Matching is the process of comparing two or more structures to discover their likenesses or differences. It is a common operation performed in symbolic classification, pattern recognition, data mining and expert systems. The definition of a matching operator for Boolean symbolic objects is important for the development of symbolic data analysis techniques. In this paper we give the definition o...

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate exp...

Symbolic pattern solving is a new approach for nding symbolic solutions of equa-tional constraints over arbitrary algebraic structures. Despite the deep results and methods already produced in equational constraint solving, real-world applications still cannot be tackled. To reduce the original complexity, the approach advocated here uses the solution pattern, the skeleton of the solution often...

This paper presents a failure diagnosis algorithm for summarizing and generalizing patterns that lead to instances of anomalous behavior in sensor networks. Often multiple seemingly different event patterns lead to the same type of failure manifestation. A hidden relationship exists, in those patterns, among event attributes that is somehow responsible for the failure. For example, in some syst...

Pattern recognition (recognizing a pattern from inputs) and recall (describing or predicting the inputs associated with a recognizable pattern) are essential for neural-symbolic processing and cognitive capacities. Without them the brain cannot interact with the world e.g.: form internal representations and recall memory upon which it can perform logic and reason. Neural networks are efficient,...