摘要翻译：
交互式部分可观测马尔可夫决策过程（I-POMDP）是最近发展起来的一个框架，它通过在状态空间中包含agent模型，将POMDP扩展到多agent环境中。本文将agent向教师交互学习的问题描述为一个I-POMDP问题，其中学习的agent编程被agent状态空间中的随机变量捕获，来自教师的所有信号被视为观察到的随机变量，教师被建模为一个独立的agent，在agent的状态空间中被显式地表示。这种方法的主要好处是：i。有原则的行动选择机制。有原则的信念更新机制。支持最常见的教师\emph{signals}，和iv。预期产生的复杂的有益的相互作用。提出了所建议的公式，它的好处，和几个有待解决的问题。
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英文标题：
《Learning from Humans as an I-POMDP》
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作者：
Mark P. Woodward and Robert J. Wood
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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：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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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：
  The interactive partially observable Markov decision process (I-POMDP) is a recently developed framework which extends the POMDP to the multi-agent setting by including agent models in the state space. This paper argues for formulating the problem of an agent learning interactively from a human teacher as an I-POMDP, where the agent \emph{programming} to be learned is captured by random variables in the agent's state space, all \emph{signals} from the human teacher are treated as observed random variables, and the human teacher, modeled as a distinct agent, is explicitly represented in the agent's state space. The main benefits of this approach are: i. a principled action selection mechanism, ii. a principled belief update mechanism, iii. support for the most common teacher \emph{signals}, and iv. the anticipated production of complex beneficial interactions. The proposed formulation, its benefits, and several open questions are presented.
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PDF链接：
https://arxiv.org/pdf/1204.0274