用于不确定性下决策的机器学习
Machine learning for decision-making under uncertainty
摘要：
我们生活在一个充满不确定性的世界里，我们经常需要在不完全信息的情况下做出很多决策。我们坚信，我们的主观信念不能用严格的数学公式来计算;而是基于达尔文的自然选择（进化过程是通过遗传编程的机器学习来模拟的），这是一种拟议的计算模型，结合了量子理论的见解，以描述和解释不确定性下的决策。与其他通过概率论解释决策过程的决策理论不同，我们提出的决策理论通过学习观察到的历史数据来发现思维的“规律”。在我们的决策理论中没有微分方程，也没有转移概率，我们的决策模型强调机器学习，决策者通过为他们做出的每个决策获得奖励或惩罚来积累经验，并为未来做出更好的决策做好准备。我们不使用通常的效用函数建模，而是使用模拟人们决策过程的量子决策树。每个量子决策树包含一组策略，每次决策时，决策者首先从量子决策树的策略池中选择一个策略，然后根据遗传规划基于期望值最大化得到的置信度选择一个行动。
英文摘要：
We live in a world brimming with uncertainty, where we constantly have to make a lot of decisions under incomplete information. We are firm believers that our subjective belief cannot be computed by rigorous mathematical formula; instead based on Darwin's natural selection (the evolution process is simulated by machine learning with genetic programming), a proposed computational model that incorporates insights from quantum theory to describe and explain decision-making under uncertainty. Unlike other decision-making theories that explain the decision-making process through probability theory, our proposed decision theory discovers "laws" of thought by learning observed historical data. There is no differential equation and no transition probability in our decision theory, our decision model has an emphasis on machine learning, where decision-makers build-up their experience by being rewarded or punished for each decision they make and prepare them for making better decisions in the future. We do not model with the usual utility function, but with quantum decision tree that simulates people's decision process. Each quantum decision tree includes a set of strategies; every time a decision is made, the decision-maker first chooses a strategy from the quantum decision tree's strategy pool, and then chooses an action based on the degree of belief which is obtained by genetic programming based on maximizing expected value.
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