A natural language for implementing algebraically Expert Systems

A CAS aid to the elaboration of the expert system supporting the managing the monolitic construction

We present a computational method in which modular and Groebner bases (GB) computation in Boolean rings are used for solving problems in Boolean gene regulatory networks (BN). In contrast to other known algebraic approaches, the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulting in a significant improvement in running time and memory space consumption. We also show how calculation in temporal logic for model checking can be done by means of our direct and efficient Groebner basis computation in Boolean rings. We present our experimental results in finding attractors and control strategies of Boolean networks to illustrate our theoretical arguments. The results are promising. Our algebraic approach is more efficient than the state-of-the-art model checker NuSMV on BNs. More importantly, our approach finds all solutions for the BN problems.

This article reveals the purpose of expert training systems in linguistics and their application in learning processes. The topic is one of the less developed ones in modern linguistics. Creating and using computer programs is important while learning a foreign language outside the language environment. It is relevant to computer linguistics, a new area of linguistics. Expert training systems facilitate the development of studied languages and allow the maximum use of the acquired linguistic knowledge in practice. An expert training system can be called a program system that implements a specific pedagogical goal based on object knowledge of subject and meta-knowledge of experts in a specific subject area. Expert systems, in turn, with the help of information received from an expert, can partially solve the problem of creating a natural language environment. A list of tasks performed by expert systems, such as the interpretation of the source data, the architecture of the objects and the stages of monitoring, is proposed. Also, the presentation of knowledge in expert training systems and the conditions for their conceptual design are discussed. At the end of the study, the author concludes that the expert system of teaching a foreign language is a multifunctional toolkit for processing and evaluating various types of knowledge. This knowledge is acceptable for linguodidactics, computer linguistics and linguistics in general. It provides the opportunity for the most profitable language environment for learning a foreign language.

The XTRA access system is aimed at making the interaction with expert systems much easier for inexperienced users. It communicates with the user in a natural language (German) extracting data relevant to the expert system from the natural language input. It answers users' queries concerning the terminology used and provides what the developers describe as “User‐accommodated natural‐language verbalisations of results and explanations provided by the expert system”. This development is described by J. Allgayer, K. Harbusch, A. Kobsa, C. Reddig, N. Reithinger and D. Schmauks in the International Journal of Man‐Machine Studies, Vol. 31, Part 2, August 1989, pp. 161–95. The developers of this system have introduced a number of novel artificial intelligence techniques. These have included the combination of natural language user input and user gestures on the terminal screen. Four different knowledge sources aid referent identification and simultaneous communication of the access system with the user and the expert system have been incorporated.

As environmental regulations become more stringent, manufacturers are confronted with the of productdesign considering environmental impacts. Life Cycle Analysis (LCA) is a developing technique that attacks this problem. LCAs provide environmental information for decision making by consumers, manufacturers, and governments. These decisions significantly affect the ability to maximize source reduction, re-use, recyclability, and recycled content. LCAs can aid in diminishing the negative impact a product has on the environment and can be utilized as a partial solution of our nations' (worlds') growing waste management problem. To date, the application of LCAs has varied from product comparison to process development and improvement. The results from these analyses have differed because of differing assumptions and differing choices of boundary conditions. An appropriate Life Cycle Analysis should be consistent across products, locations, and especially across experimenters. The development of LCAs lends itself to computer-assisted design methods such as the techniques of Expert Systems, due to the large amount of information associated with successfully developing an LCA. In this paper, we will discuss using Hierarchical Classification, a specific task specific architecture expert systems approach, to organize the terms necessary in performing an LCA. Although not yet definitive, we believe our experience indicates that such approaches to organizing the vast knowledge necessary to accomplish an LCA may be a key factor in eventual successful development of comprehensive LCAs in industry.

We obtain a parametric (and an orbital) normal form for any non-degenerate perturbation of the generalized saddle-node case of Bogdanov--Takens singularity. Explicit formulas are derived and greatly simplified for an efficient implementation in any computer algebra system. A Maple program is prepared for an automatic parametric normal form computation. A section is devoted to present some practical formulas which avoid technical details of the paper.

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

Much research in computational linguistics and artificial intelligence has gone into characterizing natural language discourse structures, developing discourse models, and using these to develop more useful natural language interfaces to databases and expert systems. However, this knowledge about the nature of discourse has not been used to any advantage in graphical expert system interfaces. This paper proposes that discourse elements and discourse models used in the world of natural language understanding can be extended to graphical expert system interfaces, promoting continuity of interactions and allowing relationships among interactions to be exploited, redundancy reduced, operations tracked, and higher-level operations recognized. fj-^-, V' 1 f, T 1 4^ ^'' Jj-v' *. - fci L

