Worthwhile-to-move behaviors as temporary satisficing without too many sacrificing processes
Worthwhile-to-move behaviors as temporary satisficing without too many sacrificing processes
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发表时间：
2009-05
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通讯作者：
H. Attouch;A. Soubeyran
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作者：
H. Attouch;A. Soubeyran
值得移动的增量原则是一种机制，在每个步骤中，智能体在移动之前和围绕当前状态进行探索之后，将中间的优势和改变的成本与保持的优势和成本进行比较。这些改变的优势和成本包括目标设定、心理、认知、学习和惯性方面。可接受的移动是这样的：移动比留下来的优势高于移动比留下来的成本的一部分，因此，达到目标的中间牺牲是有限的。当智能体更加以目标为导向并且在每一步都有足够的改进时，该过程就会以永久例程结束，这是智能体更愿意留下来而不是改变的休息点，尽管可能会因未能实现目标而感到一些挫败感。在移动本地成本较高的情况下，这种方法会导致埃克兰 epsilon 变分原理的认知证明。重新审视假设（假设代理会优化），导致引入一类新的惯性优化算法、局部搜索和邻近算法。
The worthwhile-to-move incremental principle is a mechanism where, at each step, the agent, before moving and after exploration around the current state, compares intermediate advantages and costs to change to advantages and costs to stay. These advantages and costs to change include goal-setting, psychological, cognitive, learning and inertia aspects. Acceptables moves are such that advantages to move than to stay are higher than some fraction of costs to move than to stay, with, as a result, a limitation of the intermediate sacrifices to reach the goal. When the agent is more goal-oriented and improves enough at each step, the process ends in a permanent routine, a rest point where the agent prefers to stay than to change, in spite of some possible residual frustration to have missed his goal. In case of high local costs to move this approach leads to a cognitive proof of Ekeland epsilon-variational principle. The as if hypothesis (as if agents would optimize) is revisited, leading to the introduction of a new class of optimization algorithm with inertia, the local search and proximal algorithms.