Heuristic Computing
- Many problems are not conducive to algorithmic solutions.
- Even if one understands the problem well enough, and possesses knowledge about the problem domain, and can construct an algorithm to solve the problem, the amount of computational resources (time or space) needed to execute the algorithm may be simply infeasible.
- From an evolutionary point of view, algorithms are not all there is to our ways of thinking. And so the question arises: what other computational means are at our disposal to perform such tasks? The answer is to resort to a mode of computing that deploys heuristics.
- Heuristics are rules, precepts, principles, hypotheses based on common sense, experience, judgement, analogies, informed guesses, etc., that offer promise but are not guaranteed to solve problems.
- Heuristic computing embodies a spirit of adventure!
- There is an element of uncertainty and the unknown in heuristic computing.
- A problem solving agent (a human being or a computer) looking for a heuristic solution to a problem is, in effect, in a kind of terra incognita. And just as someone in an unknown physical territory goes into exploration or search mode so also the heuristic agent: he, she, or it searches for a solution to the problem, in what computer scientists call a problem space, never quite sure that a solution will obtain.
- Thus one kind of heuristic computing is also called heuristic search.
- Many strategies, however, that deploy heuristics have all the characteristics of an algorithm—with one notable difference: they give only ‘nearly right’ answers for a problem, or they may only give correct answers to some instances of the problem.
- Computer scientists, thus, refer to some kinds of heuristic problem solving techniques as heuristic or approximate algorithms.
- The term ‘heuristic computing’ encompasses both heuristic search and heuristic algorithms.
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