I am new to Boolean algebra, I want to know when is Boolean algebra used. I am getting confused with it. Please, anyone can clear it so I can understand? Is Boolean algebra using to simplify mathematical calculation during execution of user program or already used in designed circuit board of computer?
Boolean algebra is used for elementary digital circuits composed of logical gates (AND, OR, NOT, XOR, ...). But it also is applied in many other fields.
Examples include philosophy, literature/search retrieval, software engineering, software verification, logic synthesis, automatic test pattern generation, artificial intelligence, logic in general, discrete mathematics, combinatorics, discrete optimization, constraint programming, game theory, information theory, coding theory ... to name a few.
An overview of Boolean algebra applications is listed in Wikipedia.
The short answer is: both.
Boolean algebra is a science, derived from the name of George Boole, a mathematician, logician and philosopher, who laid the foundation of two-valued logic.
Algebra is the study of mathematical symbols and the rules of manipulating those symbols via formulas.
Therefore, Boolean Algebra is the field of study that focuses on mathematical symbols and the rules of manipulating those symbols via formulas on the domain of 2-valued logical expressions whose foundations were laid down by George Boole.
The most atomic principle of Boolean Algebra is that a statement may be true or false and nothing else. True is represented by 1 and false is represented by 0. Let's see a few expressions
A and B = A * B
that is, A and B is true if and only if both values are true, that is, 1 * 1 = 1, but 1 * 0 <> 1, 0 * 1 <> 1 and 0 * 0 <> 1.
A or B = A + B - A * B
Since we are in the domain of two values, A or B has to be either 0 or 1. So, we exclude A * B from the result of their addition to cover the case when both A and B is true, which would now be evaluated to 1 + 1 - 1 = 1.
These logical formulas and many others are covered in electronic circuits via logical gates, but this field of study is used by you at every day of your life as well, but you do not necessarily realize it.
Whenever you assume that a statement is fully true or fully false, you are in line with Boolean algebra. When you draw a conclusion, you are operating with the logical operator called implication.
Basically, more often than not, when you use logic, you partially or fully apply Boolean standards (hopefully).
So, Boolean Algebra is the field if study that operates with statements. And since science and everyday life is all about thoughts/statements, Boolean Algebra is everywhere, you cannot escape it.
The more interesting question is: if Boolean Algebra is so prevalent, then what is the context of non-Boolean logic?
And the answer is simple. Whenever we operate with the probability of the unknown or partial truths.
If you toss a coin, you know for sure that it will be either a head or a tail, but, before tossing the coin you do not know which one, so you compute the probability, which is 0.5 for each case.
If you throw a dice, then you know that it will either be a six or something else. You do not know in advance whether it will be a six or something else, but you know that there is a 1/6 probability that it will be a six.
Probability is a value between 0 and 1. You can convert the raw probability value that is between 0 and 1 to the more popular % value by multiplying its value with 100. So, the probability of a heads as a result of a coin toss would be (0.5 * 100) % = 50%.
So, probability calculation deals with the unkown, even if the statements would eventually become boolean statements, before some events they cannot be fully evaluated, but they can be predicted to some extend. So, this is the field of probability calculation.
Statistics is the field that assumes that the frequency of events in a larg input pattern that's known from the past can predict the future. It's a case of applied probabilities. This is also a non-boolean approach to statements.
Fuzzy logic (no, this is not a joke, there is actually a field of study that goes by this name) deals with partial truths. If you are during the process of eating your lunch, then it is not true that you ate your lunch, neither that you did not eat your lunch, because you have eaten a part of it and you have completed it to a certain extent. So, Fuzzy logic is also a multi-valued field of study, which deals with partial truths.
Conclusion
Boolean algebra is used everywhere, including the areas you have been wondering about and much more. So, Boolean algebra is used both in the circuits and mathematical computations. The very bits that comprise any information (except for the esotheric qbits, but in order not to confuse you I will not delve into that area here) are statements on their own. A 0 means lack of current and a 1 means the presence of current. So, if we have a number represented on 8 bits (for the sake of simplicity), that's a set of 8 statements that ultimately results in the representation of a number.
