Situation Assessment Research Paper

Chapter 3
Situation Assessment
3.1 Introduction
Situation assessment is the process of evaluating a situation for its suitability to support decision-making. One theory proposes that experienced decision makers base most of their decisions on situation assessments. The decision-makers mostly use experience for their decisions - that is, they select actions that previously have worked well in similar situations. What they do is to extract the most significant characteristics from the situation. Due to the presence or lack of certain essential characteristics, they relate to similar situations and what actions that have worked well in past cases. Shortly, situation assessment is to create relevant relations between objects in the environment.

In order to recognize and classify the situations, the technique must be able to handle uncertainty and have an easy way of modeling the situations.

One extreme, neural nets, has the obvious ability to recognize situations. Neural nets starts with no knowledge at all, and learns from training data. The drawback is that the net has to be adequately trained, and lack of training data is always a big problem.

The other extreme, a forward-chained expert system, has to be modeled by an expert and cannot update its knowledge automatically. The system contains no more than the knowledge of its designer, and if taking the difficulties of storing the knowledge properly into account, not even that.

Somewhere in between these techniques is the Bayesian network technique. This technique can be modeled as an expert system, but also has the ability of updating its beliefs. To make the system easy to use, the nodes in the net are often discrete. An expert can easily enter estimates of the probabilities for one situation leading to another, and by that come up with a “quite good” net. By using the ability to update the net, the performance increases if we have proper training data to use. Bayesian networks also have the ability to investigate hypothesis of the

In the classification of numerical data that measures the temperature of a certain material, a normal practice is to assign a grade to the membership function, that is, very cold, quite hot, and so on. The problem arises when assigning a grade to temperatures such as 19° and 21°C; the difference between the temperatures is not very big, but when classified using hard boundaries, 19°C is treated as cold and 21°C is treated as hot. Trying to model a system using hard boundaries could result in erroneous outputs. Classification can be more precise using fuzzy boundaries. A temperature of 19°C could be classified as cold with a membership value of 0.6 and as hot with a membership value of 0.4. In a similar manner, a temperature of 21°C can be classified as both hot and cold. The importance of fuzzy classification becomes more pronounced when dealing with noisy