Heuristic search techniques are an important area of Artificial Intelligence (AI) research. Heuristic search is a form of problem-solving where the goal is to find a solution path which leads to the goal state on a problem or task, using heuristics. In AI, the term ‘heuristics’ refers to rules of thumb or strategies which are used to reduce the number of possible solutions in a given problem space. Heuristic search algorithms use these heuristics as a way of guiding their progress through the search space towards finding an optiomal solution. Some common heuristic search techniques include Depth-First Search (DFS), Breadth-First Search (BFS), A* Search, Hill Climbing and Simulated Annealing. Each technique has its own advantages and disadvantages but all share one purpose – to efficiently explore potential paths towards solving a problem.
Depth-first Search (DFS) is an example of recursive backtracking: starting at one point in the graph and iteratively exploring every avenue available until you reach your destination or hit dead ends, then backtracking and trying another approach with successive branches from other points in the graph. This can be useful for quickly locating solutions in deep or large graphs, although it tends to get stuck in loops without any mechanism for finding better options on parts that have already been explored – DFS does not take into consideration previously found paths when looking up new ones so it won’t detect cycles easily if at all by itself.

Breadth-first Search (BFS) is similar but works differently than DFS; instead of going deeper into one branch before exploring other possibilities from different starting points like DFS does, BFS looks initially at all those possible options first before delving further down specific branches – this can make it more effective for certain types problems where shorter overall paths are desired over deep searches with many backtracks along multiple levels deep within trees/graphs structures.