For example, a 15 is represented (on 8 bits) as
00001111
because
15
= 0 * 1^7 + 0 * 1^6 + 0 * 1^5 + 0 * 1^4 + 1* 1^3 + 1 * 1^2 + 1 * 1^1 + 1 * 1^1
= 0 * 128 + 0 * 64 + 0 * 32 + 0 * 16 + 1 * 8 + 1 * 4 + 1 * 2 + 1 * 1
So even the numbers you see in front of you on the computer are evaluated with Boolean algebra.
Related
I'm very interested in the idea of creating/designing (but most likely only imagining) a ternary computer rather than a binary computer.
If I were to do this, I would used a balanced base-3 system, so a trit (trit is to base-3 as bit is to base-2) could be -1, 0, or +1. Storing data using trits would be approximately 36% more compact than storing data using bits like we do on today's computers, however ternary arithmetic would be much more complicated so there's no telling whether an ALU using ternary-computing would be faster or slower than binary.
But I digress, that's just a little background stuff that doesn't entirely pertain to the question, but it is related. :)
So, possible values for a trit:
-1 is off/false, same as 0 in binary.
0 is unknown. No equivalent in binary.
+1 is on/true, same as 1 in binary.
My question is...what is the point of that 0 in terms of computing? For example, I've been reading up a lot on logic gates and I understand both how they work and how they can work together to create an ALU. A binary AND gate is very simple, and combined with other binary logic gates they could all be used in combination to perform arithmetic such as creating an Adder, or a unit that performs addition.
I can't even comprehend how this would be done using ternary-computing. How would the unknown (0) factor into the logic gates and be used to perform arithmetic? Hell, I can't even comprehend what outputs a ternary AND gate would put out and how they'd be used.
For example, I would assume for a ternary computer an AND gate would accept 3 inputs instead of 2. Let's call the inputs A, B, and C.
In a binary AND gate, A and B can be 0 or 1. There are four possible combinations of what A and B could be input into the AND gate as. This results in only four possible outputs from the AND gate. If A and B are both 1 then the AND gate outputs a 1. If it is any of the other three combinations of A and B then it outputs 0. (possible results from AND gate considering all possible A/B combinations: 0, 0, 0, 1)
A ternary AND gate would take in 3 inputs, right? So in a ternary AND gate, A, B, and C could be -1, 0, or 1. This means that there are 27 possible combinations for A/B/C. Rather than listing out the possible outcomes for each combination, I'll just add them up for you guys. :)
Anyway, there is only one combination in which 1 will be the output, there are 7 combinations in which 0 will be the output (assuming if A, B, and C are all 0 the AND gate will output 0), and there are 19 combinations in which -1 will be the output. In a binary Adder if the gate throws a 1 it would be sent off to another gate to be evaluated and so on until the addition is complete. In ternary...what would a gate do if it received a 0?
I know that's a lot of reading, so I'll try to sum it up and list the main questions below:
How would the 0 of a trit in a balanced ternary system be used/handled in logic gates?
If a logic gate outputs a 0, and the gate is being used in an ALU to perform arithmetic (let's say addition for example), how would the gate that receives the 0 be expected to handle it? Basically, how would one go about creating an Adder using ternary logic?
And lastly, am I correct assuming that in a ternary computer logic gates would accept 3 inputs instead of 2 like binary computers, or would logic gates still be dyadic?
An essential goal of ALU design is to perform arithmetic on integers. In the first place addition (subtraction), then multiplication and division.
When written in base 3, these operations are well defined. For instance
+ | 0 1 2
------------
0 | 0 1 2
1 | 1 2 10
2 | 2 10 11
As with binary arithmetic, on needs to compute a sum trit and a carry. When the carry is propagated, the following table applies
+c| 0 1 2
------------
0 | 1 2 10
1 | 2 10 11
2 | 10 11 12
So you indeed need two three-input functions (two trits in and a carry in), giving the sum trit and the carry out bit. (Notice that binary ALUs add the same way: two bits in and a carry in giving a sum and a carry out bit.)
