Detailed Explanation: In AI, the solution space encompasses all the potential answers or configurations that can be derived from a problem's parameters. This space is crucial in optimization and search algorithms, where the goal is to navigate through possible solutions to identify the most optimal one. The nature of the solution space, whether discrete or continuous, affects the complexity and approach of the algorithm used. For instance, in combinatorial problems like the traveling salesman problem, the solution space is vast and requires sophisticated techniques such as heuristic search or genetic algorithms to efficiently explore. Understanding the structure and constraints of the solution space helps in designing algorithms that can effectively and efficiently converge on the best possible solution.

Historical Overview: The concept of solution space has been inherent in mathematical optimization and decision theory since the early 20th century. It gained significant traction in the field of AI with the development of search algorithms in the 1950s and 1960s, particularly with the advent of algorithms like A* (1968) which specifically navigate solution spaces to find optimal paths.

Key Contributors: Significant contributors to the concept of solution space include early pioneers in operations research and optimization such as George Dantzig, who developed the simplex method for linear programming. In AI, Richard Bellman, known for his work on dynamic programming and the Bellman equation, and Peter Hart, Nils Nilsson, and Bertram Raphael, who developed the A* search algorithm, have been pivotal in advancing our understanding and application of solution spaces in computational problems.