Common Sense Reasoning in Diagnostic Systems - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 111

 ATMS premises - an observed device behaviour;

 ATMS assumptions - components of a device;

 inferred ATMS nodes - predictions of an diagnostic system;

 Nogood - the difference between predicted and observed device behaviour.

7. The current measurement point determination

One of the key aspects of the model-based fault search algorithm is to determine the optimal
current measurement in a diagnosed device [de Kleer, 1987]. Efficiency of the current
measurement choosing allows essentially reducing a decision search space while the
inefficiency of choice will increase an operating time, the space of a searching algorithm, and
also require additional resource spends to implement a measurement.
The best measurement point in a diagnosed device is a place (point) of measuring a value
giving the largest information promoting the detection of a set of fault components at
minimal resource spending.
One of the best procedures for reducing resource expenses is to produce the measuring
giving the maximal information concerning predictions made on the basis of the current
information on a system.

7.1 Heuristic methods of choosing a measurement point
The purpose of the best choosing a measurement point is to derive the maximal component
state information. After each measuring there is a confirmation or refutation of prediction
values in a point of measurement. So, it is possible to use the following aspects [Vagin et al.,
2006a,b,c]:
 knowledge about environments that support predicted values in the measurement

points which can be confirmed or refuted;

 knowledge about inconsistent environments;

 knowledge about coincided assumptions of the inconsistent environments.

7.2 Knowledge about supporting environments
The diagnostic procedure constructs predictions of values for each device point with the list
of environments in which the given prediction is held. The list of environments represents
assumption sets about correctness of corresponding device components.
Let's consider a sample of the 3-bit parity checker (Fig. 9) [Frohlich, 1998].
The vector (1, 0, 1) is input to device components (Inv1, Inv2, Inv3). Let the
following measurements be obtained: D=0, R4=0, R2=1. The mismatch between observations
and predictions speaks about a fault in a device. Based on measured observations additional
predictions of values are formed. In general, it is obtained the following set of predictions
with appropriate environments:
<R3=0, {{And3,Inv1}, {And2,And3,Inv6}, {And2,And3,Inv5}}>;
<R1=0, {{And1,Inv3}, {And1,And2,Inv4}, {And1,And2,Inv5}}>;
<O6=0, {{And2,Inv6}}>;
<O6=1, {{Inv3,Inv6}}>;
<O5=1, {{And2}}>;
<O5=0,{{Inv2,Inv5}, {And4,Inv1,Inv3,Inv4,Inv6}}>;
<O4=0, {{And2,Inv4}}>;