Amalia is a generator framework for constructing analyzers for operationally defined formal notations. These generated analyzers are components that are designed for customization and integration into a larger environment. The customizability and efficiency of Amalia analyzers owe to a computational structure called an inference graph. This paper describes this structure, how inference graphs e...

Human neo-cortex can be viewed as a modality invariant system for pattern discovery and associative learning. Similarly, research in the field of distributional learning suggests that much of human language acquisition can be explained by generic statistical learning mechanisms. The current paper argues that pattern processing capabilities of the human brain can be better understood if the proc...

To fully understand sequential effects on learning in humans, we need a comprehensive theory of cognition. Such a theory should be complete enough to perform the task of interest and to learn like humans while doing so. These theories, called process models or cognitive models, can be broken down into aspects that do not change between tasks — the architecture — and aspects that do change — the...

We use the Abstract State Machine methodology to give formal operational semantics for the Location Consistency memory model and cache protocol. With these formal models, we prove that the cache protocol satisfies the memory model, but in a way that is strictly stronger than necessary, disallowing certain behavior allowed by

We present a method for analyzing the dynamics of a Z document describing a non-sequential system. First a formal operational semantics based on the chemical metaphor is given to Z. Then, some Unity-like temporal logic constructs are deened on such a formal operational semantics in order to allow the speciication and analysis of dynamic and temporal properties of concurrent systems, such as saf...