Whether this can be implemented from elementary dyadic or triadic gates would be technology dependent.
The logical predicates AND/OR have no reason to be modified and should remain binary. Boolean arithmetic remains Boolean.
Besides, if you enumerate all ternary functions of two ternary arguments (i.e. 9 input combinations), you find 19683 of them. Contrast this to 16 binary functions. This mess is unmanageable. (Don't even think of all triadic ternary functions, 7625597484987 of them.)
Okay, so I believe I may have found the answer to my questions.
So, there's no reason to reinvent the wheel, using standard binary logic gates would work just fine in a ternary computer.
In binary, 0 is false and 1 is true, and a number in binary is to be read from right to left, each digit following a power of two. For example, 10010 would be 18 (2 + 16). This number system is incremental, meaning the more 1's you have to the left the higher the number would be if converted to decimal, but there's no way to decrement with this system. All of this is done using transistors that make use of only caring about whether there is voltage flowing through it or if there isn't, thus determining if there is voltage it is on, and the bit is a 1, and if not then it is off, and the bit is a 0.
In ternary, there wouldn't be an on/off. Unlike the standard transistor in binary computers that tests whether there is or isn't voltage to determine the bit's value, a transistor for a ternary computer would test for if the voltage is negative, positive, or a ground. (-1 being negative voltage, 0 being the ground, +1 being positive voltage).
Using this system, decimal numbers would be written in ternary similarly to how a decimal number would be written in binary with one exception: ternary allows for decrementation. Take a number in binary for example, with the first digit starting on the right the digits correlate to 2^0, then 2^1, and so on. In binary all the digits with 1's and their correlating power of 2 is then added up to give you your number.
Now imagine ternary. From right-to-left it would follow 3^0, 3^1, 3^2, and so on. however, a +1 trit would indicate that the power of 3 that the digit correlates to is positive, a 0 trit would mean that digit is ignored just as a 0 does the same in binary, and a -1 trit would mean that the power of 3 that the digit correlates to is negative. This allows for both incrementation and decrementation.
Take this ternary number for example: (I'm going to use '-' instead of '-1', and '+' to represent '+1': +-0+-
this would be, reading from left to right, (-1)+(3)+(-27)+(81) = 56
While it's true that ternary and dyadic logic gates are compatible, an ALU would have to be designed very differently. Basically that's it. :)
I am unsure how to use the Distributive property on the following function:
F = B'D + A'D + BD
I understand that F = xy + x'z would become (xy + x')(xy + z) but I'm not sure how to do this with three terms with two variables.
Also another small question:
I was wondering how to know what number a minterm is without having to consult (or memorise) the table of minterms.
For example how can I tell that xy'z' is m4?
When you're trying to use the distributive property there, what you're doing is converting minterms to maxterms. This is actually very related to your second question.
To tell that xy'z' is m4, think of function as binary where false is 0 and true is 1. xy'z' then becomes 100, binary for the decimal 4. That's really what a k-map/minterm table is doing for you to give a number.
Now an important extension of this: the number of possible combinations is 2^number of different variables. If you have 3 variables, there are 2^3 or 8 different combinations. That means you have min/maxterm possible numbers from 0-7. Here's the cool part: anything that isn't a minterm is a maxterm, and vice versa.
So, if you have variables x and y, and you have the expression xy', you can see that as 10, or m2. Because the numbers go from 0-3 with 2 variables, m2 implies M0, M1, and M3. Therefore, xy'=(x+y)(x+y')(x'+y').
In other words, the easiest way to do the distributive property in either direction is to note what minterm or maxterm you're dealing with, and just switch it to the other.
For more info/different wording.
I have a MySQL database I'm searching through. Lets say this is a database of people. When querying for a specific record, it is possible to find a match 100% on each attribute. But querying the database to find closest match on probability (closest matches on table attributes) is more of the strategy.
