Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Structured representation of complex stochastic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Frame Problem and Bayesian Network Action Representation
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
ACM Transactions on Computational Logic (TOCL)
A new characterization of probabilities in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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For many years, probabilistic models were largely neglected within the AI community. Now they play a fundamental role in many areas in AI, including diagnosis, planning, and learning. One of the crucial reasons for this transition is the use of structured model-based representations such as Bayesian networks. Building on this idea, we can extend the success of probabilistic modeling to much more complex domains, ones involving many components that interact and evolve ove time. These domains are significantly beyond the scope of traditional Bayesian networks. I describe a broad class of structured probabilistic representations that extend Bayesian networks to deal with these new challenges. I argue that these representations can form the basis for agents that reason and act in complex uncertain environments.