کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
397194 1438517 2007 26 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Inference in multi-agent causal models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Inference in multi-agent causal models
چکیده انگلیسی

In this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform causal inference, i.e. the calculation of the causal effect of one variable on other variables. We treat a state-of-the-art algorithm for performing causal inference that is based on a new factorization of the joint probability distribution and is a systematic approach for the calculation due to Tian and Pearl.We elaborate on the problems that can arise when working with a centralized approach and discuss how a decentralized cooperative multi-agent approach might overcome some of these problems.The main contribution of this article is the introduction of multi-agent causal models as a way to overcome the problems in a centralized setting. They are an extension of causal Bayesian networks to a distributed setting consisting of a number of agents each having access to an overlapping set of the variables. We extend a state-of-the-art causal inference algorithm for this particular domain. We will show that our approach is as powerful in computing causal effects as the centralized algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 46, Issue 2, October 2007, Pages 274-299