S. Kluska-Nawarecka

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 Simulation in Finite Element Environment

Abstract The simulation of real processes with the use of the Finite Element Method are commonly used in practice. But there are a group of phenornena that are hardly sirIlulated with FEM such as the is simulation of dynamic changes of the parameters and FEM structure of the simulated processes. Replacing the FEM by an multiagent system where the finite elernents rnakes an environrnent for the agents we may obtain dynanlic modification of the systenl st, ructure and parameters upon simulation. Basic concepts of system irnplenlenting the rnethod are shown, and sorne resolved problems are presented.

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.

Creative Expert System: Comparison of Proof Searching Strategies

This paper presents comparison of time cost of three proof searching strategies in a creative expert system. Initially, model of the creative expert system and inference algorithm are proposed. The algorithm searches for a proof up to a given maximal depth, using one of the following strategies: finding all possible proofs, finding the first proof by depth-first and finding the first proof by breadth-first. Calculation time is measured in inference scenarios from a casting domain. Creativity of the expert system is achieved thanks to integration of inference and machine learning. The learning algorithm can be automatically executed during inference process, because its execution is formalized as a complex inference rule. Such a rule can be fired during inference process. During execution, training data is prepared from facts already stored in the knowledge base and new implications are learned from it. These implications can be used in the inference process. Therefore, it is possible to infer decisions in cases not covered by the knowledge base explicitly.

Computer-Assisted Methods of the Design of New Materials in the Domain of Copper Alloy Manufacturing

The design of a new copper alloy to obtain material characterized by the expected mechanical properties requires experiments, which enable testing the influence of different processing techniques (e.g. heat treatment operations) on the investigated material. This work consumes both time and money, and hence is a real obstacle in situations, when the researcher has limited resources for preparing and testing only a couple of copper alloy samples. The process of design and testing of a new copper alloy can be speeded up by the use of computing methods, which can be helpful especially in the prime choice of material and its processing technique. The, investigated in this work, methodologies have been chosen from the domain of Artificial Intelligence allowing for a specific nature of the experimentally obtained data, which is usually incomplete in respect of the whole knowledge concerning the studied phenomena. Application of data mining techniques (the theory of rough sets and algorithms of rules or decision trees induction) enable constructing a knowledge base as a set of rules expressing the influence of different processing techniques on mechanical properties of the tested material. Case-based reasoning (CBR), as a methodology focused on the solution of problem together with sustained learning, enables us to build an advisory system giving advice on material design and learning on experimentally obtained results. The data mining techniques and CBR methodology complement each other – the data mining techniques allow generalization of knowledge related to the performed experiments, while CBR uses knowledge in the form of individual experimental items (cases).

Agent-based Approach to WEB Exploration Process

The paper contains the concept of agent-based search system and monitoring of Web pages. It is oriented at the exploration of limited problem area, covering a given sector of industry or economy. The proposal of agent-based (modular) structure of the system is due to the desire to ease the introduction of modifications or enrichment of its functionality. Commonly used search engines do not offer such a feature.The second part of the article presents a pilot version of the WEB mining system, represent- ing a simplified implementation of the previously presented concept. Testing of the implemented application was executed by referring to the problem area of foundry industry.

Formalisms and Tools for Knowledge Integration Using 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.