USING META-PROGRAMMING, CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

In recent years, several constraintdbased temporal reasoning frameworks have been proposed. They consider temporal points or intervals as domain elements linked by temporal constraints. Temporal reasoning in these systems is based on constraint propagation. In this paper, we argue that a language based on constraint propagation can be a suitable tool for expressing and reasoning about temporal problems. We concentrate on Constraint Logic Programming (CLP) which is a powerful programming paradigm combining the advantages of Logic Programming and the efficiency of constraint solving. However, CLP presents some limitations in dealing with temporal reasoning. First, it uses an “arc consistency” propagation algorithm which is embedded in the inference engine, cannot be changed by the user, and is too weak in many temporal frameworks. Second, CLP is not able to deal with qualitative temporal constraints. We present a general meta CLP architecture which maintains the advantages of CLP, but overcomes these two main limitations. Each architectural level is a finite domain constraint solver (CLP(FD)) that reasons about constraints of the underlying level. Based on this conceptual architecture, we extend the CLP(FD) language and we specialize the extension proposed on Vilain and Kautz’s Point Algebra, on Allen’s Interval Algebra and on the STP framework by Dechter, Meiri and Pearl. In particular, we show that we can cope effectively with disjunctive constraints even in an intervaldbased framework.

The concept of Expert Decision Support Systems is based on the integration of Decision Support Systems and Expert Systems, providing the decision maker with features from both types of systems (e.g. simulation, explanations, control of reasoning). Constraint logic programming languages, such as CLP(R) or CHIP, achieve this integration by merging logic programming, a paradigm of Artificial Intelligence and Expert Systems, with constraint solving techniques similar to those used in Operations Research and Decision Support Systems. Their embedded constraint solver extends the expressive power of logic programming languages, most notably on the domain of numerical expressions. The paper illustrates how constraint logic programming may be used in EDSS development by presenting a small classroom scheduling problem developed with CLP(R), and compares this approach to more traditional Operations Research techniques.

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.

Fuzzy logic (FL) is a mathematical technique for dealing with imprecise data and problems that have many solutions rather than one. Although it is implemented in digital computers which ultimately make only yesno decisions, FL works with ranges of values, solving problems in a way that more resembles human logic. FL is a multi-valued (as opposed to binary) logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 and 1, black and white, yes or no; in terms of Boolean algebra, everything is in one set or another but not in both. FL allows for partial membership in asset values between 0 and 1, shades of gray, and introduces the concept of the “fuzzy set.” When the approximate reasoning of FL (Zadeh, 1965) is used with an expert system, logical inferences can be drawn from imprecise relationships. FL theory was developed by Lofti A. Zadeh at the University of California in the mid 1960s. However, it was not applied commercially until 1987 when the Matsushita Industrial Electric Co. used it to automatically optimize the wash cycle of a washing machine by sensing the load size, fabric mix, and quantity of detergent and has applications in the control of passenger elevators, household applications, and so forth.

Constraint Logic Programming language embodying extensional finite Fuzzy Sets is presented. Basic fuzzy set operations (=, ∈, ≠, and ∉) are defined as constraints over fuzzy sets and their elements. Simple list-like representation of sets is presented, with fset/2 as the interpreted set constructor and {} as the empty set. Members of the fuzzy sets are fuzzy elements (terms with mu/2 as the main functor), with explicit membership value associated with each individual member. The language is implemented using the concept of attributed variables; brief discussion of the usability of attributed variables for this kind of meta-programming is also given.KeywordsLogic ProgramLogic ProgrammingConstraint LogicGround TermConstraint Logic ProgrammingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Constraint Logic Programming (CLP) languages extend logic programming by allowing the use of constraints from different domains such as real numbers or Boolean functions. They have proved to be ideal for expressing problems that require interactive mathematical modeling and complex combinatorial optimization problems. However, CLP languages have mainly been considered as research systems, useful for rapid prototyping, by not really competitive with more conventional programming languages where efficiency is a more important consideration. One promising approach to improving the performance of CLP systems is the use of powerful program optimizations to reduce the cost of constraint solving. We extend work in this area by describing a new optimizing compiler for the CLP language CLP(ℛ). The compiler implements six powerful optimizations: reordering of constraints, removal of redundant variables, and specialization of constraints which cannot fail. Each program optimization is designed to remove the overhead of constraint solving when possible and keep the number of constraints in the store as small as possible. We systematically evaluate the effectiveness of each optimization in isolation and in combination. Our empirical evaluation of the compiler verifies that optimizing compilation can be made efficient enough to allow compilation of real-world programs and that it is worth performing such compilation because it gives significant time and space performance improvements.

