Maybe I'm wrong, but last I checked "Fuzzy Logic" means a perception based on personal outlook. For example, I'm 5'8", a man who is 6'0" would be "tall" to me, and considering the average male is about my height, 6'0" would be "tall" in consideration even to a 6'8" person. However, 5'9" person begins to get "fuzzy" in tallness category. Where does the GA in this film about eating show fuzzy logic? A rename to "GA evolution" would probably be better.
You are not wrong, but there is more to the theory than only that, including practical applications, such as Fuzzy controllers.
In the experiment, GA is used to find the optimal crispy boundaries for several fuzzy groups. The selected parameters are used in the decision making circuitry.
So basically you are saying that they see their world as "the food is near you" or "the food is far to the right of you" and they have to figure out what that means? If this is true, you are taking a probabilistic encoder to make a bunch of agents learn how to decode it, adding an extra step in the process, now it would be cooler if you had 1 "see" the food and communicate to another "blind" agent to get it, that would show some fuzzy logic (keeping comms nonmathematical) and be interesting.
The circuit is basic, equivalent to 50 or less transistors. The job is to figure how to connect input and output in a optimal fashion. Any addition in functionality, such as multi-agent cooperation, would further and unnecessarily complicate the implementation. Fuzzy controllers are the simplest, closest thing to human thinking you can shrink into a small number of components,? and easier to test and implement as well.
I will agree with you on the simplest and easiest, but as far as being closest to human thinking, neural networks takes the cake on that one.....transistors are either on or off, neural networks are more analog which is closer to a neurons functionality. True that a neuron is an all-in output, but the transmitters can be weighted in inhibition or excitation as well as the gateways on the receiving synapse. Nice work though otherwise, good luck.
The transistors are not an option, but imposed by the circuit model. I use neural networks for pattern recognition, which is its main usage. It requires more transistors to emulate a single neuron, furthermore that kind of connections are not easily implemented in digital circuits. This fuzzy controller was the best choice to use the limited number of connections in the best way possible. The genetic algorithm took care of adjusting the values for me.
fuzzy logic is an artificial intelligence term as well, its been around for a long time. believe it or not some terms exist with different meanings depending on context!
What you describe are fuzzy sets. Typical examples are "short", "medium", "tall" for height or "cold", "cool", "warm", "hot" for temperature. Each of these sets can be described by full enumeration (ick) or a membership function of some sort that describes the degree to which a particular observation belongs to the fuzzy set. In your example, 6'0" might be 1.00 tall and 5'8" might be 0.87 tall. However, 5'8" might be 1.00 medium and 6'0" might be 0.9 medium.
Using this "degree of truth" (membership in fuzzy sets) we can combine properties and their degree of truth into Fuzzy Rules: IF food is tasty and service is great THEN tip is large. This is a control rule, how much tip to leave. Fuzzy Logic enables you to determine the degree of truth of the antecedent and the degree of truth of the consequent (the implication) and combine the degree of truth for all rules with non-zero degrees of truth. Defuzzification then gives a crisp value of a tip.
So such a group of rules is a fuzzy controller. I assume that what the author is saying when he says "optimal crispy boundaries" is that the GA optimized the parameters of the fuzzy sets...but he uses a lot of non-standard language and doesn't come right out and say what his fuzzy sets and fuzzy rules are, perhaps they're even encoded in a neural network which is a popular approach.
Some statements do make me scratch my head: "limited number of connections", I'm not sure what that might mean.
FL is formal logical model, it itself is not non-deterministic, it has strict rules but it deals with uncertain data which is found in real world. Sometimes it is not possible to know everything about system or it is too expensive and in such cases FL is more than a good solution. Just bear in mind that even classical logic can be viewed as a special case of FL... Try to learn more of it you will be amazed.
I have to say that first this is cool. Secondly, this looks exactly like something I wrote in OpenGL/C++ in 2000 or so. I did put it up with source for dl so it'd be interesting to know if it uses some of my code. xtatxinc was the site and it was called Herbies.
This is a short Matlab code (less than 50 lines) made all by myself in 2001, also with source and demos available online . No point in stealing such an elementary code. Even though, was worth a publication.
