Minds and Machines
Fuzzy logic in it's simplest terms expands the dicotomy of true or not true to include a range of answers in between. The usual example is say instead of being black or white, fuzziness allows for shades of gray. Since fuzzy logic allows this extra bandwidth in fuzzy answers, fuzzy rules used in programming can cover a much broader area. A fuzzy rule such as "When it rains, you get wet"*** can cover a lot of ground. It would be able to several instantions of itself such as "when it rains a lot, you get wet a lot" or "when it rains a little, you get wet a little".
Rules like this are beautiful because they are human rules. They are a much better model of how we think. It is not often that questions that arise in life have bivalent answers. There are a few that do such as "Are you married?". Other questions such as "Do you like your job?", would tend to elicit a range of a response falling somewhere between yes and no.
How exactly is a fuzzy rule able to cover so much ground? By the use of a patches. A fuzzy rule will define a fuzzy patch. Say for example that you would like to use fuzzy logic to control an air conditioner (Kosco's example). You could define a fuzzy set for the temparature range as COLD, COOL, JUST RIGHT, WARM and HOT. A system could be composed of a few sloppy rules with wide patches, or many precise rules with narrow patches. Perhaps the air conditioner system is representative of other real systems. That is, an optimal solution involves some wide sloppy rules, and some precise ones.
The fuzzy rules that would go with the air conditioner system would be:
Rule 1: If the temperature is cold, them motor speed stops.
Rule 2: If the temperature is cool, the motor speed slows.
Rule 3: If the temperature is just right, the motor speed is medium.
Rule 4: If the tempertaure is warm, the motor speed is fast.
Rule 5: If the temperature is hot, the motor speed blasts.
This fuzzy system works well because the patches will cover lines that correspond to relations between temperature and motor speed if they are non-linear and squiggle. In contrast a similar bivalent system might be built of many specific rules such as if temparture is 60 degrees than the motor speed is 50. Patches developed from rules like this would only be points, and the system developed from it would only define a collection of points-- not a terrific model. Therein lies a greatness of fuzziness.
Another wonderful aspect of fuzziness is that it does not disclude the old bivalent system of logic. The fuzzy spectrum of greys, completely true and completely false simply fit in as black and white. That the old system fits into the new fuzzy system gives me the feeling the old way was on the right track, and fuzzy logic has added a robustness and ingenious efficiency on top of the old system. Also it allows to move forward slowy from the old system rather than taking a radical step away from it.
If fuzziness is so wonderful, what can it really contribute to the development of AI? Let's first look at some things that have already been developed with fuzzy logic. First of all, there are the microwaves in the dorms here that can cook perfect popcorn. There are air conditions with similar rules sets as described above. This system prevents overshoot-undershoot temperature oscillation and consumes less power. There is an auto transmission that uses fuzzy logic to select gear ratio based on engine load, driving style, and road conditions. There is fuzzy factory control software that schedules taks and assembly line strategies. There is even a fuzzy golf diagnotic system that selects golf clubs based on golfer's physique and swing. The list is lengthy and amazing. From toasters to train systems, fuzzy logic is making machines "smarter".
To make apparent the contribution that fuzzy logic can make to AI (here I am thinking of human AI), let's look at some of the products that display intelligence in more humanlike tasks. Sony has developed a fuzzy based palm top computer that can recognize handwritten Kanji characters. Epson has made a translator that recognizes and translates words in a pencil sized unit. Most interesting was a washing machine that adjusts its washing strategy based on sensed dirt level, fabric type, load size and water level and used a neural network to tune the rules to a user's tastes.
This kind of system is an example of adaptive fuzzy logic. With the help of a neural net, it can learn from the data it has collected and adjust its rules. This kind of set up has tremendous possibilities.
Fuzzy Thinking : The New Science of Fuzzy Logic was the primary source for this report.