In object-oriented representation, inheritance determines the attributes of a given object, given its type. Conversely, recognition determines the type of an object, given its attributes. The author addresses the more general problem of recognition from constraints; arguing that it can be seen as a special case of the classical abduction process and that it can be implemented as an abduction operation on constraints. Abductive reasoning is common in most fields of artificial intelligence. Constraint logic programming (CLP) generalizes logic programming by replacing unification with constraint solving. He first explores the relationship between abduction and CLP and shows that CLP is a special case of abduction. A class of problems is then identified for which the usual unrestricted forms of abduction are unsuitable. Within the CLP framework a restricted form of abduction is formulated which is applicable to the whole class of problems, in which object-oriented recognition can be considered as a special case. >

A fundamental shift in the preferred approach to building applied artificial intelligence (AI) systems has taken place since the late 1960s. Previous work focused on the construction of general-purpose intelligent systems; the emphasis was on powerful inference methods that could function efficiently even when the available domain-specific knowledge was relatively meager. Today the emphasis is on the role of specific and detailed knowledge, rather than on reasoning methods. The first successful application of this method, which goes by the name of knowledge-based or expert-system research, was the DENDRAL program at Stanford, a long-term collaboration between chemists and computer scientists for automating the determination of molecular structure from empirical formulas and mass spectral data. The key idea is that knowledge is power, for experts, be they human or machine, are often those who know more facts and heuristics about a domain than lesser problem solvers. The task of building an expert system, therefore, is predominantly one of “teaching” a system enough of these facts and heuristics to enable it to perform competently in a particular problem-solving context. Such a collection of facts and heuristics is commonly called a knowledge base. Knowledge-based systems are still dependent on inference methods that perform reasoning on the knowledge base, but experience has shown that simple inference methods like generate and test, backward-chaining, and forward-chaining are very effective in a wide variety of problem domains when they are coupled with powerful knowledge bases. If this methodology remains preeminent, then the task of constructing knowledge bases becomes the rate-limiting factor in expert-system development. Indeed, a major portion of the applied AI research in the last decade has been directed at developing techniques and tools for knowledge representation. We are now in the third generation of such efforts. The first generation was marked by the development of enhanced AI languages like Interlisp and PROLOG. The second generation saw the development of knowledge representation tools at AI research institutions; Stanford, for instance, produced EMYCIN, The Unit System, and MRS. The third generation is now producing fully supported commercial tools like KEE and S.1. Each generation has seen a substantial decrease in the amount of time needed to build significant expert systems. Ten years ago prototype systems commonly took on the order of two years to show proof of concept; today such systems are routinely built in a few months. Three basic methodologies—frames, rules, and logic—have emerged to support the complex task of storing human knowledge in an expert system. Each of the articles in this Special Section describes and illustrates one of these methodologies. “The Role of Frame-Based Representation in Reasoning,” by Richard Fikes and Tom Kehler, describes an object-centered view of knowledge representation, whereby all knowldge is partitioned into discrete structures (frames) having individual properties (slots). Frames can be used to represent broad concepts, classes of objects, or individual instances or components of objects. They are joined together in an inheritance hierarchy that provides for the transmission of common properties among the frames without multiple specification of those properties. The authors use the KEE knowledge representation and manipulation tool to illustrate the characteristics of frame-based representation for a variety of domain examples. They also show how frame-based systems can be used to incorporate a range of inference methods common to both logic and rule-based systems. "Rule-Based Systems,” by Frederick Hayes-Roth, chronicles the history and describes the implementation of production rules as a framework for knowledge representation. In essence, production rules use IF conditions THEN conclusions and IF conditions THEN actions structures to construct a knowledge base. The autor catalogs a wide range of applications for which this methodology has proved natural and (at least partially) successful for replicating intelligent behavior. The article also surveys some already-available computational tools for facilitating the construction of rule-based knowledge bases and discusses the inference methods (particularly backward- and forward-chaining) that are provided as part of these tools. The article concludes with a consideration of the future improvement and expansion of such tools. The third article, “Logic Programming, ” by Michael Genesereth and Matthew Ginsberg, provides a tutorial introduction to the formal method of programming by description in the predicate calculus. Unlike traditional programming, which emphasizes how computations are to be performed, logic programming focuses on the what of objects and their behavior. The article illustrates the ease with which incremental additions can be made to a logic-oriented knowledge base, as well as the automatic facilities for inference (through theorem proving) and explanation that result from such formal descriptions. A practical example of diagnosis of digital device malfunctions is used to show how significantand complex problems can be represented in the formalism. A note to the reader who may infer that the AI community is being split into competing camps by these three methodologies: Although each provides advantages in certain specific domains (logic where the domain can be readily axiomatized and where complete causal models are available, rules where most of the knowledge can be conveniently expressed as experiential heuristics, and frames where complex structural descriptions are necessary to adequately describe the domain), the current view is one of synthesis rather than exclusivity. Both logic and rule-based systems commonly incorporate frame-like structures to facilitate the representation of large amounts of factual information, and frame-based systems like KEE allow both production rules and predicate calculus statements to be stored within and activated from frames to do inference. The next generation of knowledge representation tools may even help users to select appropriate methodologies for each particular class of knowledge, and then automatically integrate the various methodologies so selected into a consistent framework for knowledge.

