A heuristic or heuristic technique (problem solving, mental shortcut, rule of thumb) is any approach to problem solving that employs a pragmatic method that is not fully optimized, perfected, or rationalized, but is nevertheless "good enough" as an approximation or attribute substitution. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.
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Heuristics in the context of Rhetoric
Rhetoric is the art of persuasion. It is one of the three ancient arts of discourse (trivium) along with grammar and logic/dialectic. As an academic discipline within the humanities, rhetoric aims to study the techniques that speakers or writers use to inform, persuade, and motivate their audiences. Rhetoric also provides heuristics for understanding, discovering, and developing arguments for particular situations.
Aristotle defined rhetoric as "the faculty of observing in any given case the available means of persuasion", and since mastery of the art was necessary for victory in a case at law, for passage of proposals in the assembly, or for fame as a speaker in civic ceremonies, he called it "a combination of the science of logic and of the ethical branch of politics". Aristotle also identified three persuasive audience appeals: logos, pathos, and ethos. The five canons of rhetoric, or phases of developing a persuasive speech, were first codified in classical Rome: invention, arrangement, style, memory, and delivery.
Heuristics in the context of Constraint satisfaction problem
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems. Additionally, the Boolean satisfiability problem (SAT), satisfiability modulo theories (SMT), mixed integer programming (MIP) and answer set programming (ASP) are all fields of research focusing on the resolution of particular forms of the constraint satisfaction problem.
Examples of problems that can be modeled as a constraint satisfaction problem include: