Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
OKBC: a programmatic foundation for knowledge base interoperability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A practical algorithm for finding optimal triangulations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
cbCPT: Knowledge Engineering Support for CPTs in Bayesian Networks
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Probabilistic Relational Models
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Representing and reasoning about mappings between domain models
Eighteenth national conference on Artificial intelligence
ACM SIGKDD Explorations Newsletter
PRL: A probabilistic relational language
Machine Learning
Ontological inference for image and video analysis
Machine Vision and Applications
Dynamically constructed Bayes nets for multi-domain sketch understanding
ACM SIGGRAPH 2006 Courses
Dynamically constructed Bayes nets for multi-domain sketch understanding
ACM SIGGRAPH 2007 courses
Force deployment analysis with generalized grammar
Information Fusion
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Bisimulation-based approximate lifted inference
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Probabilistic ontology trees for belief tracking in dialog systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Relational preference rules for control
Artificial Intelligence
Patterns discovery for efficient structured probabilistic inference
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Causal mechanism-based model constructions
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Hypothesis management in situation-specific network construction
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Toward general analysis of recursive probability models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Probabilistic reasoning techniques for the tactical military domain
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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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, Object-oriented Bayesian Networks, 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.