Most universities in Belgium do research in AI and offer AI courses to students. The largest part of the research is into the more classical AI subjects, such as logic programming and theorem proving. However, sub-symbolic AI is also investigated. It seems that all Belgian Universities have their own distinct flavour of research. Below, we present the different Belgian universities in alphabetical order. The Facultes Universitaires Notre Dame de la Paix Namur (University of Namur) has a group called ‘semantics and computational logic’ led by B. Le Charlier and P. Schobbens. This group is theoretically oriented and although their interests are broader than just AI, some of their research is into computational logic, automated theorem proving and formalisation of agentbased systems. The Katholieke Universiteit Leuven (Flemish catholic university of Leuven) has different groups working on artificial-intelligence subjects. First of all, there is the group on declarative languages and artificial intelligence led by M. Bruynooghe. The artificialintelligence interest of this group is into knowledge representation and machine learning, with research themes such as data mining, constraint logic programming, reasoning with time, natural-language processing and learning in agents. They are also involved in the Robocup simulation league. Second, there is a group doing research into intelligent mobile robots led by H. Van Brussel and J. De Schutter. This group is part of the mechanical engineering department. Finally, there is a group at the economy department, led by J. Vanthienen and M. Verhelst, that investigates knowledge systems and modelling. At the Limburgs Universitair Centrum (Limburg University Centre) M. Gyssens of the theoretical computer science group works on the structure and complexity of constraint satisfaction problems. J. Van den Bussche of the same group works on implementation of query languages for data mining. The Universite Catholique de Louvain (French catholic university of Louvain) has several small groups investigating different AI subjects. Computational linguistics is investigated by E. Milgrom and S. Granger. Logic programming is investigated by Y. Deville. A. Thayse investigates natural-language processing, P.J. Courtois investigates self-organising systems, P. Van Hentenryck works on optimisation and constraint satisfaction and Y. Kamp investigates collective intelligence. The Universite de Liege (Montefiore) (University of Liege) has D. Gribomont working on verification methods, automatic theorem proving and formal logic. The Universitaire Instelling Antwerpen (University of Antwerp) has the centre for Dutch language and speech, led by W. Daelemans. Part of the research of this institute is purely linguistic, but a great deal of research is also performed into (memory-based) machine learning of natural language, using large corpora of text, as well as into intelligent processing of text. The Universit Libre de Bruxelles (French Free University of Brussels) has the IRIDIA (Institut de Recherches Interdisciplinaires et de Developpements en Intelligence Artificielle) group led by M. Dorigo and H. Bersini. The main research interests are: ‘belief representation and AI-techniques for process control and classification’ and ‘nature-inspired heuristics for the solution of combinatorial and continuous space optimisation problems’. Sub-symbolic techniques are

