Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Query DAGs: a practical paradigm for implementing belief-network inference
Journal of Artificial Intelligence Research
Effective Bayesian inference for stochastic programs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Accounting for context in plan recognition, with application to traffic monitoring
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Topological parameters for time-space tradeoff
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Feature-based generators for time series data
WSC '05 Proceedings of the 37th conference on Winter simulation
Plan recognition for interface agents
Artificial Intelligence Review
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
New advances in logic-based probabilistic modeling by PRISM
Probabilistic inductive logic programming
Querying parse trees of stochastic context-free grammars
Proceedings of the 13th International Conference on Database Theory
A new model of plan recognition
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Probabilistic state-dependent grammars for plan recognition
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Goal recognition over POMDPs: inferring the intention of a POMDP agent
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Probabilistic context-free grammars (PCFGs) provide a simple way to represent a particular class of distributions over sentences in a context-free language. Efficient parsing algorithms for answering particular queries about a PCFG (i.e., calculating the probability of a given sentence, or finding the most likely parse) have been developed and applied to a variety of pattern-recognition problems. We extend the class of queries that can be answered in several ways: (1) allowing missing tokens in a sentence or sentence fragment, (2) supporting queries about intermediate structure, such as the presence of particular nonterminals, and (3) flexible conditioning on a variety of types of evidence. Our method works by constructing a Bayesian network to represent the distribution of parse trees induced by a given PCFG. The network structure mirrors that of the chart in a standard parser, and is generated using a similar dynamic-programming approach. We present an algorithm for constructing Bayesian networks from PCFGs, and show how queries or patterns of queries on the network correspond to interesting queries on PCFGs. The network formalism also supports extensions to encode various context sensitivities within the probabilistic dependency structure.