A resource-based paradigm for the configuring of technical systems from modular components

Abstract

In the resource-based paradigm the interfaces through which technical systems, their coniponents and their environment interact are modelled as abstract resources, and each technical entity is characterized by the types and amounts of resources it supplies, consumes and uses. This intuitive model, derived in one application area, is shown to be in concordance with the design rationale of modular component syslems. A simple self-organizing configuring inference procedure for the resource-based paradigm, resource-balancing, with a description of the environment of the technical system as the requirement specification, is derived front the basic acceptance criterion for configurations. Five levels of knowledge are defined for this puradigm and introduced in a simple representation scheme which, through its inherent locality and mutuul isolation of component knowledge, allows efficient acquisition and maintenance of even large component knowledge bases. First experiences with the implementation and use of these ideas in the prototype shell COSMOS are reported. AI Topic: knowledge representation, inference, configuring Domain Area: modular component systems Language: CommonLISP + Frame Oriented Language KitStatus: field test of shell prototype, on-going research Effort: 3 PY Impact: novel basic model for the configuring task with a decisively improved maintainability of the knowledge base.

Online customization with configurable standard models

“…Similarity Score is the reverse of distance, and can be measured by one of the nearest-neighbor algorithm [7]. …”

Case reuse based product fuzzy configuration

“…Especially along with the rapid development of artificial intelligence application, numerous configuration methods are developed [1,2], such as rule-based approach [3,4] which uses rules to express product configuration knowledge, structure-based approach [5,6] which uses key components to express configuration task, constraint-based approach [7][8][9] which checks all the constraints of assignment for components, resource-based approach [10] which divides components into two species based on whether to produce resource or to consume resource, logic-based approach [11,12] which uses logic programming and weighted constraint rules language to express configuration problem, and so forth. These different approaches can be separated into two categories: knowledge-based which emphasizes the configuration knowledge representation and solution-based which emphasizes the configuration solution result.…”

Design Space Exploration for Rapid Product Realization

“…These representations are specified in a knowledge base and can be evaluated by constraint propagation [27][28][29][30]. Lin and Chen [31] integrate the performance criteria and functional coupling of concepts in an optimization framework for concept selection.…”