[ Main page | About Your System | Knowledge Bases | About Fuzzy Logic | The Importance of Explainability | Commercialising ]
This page is about the knowledge bases I have written for you. They all use the same notation for rules and fuzzy sets, and they all work in the same way. You will be able to read them without much more than a knowledge of English; but to know exactly how they calculate a result, and how much each rule contributes to it, you'll need to work through the About Fuzzy Logic page above.
Before showing you the knowledge bases, I should mention something implied by the paragraph above. As mentioned on the About Your System page, the right-hand panel on the simulation page — the one headed "Pricing Rules" — holds shorthand traces of the rules you have used.
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Linked from here. The knowledge base reacts
to one input variable, days_to_flight. There are
two fuzzy sets for this, many_days and
few_days. There is one rule for each, testing
membership of days_to_flight in it.
Intuitively, the rules do what they say. They select a low price increase if there are many days left before the flight, and a moderate increase if there are few days left. There is a very narrow transition region around 32 days where the price increase is a mixture of both. It's narrow because the input fuzzy sets only just overlap.
days_to_flight, but has an
extra fuzzy set, last_minute. This is bunched up
against the zero-days-left end, and mandates an extra, high,
increase for about six days left. The closer days left gets
to zero, the more this high increase gets mixed in.
I have also changed the many_days and
few_days fuzzy sets so that they
have shallower slopes, and thus overlap more. This
means that the transition from low to moderate
price increase around 32 days is smoother and longer.
What this shows is that we can tailor such transitions
by tweaking our fuzzy sets.
Linked from here. This knowledge base
reacts to a different input variable, class.
This is either "tourist" or "business". There are two
rules, one of which selects a low price increase for tourist class,
and the other of which selects a moderate price increase for
business class.
The class variable is
categorical — a classification into two possibilities —
so doesn't involve fuzzy sets in the rule conditions at all.
Fuzzy sets are used as before for price increases, but get
weighted by either 0 or 1. This means that, in effect, one
of the rules always concludes a price increase weighted by 1, but then
the other one concludes an increase weighted by 0, so its
contribution can be ignored.
Linked from here. This knowledge base combines Days To Flight and Tourist Or Business. There are four rules, which cover all possible combinations of class with days to flight being many or few.
Once again, the rules can be understood intuitively. They
introduce a new operator, and, which has more
or less the same meaning as in English. As the About Fuzzy
Logic references explain, and works by
taking two memberships as input and calculating their minimum.
In A and B, if A's membership is 0, this returns 0.
If A's membership is 1, this returns B's membership.
Since, from the previous section, A is always class = "business" and
class = "tourist", and these are always 0 or 1, A effectively
acts as a gate. It either switches the rule off, or lets B
have full say over the result.
Linked from here. Capacity Utilisation refers to the proportion of a flight's seats that are occupied or sold at any given time. Unlike attributes such as days to flight or class, which passengers can directly choose or control, capacity utilisation is typically a result of airline operations and market demand. Passengers won't have direct control over this attribute, nor is it generally visible to them when booking a flight. Instead, it is more of a derived characteristic that airlines use to gauge how full flights are. They may then adjust pricing or availability accordingly.
The knowledge base assumes that higher capacity utilisation — more seats filled — could trigger higher price increments due to increased demand.
As far as fuzzy logic and programming go, the rules are very simple, and don't introduce any new concepts over those already seen.
Linked from here. This knowledge base integrates various factors such as overall market demand, health concerns, and changes in event popularity to model their combined effect on flight pricing. It reflects a more aggregate level of decision-making where specific event details or individual preferences are less visible, focusing instead on broader market trends and external influences.