Uninformed and Informed Search Techniques in Artificial Intelligence

In this master thesis the search techniques in Artificial Intelligence are analyzed. The search techniques are grouped into two main categories which are uninformed search techniques and informed search techniques. Such uninformed search techniques as breadth-first search, depth-first search, depth-limited search, iterative deepening search, uniform cost search, and bidirectional search are considered. The best-first search, greedy best-first search, A* search and hill climbing techniques as paradigms of informed search techniques are studied. The completeness, optimality, time complexity, and space complexity properties of all above mentioned search techniques are discussed. The Dijkstra’s algorithm is used to find the shortest paths from the initial node to all other nodes in a weighted digraph.