In this scenario, does it make sense to create a temporary table (much like a tally-sheet) to indicate what attributes match/what attributes are present? What is the typical approach to doing advanced searches on database like this?
Example (below) of a hypothetical stored Procedure
*parameters are just to exemplify how I would search. I'm not concerned how to perform my selects. Question is about approach, strategy, technique *
call FindPerson ("Brown Eyes", "Brown hair", "Height:6'1", "white", "Name:Joe" ,"weight180", "Age 34" "sex m");
RESULT TABLE
NAME AGE HEIGHT WEIGHT HAIR SKIN sex RANK_MATCH
Joe 32 6'1 180 Brown white m 1
Mike 33 6'1 179 Brown white m 2
James 31 6'0 179 Brown black m 3
Just out of my mind. You can create your own score and sort by it. Something like
SELECT `id`,
(IF(`age`=32,1,0)+IF(`height`="6'1",1,0)+...) as `score`
FROM `people`
HAVING `score` > 0
ORDER BY `score` DESC
LIMIT 10;
With this, you can handle every field with its own comparison, and also weight the individual attributes by not just add 1 but 2 or more.
But I'm quiet not sure, how performant this is.
The approach I would use would be to create a scoring function (your stored proc) that would evaluate the given input's standard distance from the mean.
In the proc, you would judge each criteria in a fashion similar to:
INPUT AGE: 32
calculate MEAN of AGE WHERE (sex = m): 34.5
calculate STANDARD DEVIATION of AGE WHERE (sex = m): 2.5
calculate how many STDEVs 32 is from the 34.5 (also known as z-score): 1
Repeat this process for all numeric datatypes, summing them and ORDER BY the sum.
In doing so, the following schema change would be required: height changed from foot/inch form to strictly inches.
Depending on your needs, you may also consider coming up with an arbitrary scale for sex and skin color/hair color. Of course, you may think that measures like these should NOT be factored in because of how drastically it would change the scoring function. If you chose to, you'd have to find some number that would be added to the above SUM...but it's hard because nominative variables don't translate easily into these kinds of things.
If you find that haircolor/skin color is able to be usefully transferred into say, the continous color spectrum, your scoring tidbit would be the same...color value of input vs color value of means and standard deviations.
The query that would find your matches would be something to the effect of:
SELECT
ABS(INPUT_AGE - AVG(AGE)) / STD(AGE) AS age_z,
ABS(INPUT_WT - AVG(WT)) / STD(WT) AS wt_z,
...
(age_z + wt_z + ...) AS score
FROM `table`
ORDER BY score ASC
These questions regard a set of data with lists of tasks performed in succession and the total time required to complete them. I've been wondering whether it would be possible to determine useful things about the tasks' lengths, either as they are or with some initial guesstimation based on appropriate domain knowledge. I've come to think graph theory would be the way to approach this problem in the abstract, and have a decent basic grasp of the stuff, but I'm unable to know for certain whether I'm on the right track. Furthermore, I think it's a pretty interesting question to crack. So here we go:
Is it possible to determine the weights of edges in a directed weighted graph, given a list of walks in that graph with the lengths (summed weights) of said walks? I recognize the amount and quality of permutations on the routes taken by the walks will dictate the quality of any possible answer, but let's assume all possible walks and their lengths are given. If a definite answer isn't possible, what kind of things can be concluded about the graph? How would you arrive at those conclusions?
What if there were several similar walks with possibly differing lengths given? Can you calculate a decent average (or other illustrative measure) for each edge, given enough permutations on different routes to take? How will discounting some permutations from the available data set affect the calculation's accuracy?
Finally, what if you had a set of initial guesses as to the weights and had to refine those using the walks given? Would that improve upon your guesstimation ability, and how could you apply the extra information?
