satisficing

A decision procedure in which an agent fixes an aspiration level — the least it will accept — and takes the first option that reaches it, instead of optimizingoptimization, n.: Finding the best possible choice according to some score, such as the lowest error or the highest reward. Training an AI is optimization: adjusting its settings until its mistakes are as small as possible. by comparing every option to find the best. Herbert Simon proposed it as a realistic model of bounded rationalitybounded rationality, n.: The idea that real decision-makers, whether people or machines, cannot consider every option or compute the perfect choice, so they make reasonable decisions within limits of time, information and thinking power.: real agents lack the information and computation to maximize expected utilityutility function, n.: A way of scoring every possible outcome by how much someone, or some AI, wants it. A rational agent is often modeled as choosing whatever maximizes its expected score., so they satisfice, raising or lowering their aspiration levels with experience. In AI the idea appears in resource-bounded reasoning, anytime algorithms and search that stops at the first solution of acceptable cost. In AI safetyAI safety, n.: The effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable. satisficing is studied as a way to avoid the extreme strategies of maximizing agents, but a satisficer may still pick a dangerous option that clears its threshold, or judge that building a maximizer is a good-enough plan — problems that motivated quantilizersquantilizer, n.: An AI decision rule that, instead of picking the single highest-scoring action, picks at random among the top few percent of ordinary, human-like actions — limiting how badly a flawed goal can backfire. and other forms of mild optimizationmild optimization, n.: Building an AI that settles for a solid result instead of squeezing out every last bit of score, since the most extreme ways to max out a goal are often loopholes or excesses nobody wanted..

The scheduler satisfices: it accepts the first timetable that is under budget and has no clashes.