Scalable Software Architectures for Decision Support

1999 ◽  
Vol 38 (04/05) ◽  
pp. 229-238 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Decision Support Systems ◽  
Medical Informatics ◽  
Intelligent Systems ◽  
Clinical Medicine ◽  
Support Systems ◽  
Basic Research ◽  
Building Blocks ◽  
Domain Ontologies

AbstractInterest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of “generic tasks” and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods – domain-independent algorithms for automating stereotypical tasks – and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

Fundamentals of creating MIVAR expert systems

2021 ◽  
Author(s):  
Oleg Varlamov
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Decision Support Systems ◽  
Intelligent Systems ◽  
Autonomous Robots ◽  
Support Systems ◽  
Knowledge Bases ◽  
Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

Intelligent Agents in Decision Support Systems

2011 ◽  
pp. 857-866 ◽  
Author(s):  
Gloria E Phillips-Wren
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Information Overload ◽  
Support Systems ◽  
Distributed Decision Making ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems

Internet-based, distributed systems have become essential in modern organizations. When combined with artificial intelligence (AI) techniques such as intelligent agents, such systems can become powerful aids to decision makers. These newer intelligent systems have extended the scope of traditional decision support systems (DSSs) to assist users with real-time decision making, multiple information flows, dynamic data, information overload, time-pressured decisions, inaccurate data, difficult-to-access data, distributed decision making, and highly uncertain decision environments. As a class, they are called intelligent decision support systems (IDSSs).

On the Heuristic Nature of Medical Decision-Support Systems

1995 ◽  
Vol 34 (01/02) ◽  
pp. 5-14 ◽  
Author(s):  
C. F. Aliferis ◽  
R. A. Miller
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Decision Support Systems ◽  
Conceptual Development ◽  
Ad Hoc ◽  
Support Systems ◽  
System Development ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Abstract:In the realm of medical decision-support systems, the term “heuristic systems” is often considered to be synonymous with “medical artificial intelligence systems” or with “systems employing informal model(s) of problem solving”. Such a view may be inaccurate and possibly impede the conceptual development of future systems. This article examines the nature of heuristics and the levels at which heuristic solutions are introduced during system design and implementation. The authors discuss why heuristics are ubiquitous in all medical decision-support systems operating at non-trivial domains, and propose a unifying definition of heuristics that encompasses formal and ad hoc systems. System developers should be aware of the heuristic nature of all problem solving done in complex real world domains, and characterize their own use of heuristics in describing system development and implementation.

Soft Computing Paradigms and Regression Trees in Decision Support Systems

2011 ◽  
pp. 1011-1035 ◽  
Author(s):  
Cong Tran ◽  
Ajith Abraham ◽  
Lakhmi Jain
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Soft Computing ◽  
Intelligent Systems ◽  
Support Systems ◽  
Regression Trees ◽  
Gradient Algorithm ◽  
Neural Network Learning ◽  
Intelligent Decision Support Systems

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of soft computing (SC) (Zadeh, 1998) technologies that underlie the conception, design, and utilization of intelligent systems. In this chapter, we present different SC paradigms involving an artificial neural network (Zurada, 1992) trained by using the scaled conjugate gradient algorithm (Moller, 1993), two different fuzzy inference methods (Abraham, 2001) optimised by using neural network learning/evolutionary algorithms (Fogel, 1999), and regression trees (Breiman, Friedman, Olshen, & Stone, 1984) for developing intelligent decision support systems (Tran, Abraham, & Jain, 2004). We demonstrate the efficiency of the different algorithms by developing a decision support system for a tactical air combat environment (TACE) (Tran & Zahid, 2000). Some empirical comparisons between the different algorithms are also provided.

Intelligent Agents in Decision Support Systems

2011 ◽  
pp. 505-513 ◽  
Author(s):  
Gloria E. Phillips-Wren
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Information Overload ◽  
Support Systems ◽  
Distributed Decision Making ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems

Internet-based, distributed systems have become essential in modern organizations. When combined with artificial intelligence (AI) techniques such as intelligent agents, such systems can become powerful aids to decision makers. These newer intelligent systems have extended the scope of traditional decision support systems (DSSs) to assist users with real-time decision making, multiple information flows, dynamic data, information overload, time-pressured decisions, inaccurate data, difficult-to-access data, distributed decision making, and highly uncertain decision environments. As a class, they are called intelligent decision support systems (IDSSs).

Soft Computing Paradigms and Regression Trees in Decision Support Systems

2006 ◽  
pp. 1-28 ◽  
Author(s):  
Cong Tran ◽  
Ajith Abraham ◽  
Lakhmi Jain
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Soft Computing ◽  
Intelligent Systems ◽  
Support Systems ◽  
Regression Trees ◽  
Gradient Algorithm ◽  
Neural Network Learning ◽  
Intelligent Decision Support Systems

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of soft computing (SC) (Zadeh, 1998) technologies that underlie the conception, design, and utilization of intelligent systems. In this chapter, we present different SC paradigms involving an artificial neural network (Zurada, 1992) trained by using the scaled conjugate gradient algorithm (Moller, 1993), two different fuzzy inference methods (Abraham, 2001) optimised by using neural network learning/evolutionary algorithms (Fogel, 1999), and regression trees (Breiman, Friedman, Olshen, & Stone, 1984) for developing intelligent decision support systems (Tran, Abraham, & Jain, 2004). We demonstrate the efficiency of the different algorithms by developing a decision support system for a tactical air combat environment (TACE) (Tran & Zahid, 2000). Some empirical comparisons between the different algorithms are also provided.

Sign in / Sign up

Export Citation Format

Share Document