Formal verification involves the use of logical and computational methods to establish claims that are expressed in precise mathematical terms. These can include ordinary mathematical theorems, as well as claims that pieces of hardware or software, network protocols, and mechanical and hybrid systems meet their specifications. In practice, there is not a sharp distinction between verifying a piece of mathematics and verifying the correctness of a system: formal verification requires describing hardware and software systems in mathematical terms, at which point establishing claims as to their correctness becomes a form of theorem proving. Conversely, the proof of a mathematical theorem may require a lengthy computation, in which case verifying the truth of the theorem requires verifying that the computation does what it is supposed to do. The gold standard for supporting a mathematical claim is to provide a proof, and twentieth-century developments in logic show most if not all conventional proof methods can be reduced to a small set of axioms and rules in any of a number of foundational systems. With this reduction, there are two ways that a computer can help establish a claim: it can help find a proof in the first place, and it can help verify that a purported proof is correct. Automated theorem proving focuses on the “finding” aspect. Resolution theorem provers, tableau theorem provers, fast satisfiability solvers, and so on provide means of establishing the validity of formulas in propositional and first-order logic. Other systems provide search procedures and decision procedures for specific languages and domains, such as linear or nonlinear expressions over the integers or the real numbers. Architectures like SMT (“satisfiability modulo theories”) combine domain-general search methods with domain-specific procedures. Computer algebra systems and specialized mathematical software packages provide means of carrying out mathematical computations, establishing mathematical bounds, or finding mathematical objects. A calculation can be viewed as a proof as well, and these systems, too, help establish mathematical claims. Automated reasoning systems strive for power and efficiency, often at the expense of guaranteed soundness. Such systems can have bugs, and it can be difficult to ensure that the results they deliver are correct. In contrast, interactive theorem proving focuses on the “verification” aspect of theorem proving, requiring that every claim is supported by a proof in a suitable axiomatic foundation. This sets a very high standard: every rule of inference and every step of a calculation has to be justified by appealing to prior definitions and theorems, all the way down to basic axioms and rules. In fact, most such systems provide fully elaborated “proof objects” that can be communicated to other systems and checked independently. Constructing such proofs typically requires much more input and interaction from users, but it allows us to obtain deeper and more complex proofs. The Lean Theorem Prover aims to bridge the gap between interactive and automated theorem proving, by situating automated tools and methods in a framework that supports user interaction and the construction of fully specified axiomatic proofs. The goal is to support both mathematical reasoning and reasoning about complex systems, and to verify claims in both domains.

This article introduces and explains a computer algebra system (CAS) wxMaxima for Calculus teaching and learning at the tertiary level. The didactic reasoning behind this approach is the need to implement an element of technology into classrooms to enhance students' understanding of Calculus concepts. For many mathematics educators who have been using CAS, this material is of great interest, particularly for secondary teachers and university instructors who plan to introduce an alternative CAS into their classrooms. By highlighting both the strengths and limitations of the software, we hope that it will stimulate further debate not only among mathematics educators and software users but also also among symbolic computation and software developers.

Rule based expert systems provide a modular and uniform approach to representing knowledge, however it has been recognized that rule-based systems become increasingly difficult to understand and maintain as the number of rules grow. Expert systems today are developed on general purpose inference shells that offer general purpose paradigms which do not take into considerations the type of problems being solved. It is up to the users to create the meta level control to prevent rule interference, and for the rules to function properly. This task tends to become increasingly difficult in direct proportion to the size of the accumulated knowledge.The solution is in a new generation of Application Specific Expert System Tools that are designed with specific paradigms and knowledge representation methodology that meet the requirements of a specific domain. This concept is examplified in the work presented here that introduces a generic expert systems shell for diagnostic reasoning. Domain knowledge is represented as five different classes of objects. A paradigm for diagnostic reasoning is built into the inference algorithm to become part of the inference shell, replacing the usual general purpose forward or backward chaining algorithm.

Depending on the specific expert system application, individuals and organizations involved in the expert system development and marketing chain may unwittingly surrender themselves to high-risk exposure to tort liability legal actions, in the event that an innocent third party should suffer physical harm. The degree of risk turns on whether the expert system is viewed by the court as a 'product' or as a 'service'. The author examines the implication of this determination in two areas of tort, product liability and negligence, and then outlines the issues surrounding the 'product-service continuum' as applied specifically to the expert system development and marketing environment. Finally, the author presents proactive measures that expert system developers and vendors should consider in order to minimize exposure to risk.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Computer algebra systems have great potential for professional engineers, scientists and technologists. They can reduce the tedium of routine manipulation required in some mathematical models and procedures, give a greater assurance of accuracy, and even enable users to apply techniques which they might find difficult without such assistance. But learning the basic concepts, particularly of the more sophisticated and capable computer algebra systems, is itself a mathematical activity. For instance, the formal concepts of functions must be grasped very clearly if the user is to get the best from these systems. This paper will explore those theoretical concepts, which may be seen as essential enabling skills for users of computer algebra systems.

Nowadays most of people of developed economy countries interact with software every day. As a result of computer systems expansion to all scope of people's activity the problem of transition from visual and command interfaces to natural language user interfaces is thrown into the sharp relief. Computational linguistics and natural language processing methods are described in this article. Represent a formal definition of a dialogue system as a Markov decision process. Methods of natural language manipulation are applied in machine translation software systems, systems of search and exchange the data, text annotation and expert systems. Prototype of natural language user interface to structured data source is developed. As a result it is convert natural language user's query to SQL query to database. Also showed application of Levenshtein distance for processing natural language query to SQL query to database. Natural language user interface is created to predefined subject field. User interface interacts to database that contain information about existent program libraries and frameworks. Consequently, using natural language processing methods it is possible to develop user natural language user interface providing capability to interact with machine.

Expert systems are now recognized as key elements in the computing systems of the future, and the most significant applications of AI. Expert systems are already being used in such varied fields as medicine, engineering, chemistry and business.