提出主观目标函数概念，让机器像人一样动态生成目标。
Subjective functions
- 用主体自身特征定义目标函数，而非依赖外部任务
- 以预期预测误差为例，验证该机制的可行性
- 对心理学、神经科学和机器学习均有启发意义
客观目标函数从何而来？我们如何选择追求的目标？人类智能能够灵活地即时合成新的目标函数。这一过程是如何实现的，能否赋予人工智能系统同样的能力？本文提出一种解答上述问题的方法，核心是引入‘主观函数’——一种内生于智能体的高阶目标函数，其定义基于智能体自身的特征，而非外部任务。文章以预期预测误差为例，探讨了主观函数的具体形式。该框架与心理学、神经科学及机器学习中的诸多思想具有深刻关联。
原文摘要 · Abstract (English)
Where do objective functions come from? How do we select what goals to pursue? Human intelligence is adept at synthesizing new objective functions on the fly. How does this work, and can we endow artificial systems with the same ability? This paper proposes an approach to answering these questions, starting with the concept of a subjective function, a higher-order objective function that is endogenous to the agent (i.e., defined with respect to the agent's features, rather than an external task). Expected prediction error is studied as a concrete example of a subjective function. This proposal has many connections to ideas in psychology, neuroscience, and machine learning.
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