Building an Expert Decision Support System: The Integration of Artificial Intelligence and Operations Research Methods

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.

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

The incorporation of global program analysis into recent compilers for Constraint Logic Programming (CLP) languages has greatly improved the efficiency of compiled programs. We present a global analyser based on abstract interpretation. Unlike traditional optimizers, whose designs tend to be ad hoc, the analyser has been designed with flexibility in mind. The analyser is incremental, allowing substantial program transformations by a compiler without requiring redundant re-computation of analysis data. The analyser is also generic in that it can perform a large number of different program analyses. Furthermore, the analyser has an object-oriented design, enabling it to be adapted to different applications easily and allowing it to be used with various CLP languages with simple modifications. As an example of this generality, we sketch the use of the analyser in two different applications involving two distinct CLP languages: an optimizing compiler for CLP(R) programs and an application for detecting occur-check problems in Prolog programs. © 1998 John Wiley & Sons Ltd.

AMTexpert is a prototype of an expert decision support system (EDSS) for the corporate alternative minimum tax (AMD. The corporate AMT was introduced as part of the Tax Reform Act of 1986 in order to force US corporations to pay more tax. The complicated and interrelated nature of the AMT system has made compliance and planning even more difficult for corporate taxpayers. Given the complexities of the corporate AMT, significant benefits may accrue from the development of an EDSS. In effect, an EDSS combines the judgmental benefits of an expert system (ES) with the computational benefits of a decision support system (DSS). AMTexpert is menu driven with six interdependent phases. The heart of the system is a ‘scoreboard’ used to compare and evaluate alternative tax positions. Context‐sensitive advice and explanations are provided to guide the user. The system was validated and evaluated by two groups of outside experts. AMTexpert demonstrates that an EDSS may be developed from existing tax and systems software.

An entity-relationship approach to decision support and expert systems

Beyond decision support systems: The role of operations research in expert systems

Many misunderstandings exist concerning management information systems and artificial intelligence. It is a misunderstanding, for instance, that ‘attaining competitive edge’, ‘strategic information’ and ‘executive information systems’ will lead to sustainable competitiveness. It is, furthermore, a misunderstanding that negative experiences with management information systems (MIS), decision support systems (DSS) and Expert Systems (ES) could be ignored. The key issue for achieving competitiveness is to improve the performance of information-workers in organizations via the exploitation of information technology. Information Management (IM) has to provide ways for task-improvement at the job-site of an individual or a group, using their knowledge and experience. It has to present better ways of task-coordination, meeting the demands of flexible organizations and at the same time flexible information systems. It has to realize information infrastructures between and above organizations. Management is facing a challenging design activity, requiring a lot of intelligence and MIS-understanding

Methods of building a multidisciplinary expert decision support system in the field of construction have been developed. The architectural solution underlying the study is based on the theory of fuzzy sets. Software approaches to the design of specialized expert systems are considered, as well as the tasks solved by the decision support system at the stage of preparing an object for construction. The purpose of the study is to develop the architecture of an expert system consisting of logical inference systems and a set of interconnected fuzzy expert modules of the knowledge base. The objective of the research is to develop a generalized algorithm for the functioning of the expert system, the architecture of the knowledge base and a prototype of the software implementation of the expert system. As a result of the research, two software products have been developed in Python and a prototype in Matlab. Examples of interfaces and program code of the developed expert decision support system in the field of construction are given.

Constraint logic programming (CLP) has demonstrated its ability to handle difficult problems coming from operation research. We think that this approach is relevant to perform program analyses too. However, program analysis often requires solvers for second order constraints, i.e. mainly fixpoint equations, that are not available in CLP languages. In this paper, we present, the language Toupie that is designed to solve such constraints. Toupie uses an extension of Bryant's binary decision diagrams to encode relations which makes it possible to handle relations with huge numbers of elements.

Simple SummaryThis paper discusses mechanisms to promote the sustained adoption of integrated decision support systems (IDSS) in dairy farming. Integrated DSS are those that receive continuous data streams from on-farm and off-farm data collection systems and integrate them to produce insightful and effective outcomes. Multiple limiting factors are preventing the use and adoption of existing IDSS and could also prevent the adoption of upcoming IDSS. In summary, these factors include value proposition, practical application, ease of use, and data governance issues related to the IDSS. Therefore, awareness and further industry-wide discussion of these components could help in the development and sustained adoption of IDSS in order to facilitate the complex process of decision making. As a commentary paper, the emphasis is on the authors’ extensive experience, expertise, and practical knowledge.Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.

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.

We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We define a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning.

Value-driven expert systems for decision support

Expert systems in management and administration - are they really different from Decision Support Systems?

CAL: A theoretical background of constraint logic programming and its applications