Problem Solving Strategies - COS 102

A set of vocabulary flashcards covering the definitions, characteristics, and examples of various problem-solving strategies such as algorithms, heuristics, and trial and error as discussed in the COS 102 lecture notes.

Problem

A question, issue, or situation that presents difficulty and requires an immediate and effective solution.

Problem-Solving Strategy (PSS)

A structured approach used to understand, analyse and solve problems efficiently, involving systematic procedures, creative thinking, or flexible cognitive abilities.

Trial and Error

A method of learning or problem solving where different responses are tried, wrong ones are rejected, and the successful response becomes stronger after repetition.

Edward Thorndike's puzzle box

An experiment where a cat was placed in a box and learned to escape more quickly through random actions that eventually led to pressing the correct lever, confirming trial-and-error learning.

Algorithm

A finite set of well-defined, unambiguous instructions or a step-by-step procedure written in human-readable language for solving a specific problem in a finite amount of time.

Finiteness

A characteristic of an algorithm stating that it must always terminate after a finite number of steps.

Definiteness

The property of an algorithm where each step must be clearly stated, precise, and unambiguous.

Effectiveness

The property of an algorithm where every step must be basic enough to be carried out easily and in a finite amount of time.

Flowcharts

Visual diagrams using shapes like boxes, diamonds, and parallelograms connected by arrows to show the flow of control in an algorithm.

Pseudocode

A non-executable, English-like description of an algorithm's logic that allows programmers to focus on the thought process without strict syntax rules.

Algorithm Efficiency

The measurement of a computer program's resource consumption, specifically in terms of time or memory storage.

Heuristic

A rule-of-thumb or cognitive shortcut used to solve problems faster by prioritizing speed over efficiency, often making smart guesses to lead to good solutions quickly.

Means-ends analysis

A problem-solving strategy introduced by Allen Newell and Herbert A. Simon in $1972$ where an end goal is broken down into subgoals and action plans are prepared.

General Problem Solver (GPS)

A model developed by Newell and Simon that could solve mathematical theorems, logical proofs, and other well-defined problems through means-ends analysis.

Decomposition

Also known as Modular Break Down, this involves breaking down a complex, large-scale problem into smaller, more manageable, and independent sub-problems.

Divide and Conquer

A technique that divides the main problem into independent subproblems, operates in three steps (Divide, Conquer, and Merge), and is used in Merge Sort and Quicksort.

Backtracking

An algorithmic technique for solving problems incrementally by abandoning any path as soon as it cannot lead to a valid solution, used in Sudoku and Maze Solving.

Dynamic Programming

An optimization over plain recursion that stores results of subproblems to reduce time complexity from exponential to polynomial.