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Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields.
Ashby (1960, section 11/5) offers three simple strategies for dealing with the same basic exercise-problem, which have very different efficiencies. Suppose a collection of 1000 on/off switches have to be set to a particular combination by random-based testing, where each test is expected to take one second.
The history of computational thinking as a concept dates back at least to the 1950s but most ideas are much older. [6] [3] Computational thinking involves ideas like abstraction, data representation, and logically organizing data, which are also prevalent in other kinds of thinking, such as scientific thinking, engineering thinking, systems thinking, design thinking, model-based thinking, and ...
Problem-based learning (PBL) is a teaching method in which students learn about a subject through the experience of solving an open-ended problem found in trigger material. The PBL process does not focus on problem solving with a defined solution, but it allows for the development of other desirable skills and attributes.
This method is purported to help improve the problem-solving process and simplify the solutions developed as a result. [ 1 ] [ 2 ] The theory of Action Learning and its epistemological position were originally developed by Reg Revans , who applied the method to support organizational and business development initiatives and improve on problem ...
It is a major component of problem-solving training, delivered as part of the induction into the Toyota Production System. The architect of the Toyota Production System, Taiichi Ohno , described the five whys method as "the basis of Toyota's scientific approach by repeating why five times [ 5 ] the nature of the problem as well as its solution ...
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. [1] In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time.
The complexity class NP, on the other hand, contains many problems that people would like to solve efficiently, but for which no efficient algorithm is known, such as the Boolean satisfiability problem, the Hamiltonian path problem and the vertex cover problem. Since deterministic Turing machines are special non-deterministic Turing machines ...