EDIT: Clarification on the difficulties of a plain linear algebraic approach. Consider the following set of walks:
a = 5
b = 4
b + c = 5
a + b + c = 8
A matrix equation with these values is unsolvable, but we'd still like to estimate the terms. There might be some helpful initial data available, such as in scenario 3, and in any case we can apply knowledge of the real world - such as that the length of a task can't be negative. I'd like to know if you have ideas on how to ensure we get reasonable estimations and that we also know what we don't know - eg. when there's not enough data to tell a from b.
Seems like an application of linear algebra.
You have a set of linear equations which you need to solve. The variables being the lengths of the tasks (or edge weights).
For instance if the tasks lengths were t1, t2, t3 for 3 tasks.
And you are given
t1 + t2 = 2 (task 1 and 2 take 2 hours)
t1 + t2 + t3 = 7 (all 3 tasks take 7 hours)
t2 + t3 = 6 (tasks 2 and 3 take 6 hours)
Solving gives t1 = 1, t2 = 1, t3 = 5.
You can use any linear algebra techniques (for eg: http://en.wikipedia.org/wiki/Gaussian_elimination) to solve these, which will tell you if there is a unique solution, no solution or an infinite number of solutions (no other possibilities are possible).
If you find that the linear equations do not have a solution, you can try adding a very small random number to some of the task weights/coefficients of the matrix and try solving it again. (I believe falls under Perturbation Theory). Matrices are notorious for radically changing behavior with small changes in the values, so this will likely give you an approximate answer reasonably quickly.
Or maybe you can try introducing some 'slack' task in each walk (i.e add more variables) and try to pick the solution to the new equations where the slack tasks satisfy some linear constraints (like 0 < s_i < 0.0001 and minimize sum of s_i), using Linear Programming Techniques.
Assume you have an unlimited number of arbitrary characters to represent each edge. (a,b,c,d etc)
w is a list of all the walks, in the form of 0,a,b,c,d,e etc. (the 0 will be explained later.)
i = 1
if #w[i] ~= 1 then
replace w[2] with the LENGTH of w[i], minus all other values in w.
repeat forever.
Example:
0,a,b,c,d,e 50
0,a,c,b,e 20
0,c,e 10
So:
a is the first. Replace all instances of "a" with 50, -b,-c,-d,-e.
New data:
50, 50
50,-b,-d, 20
0,c,e 10
And, repeat until one value is left, and you finish! Alternatively, the first number can simply be subtracted from the length of each walk.
I'd forget about graphs and treat lists of tasks as vectors - every task represented as a component with value equal to it's cost (time to complete in this case.
In tasks are in different orderes initially, that's where to use domain knowledge to bring them to a cannonical form and assign multipliers if domain knowledge tells you that the ratio of costs will be synstantially influenced by ordering / timing. Timing is implicit initial ordering but you may have to make a function of time just for adjustment factors (say drivingat lunch time vs driving at midnight). Function might be tabular/discrete. In general it's always much easier to evaluate ratios and relative biases (hardnes of doing something). You may need a functional language to do repeated rewrites of your vectors till there's nothing more that romain knowledge and rules can change.
With cannonical vectors consider just presence and absence of task (just 0|1 for this iteratioon) and look for minimal diffs - single task diffs first - that will provide estimates which small number of variables. Keep doing this recursively, be ready to back track and have a heuristing rule for goodness or quality of estimates so far. Keep track of good "rounds" that you backtraced from.
When you reach minimal irreducible state - dan't many any more diffs - all vectors have the same remaining tasks then you can do some basic statistics like variance, mean, median and look for big outliers and ways to improve initial domain knowledge based estimates that lead to cannonical form. If you finsd a lot of them and can infer new rules, take them in and start the whole process from start.
Yes, this can cost a lot :-)
I need some help with this Boolean Implication.
Can someone explain how this works in simple terms:
A implies B = B + A' (if A then B). Also equivalent to A >= B
Boolean implication A implies B simply means "if A is true, then B must be true". This implies (pun intended) that if A isn't true, then B can be anything. Thus:
False implies False -> True
False implies True -> True
True implies False -> False
True implies True -> True
This can also be read as (not A) or B - i.e. "either A is false, or B must be true".
