A Simple Model for Sequences of Relational State Descriptions
A Simple Model for Sequences of Relational State Descriptions
复制标题
关系状态描述序列的简单模型
DOI：
10.1007/978-3-540-87481-2_33
复制
发表时间：
2008
期刊：
影响因子：
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通讯作者：
L. D. Raedt
中科院分区：
文献类型：
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作者：
Ingo Thon;Niels Landwehr;L. D. Raedt
人工智能旨在开发能够在复杂环境中学习和行动的智能体。现实环境通常具有可变数量的对象、它们之间的关系以及不确定的转换行为。标准概率序列模型提供了有效的推理和学习技术，但通常无法完全捕获关系复杂性。另一方面，统计关系学习技术通常效率太低。在本文中，我们提出了一个简单的模型，该模型在表达性/效率权衡中占据中间位置。它基于 CP-logic，一种用于建模因果关系的表达性概率逻辑。然而，通过专门化 CP 逻辑来表示关系状态描述序列上的概率分布，并采用马尔可夫假设，推理和学习变得更加容易处理和有效。我们表明，所得模型能够处理具有大量对象和关系的概率关系域。
Artificial intelligence aims at developing agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. Standard probabilistic sequence models provide efficient inference and learning techniques, but typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient. In this paper, we present a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic, an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions, and employing a Markov assumption, inference and learning become more tractable and effective. We show that the resulting model is able to handle probabilistic relational domains with a substantial number of objects and relations.