Reasoning under uncertainty is a central issue in artificial intelligence. Real-world agents must deal with noisy sensor information, non-deterministic effects of actions, and unpredictable exogenous events. Probabilistic reasoning methods, and Bayesian networks (BNs) in particular, have emerged as an effective and principled method for reasoning under uncertainty. BNs exploit conditional independence relationships to create natural and compact domain models, thereby supporting useful reasoning patterns, and providing effective probabilistic inference and learning algorithms. However, BNs are inherently limited by their attribute-based nature, making it difficult to apply them to large, complex domains.
This thesis addresses the issue of representing and reasoning about probabilistic models of complex systems. We believe that the key to reasoning effectively about complex systems is to provide a language that supports the expression of system structure. We present a powerful object-based representation language, that integrates logical and probabilistic representations. Our language provides the ability to create structured, modular probabilistic models. The language maintains the key advantages of BNs, exploiting conditional independence relationships. In addition, it is capable of representing other aspects of system structure not represented in BNs. In particular, it supports the decomposition of complex systems into weakly interacting subsystems, and the reuse of models for many different components of a system.
Another key benefit of our language is that it is very flexible. The same probabilistic representations can be applied in many different situations, with very different configurations. In fact, our language can even represent uncertainty over the system configuration itself, and integrate that uncertainty directly with uncertainty over the basic properties of objects in the system. Our framework also supports the representation of powerful recursive probability models.
We present inference algorithms for our language that exploit the structure that can be expressed in it—not only the conditional independence structure normally exploited by BN algorithms, but also encapsulation, reuse of computation and symmetry resulting from the object-based representation. We describe an implemented system that supports representation and reasoning with models in our language, and provide experimental results demonstrating the advantages of exploiting structure in inference.
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- Howard C and Stumptner M (2014). A Survey of Directed Entity-Relation--Based First-Order Probabilistic Languages, ACM Computing Surveys, 47:1, (1-40), Online publication date: 1-Jul-2014.
- Torti L, Gonzales C and Wuillemin P Patterns discovery for efficient structured probabilistic inference Proceedings of the 5th international conference on Scalable uncertainty management, (247-260)
- Howard C and Stumptner M (2009). Automated compilation of Object-Oriented Probabilistic Relational Models, International Journal of Approximate Reasoning, 50:9, (1369-1398), Online publication date: 1-Nov-2009.
- Jiang A, Leyton-Brown K and Pfeffer A Temporal action-graph games Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, (268-276)
- Koriche F (2008). Learning to assign degrees of belief in relational domains, Machine Language, 73:1, (25-53), Online publication date: 1-Oct-2008.
- Wang D, Michelakis E, Garofalakis M and Hellerstein J (2008). BayesStore, Proceedings of the VLDB Endowment, 1:1, (340-351), Online publication date: 1-Aug-2008.
- De Raedt L and Kersting K Probabilistic inductive logic programming Probabilistic inductive logic programming, (1-27)
- Narayanan S, Sievers K and Maiorano S OCCAM Proceedings of the 6th international and interdisciplinary conference on Modeling and using context, (356-368)
- Chakrapani L, Korkmaz P, Akgul B and Palem K (2008). Probabilistic system-on-a-chip architectures, ACM Transactions on Design Automation of Electronic Systems, 12:3, (1-28), Online publication date: 17-Aug-2007.
- Jaeger M Parameter learning for relational Bayesian networks Proceedings of the 24th international conference on Machine learning, (369-376)
- Howard C and Stumptner M Representation and reasoning for recursive probability models Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence, (120-130)
- Schubert L Turing's dream and the knowledge challenge proceedings of the 21st national conference on Artificial intelligence - Volume 2, (1534-1538)
- Howard C and Stumptner M Applying OPRMs to Recursive Probability Models Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy, (761-762)
- Chakrapani L, Akgul B, Cheemalavagu S, Korkmaz P, Palem K and Seshasayee B Ultra-efficient (embedded) SOC architectures based on probabilistic CMOS (PCMOS) technology Proceedings of the conference on Design, automation and test in Europe: Proceedings, (1110-1115)
- Getoor L and Grant J (2006). PRL, Machine Language, 62:1-2, (7-31), Online publication date: 1-Feb-2006.
- Howard C and Stumptner M Probabilistic reasoning techniques for the tactical military domain Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III, (46-53)
- Kersting K An Inductive Logic Programming Approach to Statistical Relational Learning Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning, (1-228)
- Narayanan S and Harabagiu S Question answering based on semantic structures Proceedings of the 20th international conference on Computational Linguistics, (693-es)
- Schubert L A new characterization of probabilities in Bayesian networks Proceedings of the 20th conference on Uncertainty in artificial intelligence, (495-503)
- De Raedt L and Kersting K (2003). Probabilistic logic learning, ACM SIGKDD Explorations Newsletter, 5:1, (31-48), Online publication date: 1-Jul-2003.
- Schmidt D Learning probabilistic relational models Relational Data Mining, (307-333)
- Langseth H and Bangsø O (2019). Parameter Learning in Object-Oriented Bayesian Networks, Annals of Mathematics and Artificial Intelligence, 32:1-4, (221-243), Online publication date: 27-Aug-2001.
- Pless D and Luger G Toward general analysis of recursive probability models Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence, (429-436)
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