Well-defined problems (WDPs) have been studied in the fields of psychology, cognitive science, and artificial intelligence for decades. WDPs are characterized by their clear and concise description of the problem and the solution. For example, the classic WDP used in AI research is the Towers of Hanoi. The problem is to move all of the disks from one peg to another, with the following rules: only one disk can be moved at a time, and a larger disk cannot be placed on a smaller disk.
Since WDPs are well-defined, they tend to be easier to solve than ill-defined problems. This is because the problem’s structure is more clearly understood and the solution can be formulated more easily. WDPs are useful in many fields, such as decision-making, game theory, and robotics.
In terms of decision-making, WDPs provide a clear set of criteria for evaluating potential solutions. This makes them well-suited for situations where many alternatives must be considered and a decision must be made quickly. For example, when deciding which stocks to buy, a decision-maker can use a WDP to evaluate the potential investments and make a more informed decision.
In game theory, WDPs can be used to identify optimal strategies. By analyzing a WDP’s structure and solution, game theorists can develop strategies that maximize a player’s expected payoff. This is especially useful in two-player games such as chess, where the optimal move can be determined by analyzing the game’s WDP.
Finally, WDPs are also useful in robotics. By providing a clear set of rules and objectives, WDPs can be used to create algorithms that allow robots to autonomously solve problems. For example, a robot can be programmed to solve the Towers of Hanoi problem by following a set of instructions based on the WDP.
In conclusion, well-defined problems are useful in a variety of fields, from decision-making to robotics. By providing a clear and concise description of the problem and solution, WDPs make it easier to formulate solutions and identify optimal strategies.
References
Adams, C. M., & Gero, J. S. (1990). Understanding ill-defined problems. Artificial Intelligence, 42(1-3), 169–194.
Gonzalez-Nino, J. C., & Castillo, O. (2018). Decision Making Under Uncertainty: Principles and Applications. New York, NY: Springer.
Krause, M. E., & Russell, S. J. (2013). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Pearson Education.
Lambert, K. & Lippman, A. (1991). An analysis of the Towers of Hanoi problem. Theoretical Computer Science, 92(2), 283–296.