Labchart reader arithmetic smoothing verification#

The practical approach is to cluster rules and facts into associated 'blocks'. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Partitioning the KB reduces rule interaction complexity overall.

Labchart reader arithmetic smoothing verification#

This requires simple and automated approaches to the KB verification & validation task.

The second objective is to produce highly reliable intelligent systems. This reduced complexity improves performance but without undo impacts during load and unload cycles. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. Knowledge base rule partitioning design for CLIPS The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component.

In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. Many CLIPS rule-bases contain one or more rule groups that perform classification. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases.Īutomated rule-base creation via CLIPS-Induce CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases.