"无信息和启发式搜索问题解决算法：完备性、最优性、时间与空间复杂度"

Problem solving by search is a fundamental concept in artificial intelligence, and it can be classified into two main categories: uninformed search and heuristic search. Uninformed search algorithms are driven purely by the structure of the search space, without any additional information guiding the search process. On the other hand, heuristic search algorithms utilize domain-specific information, known as heuristics, to guide the search towards the most promising paths. One of the key aspects of search algorithms is their completeness, which means that if a solution exists, the algorithm will eventually find it. Additionally, the optimality of a search algorithm refers to its ability to find the best possible solution. The time complexity and space complexity of a search algorithm are also important considerations, as they determine the efficiency of the algorithm in finding a solution. Search algorithms have been successfully applied to various AI problems, including game playing programs. The formalism of formulating a problem as a search problem involves defining a state space over which to search, abstracting the problem into states and actions, and specifying the initial state and goal state. By applying search algorithms to this formalism, AI systems can effectively solve complex problems by exploring the search space and identifying the optimal solution. Overall, search algorithms play a crucial role in problem solving in artificial intelligence, providing a systematic and efficient approach to finding solutions in a wide range of applications. From uninformed search to heuristic search, these algorithms offer a versatile toolbox for tackling complex problems and advancing the field of AI.