Differences Between Machine Learning and Rule Based Systems
                   Machine Vision System
DIFFERENCES BETWEEN MACHINE LEARNING AND RULE BASED SYSTEMS
Machine Vision has gained massive traction in dynamic industries such as manufacturing and production within the past few years. These industries are progressively leveraging machine vision to enhance their customer experience, optimize the usage of resources, and achieve better quality assurance. Qualitas Technologies have been an advocate for this technology ever since our inception.
With rapid innovation and advancements in many different areas, machine vision technology has numerous benefits in store for the manufacturing industry at various levels. New imaging techniques have provided an avenue for newer application opportunities. However, the heart of any machine vision system is the image-processing algorithm. That said, there are various approaches you can use for image processing. Over the years, rule-based systems were the most commonly used approaches. Now with the advent of machine learning, and with the rapid progress in the development of the technology, more and more machine vision service providers are adopting ML for their systems.
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So what exactly are the differences?
Rules-based systems are a simple kind of artificial intelligence, which uses a series of simple IF-THEN statements that guide a computer to reach a conclusion or recommendation based on certain rules or logic. A rules-based system is built on two main components: a set of facts about a situation, and a set of rules for how to deal with those facts:
A set of facts. Also known as the knowledge base.  These facts are a combination of data, such as income and a condition such as ‘is zero’, or ‘is greater than £10k’.
A set of rules. Also known as the rules engine. It is the rules that describe the relationship between the IF and the THEN statements.
With these two basic concepts, it is possible to build a basic AI system such as a tool to recommend clothing choices on a particular day. Let us look at an example better suited for the manufacturing industry. Consider a machine vision system that needs to identify a rubber wheel variant. The first most logical rule to specify is the colour of the rim. We can clearly see the difference in the colors of the rims in the following image and can instruct the algorithm to classify based on the same.
One of the key benefits of a rules system is that writing and implementing rules is quite easy. If we know about the situation of interest, we can easily create rules based on simple IF-THEN statements to represent relationships and classifications. However, we need to account for special cases. What if there are two different types of wheels with the same colored rims? Then we need to define a rule to count and classify based on the number of holes.
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But what if we come across a type of wheel that does not have holes? This list could go on. Rules-based systems are deterministic in nature. Not having the right rule in place can result in false positives and false negatives. Therefore, a system of rules can start quite simple but can become rather unwieldy over time as more and more exceptions and rule changes are added. While individual rules might be easy to understand and represent, the complex interactions of a full rules engine may be more difficult to grasp. Â
MACHINE LEARNING APPROACH
Machine learning is an alternative approach that can be used in machine vision that can help to address some of the shortcomings with rules-based methods. Rather than attempting to fully emulate the decision-making process like a human, machine learning methods typically only take the outcomes and tweak themselves to reach those outcomes. Machine learning is probabilistic in nature and uses statistical models rather than deterministic rules. The basic operation of a machine learning process is to say that based on the historic outcomes we can predict future outcomes.
Machine learning approaches assume that outputs for any problem can be described by a combination of input variables and other parameters. The machine learning algorithm itself is often regarded as a ‘black box‘ – the inputs and outputs are closely connected to the real world, but the internal works are more difficult to describe.
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In the machine learning approach for our aforementioned example, we first need to train our models on examples of each variant. Through these examples, and by predicting what each of them could be, the system learns to differentiate between them. The system works similarly to how a human would differentiate objects based on patterns.
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