A Software Architecture for Knowledge-Based Systems

. The paper introduces a software architecture for the specification and verification of knowledge-based systems combining conceptual and formal techniques. Our focus is component-based specification enabling their reuse. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter. We present algebraic specifications and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our architecture we can decompose the overall specification and verification task of the knowledge-based systems into subtasks. We identify different subcomponents for specification and different proof obligations for verification. The use of the architecture in specification and verification improves understandability and reduces the effort for both activities. In addition, its decomposition and modularisation enables reuse of components and proofs. Ther..

Knowledge modelling for the motion detection task

In this article knowledge modelling at the knowledge level for the task of moving objects detection in image sequences is introduced. Three items have been the focus of the approach: (1) the convenience of knowledge modelling of tasks and methods in terms of a library of reusable components and in advance to the phase of operationalization of the primitive inferences; (2) the potential utility of looking for inspiration in biology; (3) the convenience of using these biologically inspired problem-solving methods (PSMs) to solve motion detection tasks. After studying a summary of the methods used to solve the motion detection task, the moving targets in indefinite sequences of images detection task is approached by means of the algorithmic lateral inhibition (ALI) PSM. The task is decomposed in four subtasks: (a) thresholded segmentation; (b) motion detection; (c) silhouettes parts obtaining; and (d) moving objects silhouettes fusion. For each one of these subtasks, first, the inferential scheme is obtained and then each one of the inferences is operationalized. Finally, some experimental results are presented along with comments on the potential value of our approach