SPOOK：一个面向概率对象的知识表示系统
SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation
摘要：
在以前的工作中，我们指出了标准贝叶斯网络作为大型复杂领域建模框架的局限性。我们提出了一种新的，结构丰富的建模语言，{em面向对象的贝叶斯Netorks}，我们认为将能够处理这些领域。然而，事实证明，OOBN的表达能力不足以对复杂领域的许多有趣方面进行建模：特定命名对象的存在，对象之间的任意关系以及领域结构的不确定性。这些方面在战场感知等现实领域至关重要。在本文中，我们提出了SPOOK，一个实现的系统，解决这些限制。SPOOK实现了一种更具表现力的语言，使其能够自然而紧凑地表示空间域。我们提出了一个新的推理算法，利用模型结构的基本方式，并表明经验，它实现了数量级的加速比现有的方法。
英文摘要：
In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, {em Object-oriented Bayesian Netorks}, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many interesting aspects of complex domains: the existence of specific named objects, arbitrary relations between objects, and uncertainty over domain structure. These aspects are crucial in real-world domains such as battlefield awareness. In this paper, we present SPOOK, an implemented system that addresses these limitations. SPOOK implements a more expressive language that allows it to represent the battlespace domain naturally and compactly. We present a new inference algorithm that utilizes the model structure in a fundamental way, and show empirically that it achieves orders of magnitude speedup over existing approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
