed (or blind) search algorithms that explore the solution space without any additional information, heuristic search algorithms use heuristics to estimate how close a particular state  is to the goal state. A heuristic is a rule of thumb or a guiding strategy that helps to prioritize certain paths over others. The goal of using heuristics is to improve the efficiency of the search process by focusing on the most promising areas of the solution space. Heuristic search algorithms are particularly useful in problems where the solution space is large and exploring every possibility in a blind manner is not feasible. 

Two common heuristic search algorithms are: 

     i) A* (A-star) Search: 

        i.a) A* (A-star) Search combines the benefits of both uniform-cost search and greedy best-first search. 

        i.b) It uses a cost function that takes into account both the cost to reach a particular state (the actual cost incurred so far) and an estimate of the cost from that state to the goal. 

        i.c) The heuristic function guides the algorithm to explore paths that seem more promising based on the estimated total cost. 

     ii) Greedy Best-First Search: 

        ii.a) Greedy best-first search focuses only on the heuristic information to decide which path to explore next. 

        ii.b) At each step, it selects the path that appears to be the most promising based on the heuristic evaluation or called heuristic function h(n). 

        ii.c) However, it does not consider the actual cost of reaching the current state, which can lead to suboptimal solutions. 

Heuristic search algorithms are widely used in various applications, such as pathfinding, puzzle-solving, and optimization problems, where the use of domain-specific knowledge can significantly improve the efficiency of the search process. 

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