Uncertainty about future developments constitutes the most important and most difficult challenge for mankind. Despite this fact, uncertainty is not a part of general science. General science assumes that the future development follows cause-effect relations which can be described by mathematical functions, where the argument represents the cause and the image represents the effect. Scientific theories have exactly this form and it is widely believed that these functions represent “truth”. Of course, this is nonsense as all the scientific theories are with certainty wrong and cannot describe the real evolution correctly. The inappropriate handling of uncertainty in science has produced a strange variety of “uncertainty theories” that causes confusion and helplessness. A friend of mine has expressed his confusion by the following words: ‘Crisp sets’, ‘fuzzy sets’, ‘rough sets’, ‘grey sets’, ‘fuzzy rough sets’, ‘rough fuzzy sets’, ‘fuzzy grey sets’, ‘grey fuzzy sets’, ‘rough grey sets’, ‘grey rough sets’, and now ‘affinity sets’. My goodness! Is there anybody around who can enlighten me, i.e., help me to see a clear pattern in this set of sets, allegedly providing powerful tools to model various kinds of uncertainty? This paper examines the role and the handling of uncertainty in quality control. How is uncertainty quantified in quality control for making decisions aiming at maintaining or improving quality of processes and products.KeywordsRandomnessIgnoranceKnowledgeFuzzynessQuantificationProbabilityCredibilityProbability spaceUncertainty spaceBernoulli space

So far, many knowledge representation methods have been discussed. Each method has its own advantages and disadvantages, so it is not possible to introduce a specific method as the best knowledge representation technique. We introduce a unique knowledge base, E/sub x/-OAV KB. As the verification and validation (V&V) of knowledge in any AI systems is important, we propose a new user friendly tool, TKT-OAV, to transform the knowledge representation method of any expert system such as semantic network or frame to the E/sub x/-OAV KB. By using this tool, we could also draw and edit the semantic network or frame and construct the knowledge base. This tool uses the standard algorithms that have been designed by C. Dadkhah and A. Ahmad (2004) for transforming the knowledges. The transformed knowledges are shown in three forms: OAV triple, OAV hierarchy and OAV table. Finally the knowledge base which its knowledges are represented in OAV triple is created. Any expert system shell can use this KB for reasoning and the verification and validation tools like OAV/spl I.bar/VVT expert, could also verify and validate this knowledge base.

Dutch research on knowledge-based instructional systems: Introduction to the special issue

Constraint logic programming framework for integrated decision supports

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

Fuzzy Logic (Fuzzy Logic) is a branch of science based on thinking like human beings and solving them with mathematical functions. Fuzzy logic theory is a mathematical theory. Based on fuzzy set theory, it also uses intermediate values. The fuzzy logic that emerged in 1965 is used in many fields. In the production of pacemakers, in the production of artificial organs, in many electronic devices, company efficiency estimation, etc. situations are used. Fuzzy logic, which is frequently used in the solution of problems that occur in uncertain situations such as quality assessment in recent years, is one of the artificial intelligence methods. With the help of machines, people-specific data and experiences are studied using the fuzzy logic approach. In this study, by using Matlab Fuzzy Toolbox, it was aimed to design a system that gives information about the breeding performances of cows. The expert system was designed based on the optimal values under the ideal conditions specified in the literature. The architecture of the system presented in this paper is designed as three input parameters and one output. The designed system was tested with 100 sample values. Afterwards, expert results were evaluated and system decisions were compared. The success of the decision support system was 94%. As a result, the reproductive efficiency of cows can be determined with this designed system. With this determination, the handling or disposal of cows can be determined.