摘要翻译：
可调整自治是指实体动态地改变自己的自治，在关键情况下将决策控制权转移给其他实体（通常是Agent将控制权转移给人类用户）。确定是否以及何时发生这种控制权转移可以说是可调整自治的基本研究问题。以前的工作已经研究了解决这个问题的各种方法，但通常集中在个体Agent与人的交互上。不幸的是，需要代理团队和人类之间协作的领域暴露了这些以前方法的两个关键缺点。首先，这些方法使用严格的一次性控制转移，这可能导致多智能体设置中不可接受的协调失败。其次，他们忽略了由于这种控制权转移而给代理人团队带来的成本（例如，在时间延迟或对行动的影响方面）。为了解决这些问题，本文提出了一种基于控制权转移策略的可调整自治的新方法。控制转移策略由两类行为的条件序列组成：(i)转移决策控制权的行为（例如，从agent转移到用户，或者反之亦然）和(ii)改变agent与团队成员预先规定的协调约束的行为，目的是最大限度地减少错误协调成本。目标是在对团队协调造成最小干扰的情况下做出高质量的个人决策。给出了控制转移策略的数学模型。该模型使用马尔可夫决策过程指导和通知策略的操作，该决策过程在给定不确定环境和个人和团队成本的情况下选择最优策略。该方法已经过仔细评估，包括通过它在现实世界中的使用，部署的多代理系统，协助研究小组的日常活动。
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英文标题：
《Towards Adjustable Autonomy for the Real World》
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作者：
D. V. Pynadath, P. Scerri, M. Tambe
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最新提交年份：
2011
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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：
  Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal two key shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects on actions) to an agent's team due to such transfers-of-control. To remedy these problems, this article presents a novel approach to adjustable autonomy, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from an agent to a user or vice versa) and (ii) actions to change an agent's pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high-quality individual decisions to be made with minimal disruption to the coordination of the team. We present a mathematical model of transfer-of-control strategies. The model guides and informs the operationalization of the strategies using Markov Decision Processes, which select an optimal strategy, given an uncertain environment and costs to the individuals and teams. The approach has been carefully evaluated, including via its use in a real-world, deployed multi-agent system that assists a research group in its daily activities.
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PDF链接：
https://arxiv.org/pdf/1106.4573