You haven't managed to refute any of my points. There isnt a great deal to learn with fuzzy logic - take a logic based model that looks like it should work but doesn't then add noise to the parts you most object to. I think its better science to understand the system sufficently well so that a deterministic part produces the desired behaviour - its not laziness, FL is a degenerate path and time learning it to any degree is time wasted.
you want my meat now eh? ok fuzzy logic may work and even have legitimate applications in practice - I'll grant you that, but look at it this way - philospohically - science, engineering, progress is achieved by reducing disorder and expressing nature in a simpler, more succinct form. From heat into work, motion into Newton's equations, randomness into expectations. All except fuzzy logic, which fails as a logical system so adds mess to bumble forward.
In your simplistic philosophy you ignore all the irreducible complexity that surrounds us and the natural increase of complexity, such as observed in evolution and humanistic cognitive processes. Don't be naive, Fuzzy logic is the best model we have so far able to deal with natural complexity and the imprecision of human thinking.
natural increases in complexity are perfectly well modelled using deterministic methods. In fact deterministic process have been shown capable of passing any statistical test of randomness, capable of limitless complexity and structure and even biological behaviour such as self-reproduction. So, complexity is certainly reducible to deterministic simplicity - fuzzy logic is a tool of those unskilled at achieving this.
BIG LOL fuzzy logic isn't a probalistic approach to reality :)) For example statements: "Bald in 80% percent" doesn't mean the same as "bald on 80%" :)) Little more technicaly speaking : function of possibilty don't must to be integrable to 1, as probability density must ;p
The link was added to the video description.
d4sp1d3r 2 years ago
Maybe I'm wrong, but last I checked "Fuzzy Logic" means a perception based on personal outlook. For example, I'm 5'8", a man who is 6'0" would be "tall" to me, and considering the average male is about my height, 6'0" would be "tall" in consideration even to a 6'8" person. However, 5'9" person begins to get "fuzzy" in tallness category. Where does the GA in this film about eating show fuzzy logic? A rename to "GA evolution" would probably be better.
sabriath 2 years ago
You are not wrong, but there is more to the theory than only that, including practical applications, such as Fuzzy controllers.
In the experiment, GA is used to find the optimal crispy boundaries for several fuzzy groups. The selected parameters are used in the decision making circuitry.
d4sp1d3r 2 years ago
So basically you are saying that they see their world as "the food is near you" or "the food is far to the right of you" and they have to figure out what that means? If this is true, you are taking a probabilistic encoder to make a bunch of agents learn how to decode it, adding an extra step in the process, now it would be cooler if you had 1 "see" the food and communicate to another "blind" agent to get it, that would show some fuzzy logic (keeping comms nonmathematical) and be interesting.
sabriath 2 years ago
The circuit is basic, equivalent to 50 or less transistors. The job is to figure how to connect input and output in a optimal fashion. Any addition in functionality, such as multi-agent cooperation, would further and unnecessarily complicate the implementation. Fuzzy controllers are the simplest, closest thing to human thinking you can shrink into a small number of components,? and easier to test and implement as well.
d4sp1d3r 2 years ago
I will agree with you on the simplest and easiest, but as far as being closest to human thinking, neural networks takes the cake on that one.....transistors are either on or off, neural networks are more analog which is closer to a neurons functionality. True that a neuron is an all-in output, but the transmitters can be weighted in inhibition or excitation as well as the gateways on the receiving synapse. Nice work though otherwise, good luck.
sabriath 2 years ago
The transistors are not an option, but imposed by the circuit model. I use neural networks for pattern recognition, which is its main usage. It requires more transistors to emulate a single neuron, furthermore that kind of connections are not easily implemented in digital circuits. This fuzzy controller was the best choice to use the limited number of connections in the best way possible. The genetic algorithm took care of adjusting the values for me.
d4sp1d3r 2 years ago
fuzzy logic is an artificial intelligence term as well, its been around for a long time. believe it or not some terms exist with different meanings depending on context!
