D. Wilk-Kołodziejczyk

Rough sets applied to the roughcast system for steel castings

Rough logic and rough sets theory are mainly devoted to analysis of incomplete, uncertain, and inconsistent data. An exploited in the article result of the theory is the so-called rough information system that gives a model for dealing with the situation in which some real objects are represented ambiguously via defined approximation (alternatives of values of the objects attributes). The proposed rough system opens a way to process such information and moreover is equipped with a powerful query language and possibility to classify real objects based on their stored rough characteristics. The implemented RoughCast system and, especially prepared for the case, knowledge base for steel casting defects form such the rough information system. They can be successfully applied in the process of knowledge integration concerning production of steel castings and, in consequence, as a tool in solving technological problems in operating foundries.

Intelligent Information System Based on Logic of Plausible Reasoning

The chapter presents a methodology for the application of a formalism of the Logic of Plausible Reasoning (LPR) to create knowledge about a specific problem area. In this case, the methodology has been related to the task of obtaining information about the innovative casting technologies. In the search for documents, LPR gives a much greater expressive power than the commonly used keywords. The discussion is illustrated with the results obtained using a pilot version of the original information tool. It also presents a description of intelligent information system based on the LPR and the results of tests on the functionality and performance parameters of the system.

Practical Aspects of Knowledge Integration Using Attribute Tables Generated from Relational Databases

Until now, the use of attribute tables, which enable approximate reasoning in tasks such as knowledge integration, has been posing some difficulties resulting from the difficult process of constructing such tables. Using for this purpose the data comprised in relational databases should significantly speed up the process of creating the attribute arrays and enable getting involved in this process the individual users who are not knowledge engineers. This article illustrates how attribute tables can be generated from the relational databases, to enable the use of approximate reasoning in decision-making process. This solution allows transferring the burden of the knowledge integration task to the level of databases, thus providing convenient instrumentation and the possibility of using the knowledge sources already existing in the industry. Practical aspects of this solution have been studied on the background of the technological knowledge of metalcasting.

Expert System with Web Interface Based on Logic of Plausible Reasoning

The paper presents an expert system based on Logic of Plausible Reasoning (LPR). This formalism reflects human ways of knowledge representation and reasoning. The knowledge is modeled using several kinds of formulas representing statements, hierarchies, similarities, dependencies and implications. Several types of inference patterns are defined. Knowledge uncertainty can be modeled. The paper is structured as follows. Research related to LPR is presented. Next, the formalism is introduced and a Web-based application, which was developed for this research, is described. Finally, a case study is presented – a prototype expert system which recommends a material and a technology for a casting process.

Agent-based Decision Support System for Technology Recommendation

Abstract This paper presents an idea of a multi-agent decision support system. Agent-based technology allows for decentralized problem solving and creating complex decision support systems, mixing various processing techniques, such as simulation, reasoning and machine learning and allows for distributed knowledge. Our main contribution is an agent-based architecture for decision support systems which is an agent-based implementation of a labeled deductive system. Such approach allows to decompose an inference algorithm into separate modules and distribute knowledge base into parts. The system is tested on a domain of material choice support for casting.

Reasoning Algorithm for a Creative Decision Support System Integrating Inference and Machine Learning

In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.

Creative Expert System: Result of Inference and Machine Learning Integration

This paper presents an idea of a creative expert system. It is based on inference and machine learning integration. Execution of learning algorithm is automatic because it is formalized as applying a complex inference rule. Firing such a rule generates intrinsically new knowledge: rules are learned from training data, which consists of facts stored already in the knowledge base. This new knowledge may be used in the same inference chain to derive a decision. Complex rules may also represent other procedural activities, like searching databases. Such a solution makes the reasoning process more creative and allows to continue reasoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate model and inference algorithm are proposed. The idea is tested on a decision support system in a casting domain.

Agent-Based Decision-Information System Supporting Effective Resource Management of Companies

The aim of the work is to propose a universal multi-agent environment for resource management in the enterprise. The system being developed is to be useful for employees of various divisions of the company: device operators, engineering staff optimizing the production process and senior management. The paper describes the architecture of the solution, which has a layered structure. The environment uses advanced techniques of artificial intelligence, including machine learning and negotiation algorithms. In the evaluation part, an implementation of a pilot version of the foundry management system is presented and a study of selected test scenarios is carried out.

Integrated Multi-functional LPR Intelligent Information System

An intelligent information system based on the use of LPR formalism , integrating basic features such as access to knowledge, reasoning, search, and expert advice, is presented. This system has been implemented and tested in the Department of Computer Science at the AGH University of Science and Technology. The methodology for the system use has been exemplified in the area of the foundry industry by the selection and conversion of technologies for making products from ADI.

The decision support system based on logic of plausible reasoning

The intention of this work is to show how logic of plausible reasoning (LPR) can be successfully used for solving complex decision problems. In the following sections a decision involve the use of LPR is described, and then its operation was illustrated on the example of an expertize concerning the choice of technology for making metal products. A knowledge base on the examined group of materials is presented and practical functioning of the system for the choice of a metal processing technology is disclosed. Typical scenarios of the system usage are presented, serving at the same time as a tool to verify its functionality. In particular, it is expected to create a module allowing automatic generation of rules to the knowledge base, and introduce machine learning to achieve optimal parameters of the inference process. The approach proposed in this study can be applied to a broad class of metal products, but in every case it should take into account the speciffic nature of a particular group of products and technological parameters of the materials used.