X-Breed: a multiple-domain knowledge based system integrated through a blackboard architecture

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

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Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound images

2012 19th IEEE International Conference on Image Processing ◽

10.1109/icip.2012.6467286 ◽

2012 ◽

Cited By ~ 10

Author(s):

Min Xian ◽

Jianhua Huang ◽

Yingtao Zhang ◽

Xianglong Tang

Keyword(s):

Domain Knowledge ◽

Breast Ultrasound ◽

Tumor Segmentation ◽

Ultrasound Images ◽

Knowledge Based ◽

Multiple Domain

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A knowledge-based system for the conceptual design of grippers for handling fabrics

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060499131020 ◽

1999 ◽

Vol 13 (1) ◽

pp. 13-25 ◽

Cited By ~ 11

Author(s):

V.C. MOULIANITIS ◽

A.J. DENTSORAS ◽

N.A. ASPRAGATHOS

Keyword(s):

Conceptual Design ◽

Domain Knowledge ◽

Heuristic Method ◽

Search Method ◽

Knowledge Based System ◽

Design Concepts ◽

Knowledge Based ◽

Reasoning Strategy ◽

Heuristic Search Method ◽

Feasible Solutions

The paper presents a knowledge-based system (KBS) for the conceptual design of grippers for handling fabrics. Its main purpose is the integration of the domain knowledge in a single system for the systematic design of this type of grippers. The knowledge presented, in terms of gripper, material and handling process, are classified. The reasoning strategy is based upon a combination of a depth-first search method and a heuristic method. The heuristic search method finds a final solution from a given set of feasible solutions and can synthesize new solutions to accomplish the required specifications. Details of the main features of the system are given, including its ability to take critical design decisions according to four criteria, weighted by the designer. The knowledge-based system was implemented in the Kappa P. C. 2.3.2 environment. Two examples are given to illustrate some critical aspects concerning the KBS development, to explain the operation of the proposed searching heuristic method, and to show its effectiveness in producing design concepts for grippers.

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Repair Learning for a Working Knowledge-Based System

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1243/pime_proc_1995_209_085_02 ◽

1995 ◽

Vol 209 (4) ◽

pp. 287-294

Author(s):

P Olley ◽

A K Kochhar

Keyword(s):

Systematic Error ◽

Knowledge Base ◽

Domain Knowledge ◽

Closed Loop ◽

Noisy Data ◽

Simulated Data ◽

Learning Method ◽

Learning Mechanism ◽

Knowledge Based System ◽

Knowledge Based

This paper addresses the issues of using a learning mechanism for closed-loop updating of the repair knowledge base of a working knowledge-based system (KBS). Issues addressed are stability under noisy data and errors arising from learning from cases in which several repairs are attempted. Simulated data are used to investigate the effects of the latter feature. It is shown that the learning method can cause a significant systematic error in learnt knowledge. A knowledge-based method, which aims to intelligently compensate for the systematic error using diagnostic domain knowledge, is investigated. It is shown that the method greatly reduces the systematic error in learnt repair knowledge.

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