illustriouschin 2 years ago
What you describe are fuzzy sets. Typical examples are "short", "medium", "tall" for height or "cold", "cool", "warm", "hot" for temperature. Each of these sets can be described by full enumeration (ick) or a membership function of some sort that describes the degree to which a particular observation belongs to the fuzzy set. In your example, 6'0" might be 1.00 tall and 5'8" might be 0.87 tall. However, 5'8" might be 1.00 medium and 6'0" might be 0.9 medium.
sgwbutcher 2 years ago
Using this "degree of truth" (membership in fuzzy sets) we can combine properties and their degree of truth into Fuzzy Rules: IF food is tasty and service is great THEN tip is large. This is a control rule, how much tip to leave. Fuzzy Logic enables you to determine the degree of truth of the antecedent and the degree of truth of the consequent (the implication) and combine the degree of truth for all rules with non-zero degrees of truth. Defuzzification then gives a crisp value of a tip.
sgwbutcher 2 years ago
So such a group of rules is a fuzzy controller. I assume that what the author is saying when he says "optimal crispy boundaries" is that the GA optimized the parameters of the fuzzy sets...but he uses a lot of non-standard language and doesn't come right out and say what his fuzzy sets and fuzzy rules are, perhaps they're even encoded in a neural network which is a popular approach.
Some statements do make me scratch my head: "limited number of connections", I'm not sure what that might mean.
sgwbutcher 2 years ago
FL is formal logical model, it itself is not non-deterministic, it has strict rules but it deals with uncertain data which is found in real world. Sometimes it is not possible to know everything about system or it is too expensive and in such cases FL is more than a good solution. Just bear in mind that even classical logic can be viewed as a special case of FL... Try to learn more of it you will be amazed.
AleksandarBakalov 4 years ago 2
some audio would be nice
koilir2 4 years ago
I have to say that first this is cool. Secondly, this looks exactly like something I wrote in OpenGL/C++ in 2000 or so. I did put it up with source for dl so it'd be interesting to know if it uses some of my code. xtatxinc was the site and it was called Herbies.
dsterry 4 years ago
This is a short Matlab code (less than 50 lines) made all by myself in 2001, also with source and demos available online . No point in stealing such an elementary code. Even though, was worth a publication.
d4sp1d3r 4 years ago
You haven't managed to refute any of my points. There isnt a great deal to learn with fuzzy logic - take a logic based model that looks like it should work but doesn't then add noise to the parts you most object to. I think its better science to understand the system sufficently well so that a deterministic part produces the desired behaviour - its not laziness, FL is a degenerate path and time learning it to any degree is time wasted.
dibbuck 4 years ago
Spoke someone with absolute none even superficial knowledge about the subject.
d4sp1d3r 4 years ago
you want my meat now eh? ok fuzzy logic may work and even have legitimate applications in practice - I'll grant you that, but look at it this way - philospohically - science, engineering, progress is achieved by reducing disorder and expressing nature in a simpler, more succinct form. From heat into work, motion into Newton's equations, randomness into expectations. All except fuzzy logic, which fails as a logical system so adds mess to bumble forward.
dibbuck 4 years ago
In your simplistic philosophy you ignore all the irreducible complexity that surrounds us and the natural increase of complexity, such as observed in evolution and humanistic cognitive processes. Don't be naive, Fuzzy logic is the best model we have so far able to deal with natural complexity and the imprecision of human thinking.
d4sp1d3r 4 years ago
natural increases in complexity are perfectly well modelled using deterministic methods. In fact deterministic process have been shown capable of passing any statistical test of randomness, capable of limitless complexity and structure and even biological behaviour such as self-reproduction. So, complexity is certainly reducible to deterministic simplicity - fuzzy logic is a tool of those unskilled at achieving this.
dibbuck 4 years ago
BIG LOL fuzzy logic isn't a probalistic approach to reality :)) For example statements: "Bald in 80% percent" doesn't mean the same as "bald on 80%" :)) Little more technicaly speaking : function of possibilty don't must to be integrable to 1, as probability density must ;p
plmqas 2 years ago
fuzzy logic is junk science - long live determinism!
dibbuck 4 years ago
That was sweet. Fuzzy logic future! I was quite impressed with the efficiency of the collectors.-MM666
mastermike666 4 years ago