Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm

Abstract

Abstract-In this paper, we develop a technique for acquiring the finite set of attributes or variables which the expert uses in a classification problem for characterising and discriminating a set of elements. This set will constitute the schema of a training data set to which an inductive learning algorithm will be applied. The technique developed uses ideas taken from psychology, in particular from Kelly's Personal Construct Theory. While we agree that Kelly's repertory grid technique is an efficient way to do this, it has several disadvantages which we shall try to solve by using a fuzzy repertory table. With the suggested technique, we aim to obtain the set of attributes and values which the expert can use to "measure" the object type (class) on the classification problem in some way. We will also acquire some general rules to identify the expert's evident knowledge; these rules will comprise concepts belonging to their conceptual structure.Index Terms-Expert systems, fuzzy repertory grid, knowledge acquisition, knowledge-based systems.

A Multiagent Architecture for 3d Rendering Optimization

“…• Rendering Agent: This agent is capable of performing the rendering of tasks by applying a knowledge base acquired through the knowledge acquisition subsystem or tool (Castro-Schez et al 2004) or learned from previous experiences using machine learning algorithms (Castro et al 1999) to optimize this process by adjusting relevant rendering parameters. As is shown in Figure 2, a Rendering Agent is a specialization of the FIPA standard agent so that all the functionality and requirements specified by FIPA are inherited by the rendering agent.…”

Fuzzy complexity assessment model for resource negotiation and allocation in agent-based software testing framework

“…Here we developed a complexity assessment scheme with 19 attributes that characterize the product and the system with respect to their complexity as in Table 1. This scheme extends the Fuzzy Repertory Table (FRT) approach (Castro-Schez et al, 2004) to assess the 'absolute complexity' and 'relative complexity' of the products. In software testing, the term 'complexity' associated with agent-based approach refers the complexity of the 'system' (testing tool which is intended to develop using agent-based approach) as well as the complexity of the 'application environment or product' (software which is to be tested using the system).…”

“…In the FRT approach (Castro-Schez et al, 2004), each attribute can be expressed by a trapezoid number (a,b,c,d) which is a fuzzy set that has a membership function as in (4). The values for the trapezoid number will be assigned by the objective observer based on the characteristics of the products.…”

“…Some of the attributes takes on linguistic terms since the user found it more natural to describe it with a vague value. Also FRT provides categorical and numerical data types that may be given by means of linguistic terms (Castro-Schez et al, 2004):…”

“…The multi-agent system illustrated here is on the basis of few basic operational real-world testing techniques (Basali & Selby, 1985), as an attempt to describe how to practice agent-based software testing, which has not previously done. The advantages of multiagent systems are that they can compartmentalize specialized task knowledge, organize them to avoid processing bottlenecks, and can be built expressly to deal with dynamic changes in the agent environment (Castro-Schez, Castro, & Zurita, 2004).…”