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摘要翻译：
Agent的行为影响世界的方式通常可以使用一组关系概率规划规则来紧凑地建模。本文研究了多个相关任务的规则集学习问题。我们采用分层贝叶斯方法，在该方法中，系统学习规则集上的先验分布。我们提出了一类由规则集原型参数化的先验分布，并对其进行随机修改以生成特定于任务的规则集。我们还描述了一种坐标上升算法，该算法迭代优化任务特定规则集和先验分布。实验表明，从相关任务传递信息可以显著减少预测块域中动作效果所需的训练数据量。
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英文标题：
《Learning Probabilistic Relational Dynamics for Multiple Tasks》
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作者：
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer, Leslie Pack
Kaelbling
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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：
The ways in which an agent's actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of learning such rule sets for multiple related tasks. We take a hierarchical Bayesian approach, in which the system learns a prior distribution over rule sets. We present a class of prior distributions parameterized by a rule set prototype that is stochastically modified to produce a task-specific rule set. We also describe a coordinate ascent algorithm that iteratively optimizes the task-specific rule sets and the prior distribution. Experiments using this algorithm show that transferring information from related tasks significantly reduces the amount of training data required to predict action effects in blocks-world domains.
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PDF链接：
https://arxiv.org/pdf/1206.5249
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