[ Main page | About Your System | About Fuzzy Logic | The Importance of Explainability | Commercialising ]
This is the main page for the Bring Me Gas expert system and the simulation environment that demonstrates it. The expert system "lives" in a simple simulated environment where there's a company that supplies gas bottles to customers. The company uses an expert system to help it plan and schedule deliveries, and to adapt these to changes in the customers' circumstances. The expert system is based around IF-THEN rules which recommend, for example, how the date for the next delivery should depend on the weather. These rules use fuzzy logic. This means that they can handle everyday vagueness, using conditions such as "if the weather is very bad", "if the customer lost a lot of their stocks", or "if it is a long time since the last delivery". Fuzzy logic excels at handling words such as "very", "many", "a little", "long", and others that do not have precise boundaries.
The expert system is not a commercial product, but a Minimum Viable Prototype — an MVP. However, the techniques I used for it, namely fuzzy logic and the software that implements and interprets it, can be used in a full product. For this reason, I have taken some considerable care in demonstrating them, by making the simulation environment clear and easy to understand, and by showing how the rules operate within it. For instructions on how to run it and how to understand the rules, please go to About Your System.
Fuzzy logic rules can be understood at an intuitive level by reading them as though they were English. This indeed is a key selling point, meaning that while coding, you can easily share knowledge with people who are domain experts but not expert-system experts. However, there is a rigorous mathematics behind fuzzy logic, dating back to its invention in the 1960s. For more on this, please go to About Fuzzy Logic. This links to some non-technical background info for non-experts, a tutorial on fuzzy logic from two computer scientists who use mapping people's heights to shoe sizes as an example, an online demonstration of this written by me, and another tutorial also written by me. There is also a link to a LinkedIn tutorial on fuzzy logic for inventory optimisation, which I shall repeat here. By Bipin Reghunathan, it's an easy read, and includes a list of rules for optimising from demand variability and other attributes.
As mentioned above, ease of communication with colleagues is an important benefit of fuzzy logic. More generally, programs that use fuzzy logic — and other kinds of logic — are much better at justifying their advice than are other techniques such as neural networks and numerical optimisation. I regard this as very important, and as worth emphasising when you demonstrate to potential investors. Other AI experts back me up, most notably Donald Michie, "the Father of British AI". For his views, please go to The Importance of Explainability. You might want to keep this available to show to others.
Finally, you will want to know what steps to take in order to move from this MVP to a full product. I explain this at Commercialising.