Techniques In Problem Solving

Researchers' underlying assumption was that simple tasks such as the Tower of Hanoi correspond to the main properties of "real world" problems and thus the characteristic cognitive processes within participants' attempts to solve simple problems are the same for "real world" problems too; simple problems were used for reasons of convenience and with the expectation that thought generalizations to more complex problems would become possible.Perhaps the best-known and most impressive example of this line of research is the work by Allen Newell and Herbert A. In computer science and in the part of artificial intelligence that deals with algorithms ("algorithmics"), problem solving includes techniques of algorithms, heuristics and root cause analysis.There are two different types of problems, ill-defined and well-defined: different approaches are used for each.

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One such component is the emotional valence of "real-world" problems and it can either impede or aid problem-solving performance.

Researchers have focused on the role of emotions in problem solving , In conceptualization, human problem solving consists of two related processes: problem orientation and the motivational/attitudinal/affective approach to problematic situations and problem-solving skills.

The use of computers to prove mathematical theorems using formal logic emerged as the field of automated theorem proving in the 1950s. Shaw, as well as algorithmic methods, such as the resolution principle developed by John Alan Robinson.

It included the use of heuristic methods designed to simulate human problem solving, as in the Logic Theory Machine, developed by Allen Newell, Herbert A. In addition to its use for finding proofs of mathematical theorems, automated theorem-proving has also been used for program verification in computer science.

Well-defined problems allow for more initial planning than ill-defined problems.

Solving problems sometimes involves dealing with pragmatics, the way that context contributes to meaning, and semantics, the interpretation of the problem.

It can also be applied to a product or process prior to an actual failure event—when a potential problem can be predicted and analyzed, and mitigation applied so the problem never occurs.

Techniques such as failure mode and effects analysis can be used to proactively reduce the likelihood of problems occurring.

In these disciplines, problem solving is part of a larger process that encompasses problem determination, de-duplication, analysis, diagnosis, repair, and other steps.

Other problem solving tools are linear and nonlinear programming, queuing systems, and simulation.


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