Artificial Intelligence
A model for reasoning about persistence and causation
Computational Intelligence
Representing Bayesian networks within probabilistic Horn abduction
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Reasoning about knowledge
On the hardness of approximate reasoning
Artificial Intelligence
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
A New Lagrangian Relaxation Based Algorithm for a Class ofMultidimensional Assignment Problems
Computational Optimization and Applications
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
Ambiguity and constraint in mathematical expression recognition
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exploiting the architecture of dynamic systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The syntactic process
Multisensor Data Fusion
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
The formal consequences of using variables in CCG categories
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Relating complexity to practical performance in parsing with wide-coverage unification grammars
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A parsing: fast exact Viterbi parse selection
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Non-Generative Grammatical Models for Document Analysis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Log-linear models for wide-coverage CCG parsing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Maximum entropy estimation for feature forests
HLT '02 Proceedings of the second international conference on Human Language Technology Research
High performance reasoning with very large knowledge bases: a practical case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
MAP complexity results and approximation methods
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in 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
Applications of description logics: state of the art and research challenges
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
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We analyse the key algorithms of data and information fusion from a linguistic point of view, and show that they fall into two paradigms: the primarily syntactic, and the primarily semantic. We propose an alternative grammatical paradigm which exploits the ability of grammar to combine syntactic inference with semantic representation. We generalize the concept of formal generative grammar to include multiple rule classes each having a topology and a base vocabulary. A generalized Chomsky hierarchy is defined. Analysing fusion algorithms in terms of grammatical representations, we find that most (including multiple hypothesis tracking) can be expressed in terms of conventional regular grammars. Situation analysis, however, is commonly attempted using first order predicate logic, which while expressive, is recursively enumerable and so scales badly. We argue that the core issue in situation assessment is force deployment assessment, the extraction and scoring of hypotheses of the force deployment history, each of which is a multiresolution account of the activities, groupings and interactions of force components. The force deployment history represents these relationships at multiple levels of granularity and is expressed over time and space. We provide a grammatical approach for inferring such histories, and show that they can be estimated accurately and scalably. We employ a generalized context-free grammar incorporating both sequence and multiset productions. Elaborating [D. McMichael, G. Jarrad, S. Williams, M. Kennett, Grammatical methods for situation and threat analysis, in: Proceedings of The 8th International Conference on Information Fusion, Philadelphia, PA, 2005], a Generalized Functional Combinatory Categorial Grammar (GFCCG) is described that is both generalized and semantically functional (in that the semantics can be calculated directly from the syntax using a small number of rules). Force deployment modelling and parsing is demonstrated in naval and air defence scenarios. Simulation studies indicate that the method robustly handles the errors introduced by trackers under noisy cluttered conditions. The empirical time complexity of batch force deployment parsing is better than O(N^1^.^5), where N is the number of track segments. Force deployment assessments are required in real-time, and we have developed an incremental parser that keeps up with real-time data, and fulfils at Level 2 in the JDL fusion hierarchy the role that trackers fulfil at Level 1.