[计算机科学] 基于POMDP的知识工程的关系方法 系统作为心理学模型的翻译 [推广有奖]

摘要翻译：
为认知障碍者（例如痴呆症）建立辅助系统是困难的，因为人们可以采取各种各样的不同方法来完成同样的任务，而且由于客户行为的不可预测性和传感器读数中的噪音而产生的重大不确定性。部分可观察马尔可夫决策过程(POMDP)模型已经成功地被用作此类辅助系统背后的推理引擎，用于诸如洗手等小型多步骤任务。POMDP模型是一个强大而灵活的建模辅助框架，可以处理不确定性和实用性。不幸的是，POMDPs的定义和构造通常需要非常劳动密集型的人工程序。我们以前的工作描述了一种知识驱动的方法，用于自动生成复杂任务的POMDP活动识别和上下文敏感提示系统。我们将产生的POMDP称为SNAP（SyNdetic Assistance Process）。类似电子表格的分析结果与POMDP模型并不直接对应，需要将其转换为正式的POMDP表示。到目前为止，这个翻译必须由训练有素的POMDP专家手工执行。在本文中，我们使用一个编码在关系数据库中的概率关系模型(PRM)来形式化和自动化这个翻译过程。我们通过从非专家中引出三个辅助任务来演示该方法。我们用基于案例的仿真来验证所得到的POMDP模型，以表明它们对该领域是合理的。我们还展示了一个设计人员指定一个数据库的完整案例研究，包括在一个人类演员的现实生活实验中的评估。
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英文标题：
《Relational Approach to Knowledge Engineering for POMDP-based Assistance
  Systems as a Translation of a Psychological Model》
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作者：
Marek Grzes and Jesse Hoey and Shehroz Khan and Alex Mihailidis and
  Stephen Czarnuch and Dan Jackson and Andrew Monk
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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：
  Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor.
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PDF链接：
https://arxiv.org/pdf/1206.5698