Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Unified theories of cognition
Investigating production system representations for non-combinatorial match
Artificial Intelligence
A systematic methodology for cognitive modelling
Artificial Intelligence - Special volume on empirical methods
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
SAL: an explicitly pluralistic cognitive architecture
Journal of Experimental & Theoretical Artificial Intelligence - Pluralism and the Future of Cognitive Science
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Composable probabilistic inference with b(laise)
Composable probabilistic inference with b(laise)
Extending the Soar Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
A unified cognitive architecture for physical agents
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A general method for reducing the complexity of relational inference and its application to MCMC
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
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A universal weak method: summary of results
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Implementing First-Order Variables in a Graphical Cognitive Architecture
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
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Cognitive architectures need to resolve the diversity dilemma - i.e., to blend diversity and uniformity - in order to couple functionality and efficiency with minimality, integrability, extensibility and maintainability. Building diverse architectures upon a uniform implementation level of graphical models is an intriguing approach because of the homogeneous manner in which such models produce state-of-the-art algorithms spanning symbol, probability and signal processing. To explore this approach a hybrid (discrete and continuous) mixed (Boolean and Bayesian) variant of the Soar architecture is being implemented via graphical models. Initial steps reported here, including a graphical implementation of production match and the beginnings of a mixed decision cycle incorporating a simple semantic memory, begin to show the potential of such an approach for cognitive architecture.