Selahattin Kuru

Qualitative system identification: deriving structure from behavior

Abstract Qualitative reasoning programs (which perform simulation, comparative analysis, data interpretation, etc.) either take the model of the physical system to be considered as input, or compose it using a library of model fragments and input information about how to combine them. System identification is the task of creating models of systems, using data about their behaviors. We present the qualitative system identification algorithm QSI, which takes as input a set of qualitative behaviors of a physical system, and produces as output a constraint model of the system. QSIs output is guaranteed to produce its input when simulated. Furthermore, the QSI-made models usually contain meaningful “deep” parameters of the system which do not appear in the input behaviors. Various aspects of QSI and its applicability to diagnosis, as well as the model fragment formulation problem, are discussed.

Goal-driven blackboard control architecture based on extending partially complete general goal trees

A control architecture for goal-driven blackboard systems is introduced. The basic elements of the architecture are goals, policies, strategies, methods and knowledge sources. The basic control loop employs a bidding mechanism to determine the knowledge source to be executed at the current cycle. The architecture employs separate control and domain blackboards, and separate knowledge sources for the control problem and for representing the domain knowledge. The major characteristics of the architecture are that it has a simple and uniform structure, and that its basic control loop is based on a formal basis, namely, extending a partially complete general goal tree. The architecture is implemented in Smalltalk and tested on a multiple-task planning problem.

Long term planning of a major national sector using a linear programming model

Abstract This article discusses a linear programming model for the long term planning of the Turkish iron and steel industry. Iron and steel industry is a complicated industry. It is rich in terms of products and production technologies, and has a large transportation problem associated with it. The model is formulated as a cost minimization problem and is solved in terms of several scenarios, each representing a different strategy for capacity expansion, using a mathematical programming package.

Blackboard Based Coordination in Cooperative Problem Solving

This article introduces a control architecture for blackboard based coordination in cooperative problem solving. The basic elements of the architecture are goals, policies, strategies, methods, and knowledge sources. The basic control loop employs a bidding mechanism to determine the knowledge source to be executed at the current cycle. The architecture employs separate control and domain blackboards, and separate knowledge sources for the control problem and for representing the domain knowledge. It has a simple and uniform structure, and it is based on a formal basis, namely, extending a partially complete general goal tree. The architecture is implemented in Smalltalk and tested on a multiple-task planning problem.

Automatic Construction of Qualitative Models

The needs for the use of deep domain models and an ability to handle incompletely specified information are evident in most of today’s expert systems. Much AI research in the area of qualitative reasoning (Weld and de Kleer 1990) has addressed this problem. Qualitative models of (usually very simple) physical systems have been formulated and various temporal and spatial reasoning programs working on these models have been developed.

Sign analysis technique for predicting system behavior

Sign analysis is a technique for deriving the behavior of a system when it is subjected to a disturbance. the technique is for systems whose mathematical models are of the form of algebraic equations. the technique is based on generating sign combinations of total differentials of system parameters in table form from closed form algebraic functions which model the system. Sign combinations are then analyzed to predict system behavior with respect to a disturbance from the equilibrium state. the technique may be applied to total differentials of gains as well.

The TSE 310 expert prototype for the Airbus A310 commercial aircraft

Using available Troubleshooting Manuals, Mean Time Between Failure data, and the Maintenance Record of each aircraft the troubleshooting process of an Airbus A-310 is being automated by implementing an expert system (TSE_310). Starting off with the basic troubleshooting tree in the Troubleshooting Manual the program uses Last Removal Date and Mean Time Between Failure data to calculate failure probabilities of the parts connected to the relevant node of the troubleshooting tree, and by using these probabilities traverses the rest of the tree in an intelligent manner to locate the faulty part requiring minimum user intervention.