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摘要翻译：
Orseau和Ring以及Dewey最近用效用函数的各种定义描述了代理行为的问题，包括自我妄想。Agent的效用函数是根据Agent与其环境交互的历史来定义的。本文通过两个实例说明，通过两个步骤建立效用函数可以避免行为问题：1）从相互作用中推断环境模型；2）计算效用作为环境模型的函数。将效用函数建立在代理必须学习的模型上意味着效用函数最初必须用与学习的模型中的结构相匹配的规范来表示。这些规范构成了对环境的预先假设，因此这种方法不适用于任意环境。但这种方法应该适用于人类设计的在物理世界中行动的代理。本文还讨论了Agent的自修改问题，并证明了在一些通常的假设下，如果Agent有修改效用函数的可能性，则Agent不会选择修改效用函数。
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英文标题：
《Model-based Utility Functions》
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作者：
Bill Hibbard
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
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PDF链接：
https://arxiv.org/pdf/1111.3934
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