Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
A general identification condition for causal effects
Eighteenth national conference on Artificial intelligence
Identifiability of Causal Effects in a Multi-Agent Causal Model
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
An Agent-Based Approach to Distributed Data and Information Fusion
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Distributed learning of Multi-Agent Causal Models
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
On the testable implications of causal models with hidden variables
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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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.