Here's how I think about it:
if(A)
return B;
else
return True;
if A is true, then b is relevant and should be checked, otherwise, ignore B and return true.
I think I see where Serge is coming from, and I'll try to explain the difference. This is too long for a comment, so I'll post it as an answer.
Serge seems to be approaching this from the perspective of questioning whether or not the implication applies. This is somewhat like a scientist trying to determine the relationship between two events. Consider the following story:
A scientist visits four different countries on four different days. In each country she wants to determine if rain implies that people will use umbrellas. She generates the following truth table:
Did it rain? Did people Does rain => umbrellas? Comment
use umbrellas?
No No ?? It didn't rain, so I didn't get to observe
No Yes ?? People were shielding themselves from the hot sun; I don't know what they would do in the rain
Yes No No Perhaps the local government banned umbrellas and nobody can use them. There is definitely no implication here.
Yes Yes ?? Perhaps these people use umbrellas no matter what weather it is
In the above, the scientist doesn't know the relationship between rain and umbrellas and she is trying to determine what it is. Only on one of the days in one of the countries can she definitively say that implies is not the correct relationship.
Similarly, it seems that Serge is trying to test whether A=>B, and is only able to determine it in one case.
However, when we are evaluating boolean logic we know the relationship ahead of time, and want to test whether the relationship was adhered to. Another story:
A mother tells her son, "If you get dirty, take a bath" (dirty=>bath). On four separate days, when the mother comes home from work, she checks to see if the rule was followed. She generates the following truth table:
Get dirty? Take a bath? Follow rule? Comment
No No Yes Son didn't get dirty, so didn't need to take a bath. Give him a cookie.
No Yes Yes Son didn't need to take a bath, but wanted to anyway. Extra clean! Give him a cookie.
Yes No No Son didn't follow the rule. No cookie and no TV tonight.
Yes Yes Yes He took a bath to clean up after getting dirty. Give him a cookie.
The mother has set the rule ahead of time. She knows what the relationship between dirt and baths are, and she wants to make sure that the rule is followed.
When we work with boolean logic, we are like the mother: we know the operators ahead of time, and we want to work with the statement in that form. Perhaps we want to transform the statement into a different form (as was the original question, he or she wanted to know if two statements are equivalent). In computer programming we often want to plug a set of variables into the statement and see if the entire statement evaluates to true or false.
It's not a matter of knowing whether implies applies - it wouldn't have been written there if it shouldn't be. Truth tables are not about determining whether a rule applies, they are about determining whether a rule was adhered to.
I like to use the example: If it is raining, then it is cloudy.
Raining => Cloudy
Contrary to what many beginners might think, this in no way suggests that rain causes cloudiness, or that cloudiness causes rain. (EDIT: It means only that, at the moment, it is not both raining and not cloudy. See my recent blog posting on material implication here. There I develop, among other things, a rationale for the usual "definition" for material implication. The reader will require some familiarity with basic methods of proof, e.g. direct proof and proof by contradiction.)
~[Raining & ~Cloudy]
Judging from the truth tables, it is possible to infer the value of a=>b only for a=1 and b=0. In this case the value of a=>b is 0. For the rest of values (a,b), the value of a=>b is undefined: both (a=>b)=0 ("a doesn't imply b") and (a=>b)=1 ("a implies b") are possible:
a b a=>b comment
0 0 ? it is not possible to infer whether a implies b because a=0
0 1 ? --"--
1 0 0 b is 0 when a is 1, so it is possible to conclude
that a does not imply b
1 1 ? whether a implies b is undefined because it is not known
whether b can be 0 when a=1 .
For a to imply b it is necessary and sufficient that b=1 always when a=1, so that there is no counterexample when a=1 and b=0. For the rows 1, 2 and 4 in the truth table it is not known whether there is counterexample: these rows do not contradict to (a=>b)=1, but they also do not prove (a=>b)=1 . In contrast, row 3 immediately disproves (a=>b)=1 because it provides a counterexample when a=1 and b=0.
I guess I may shock some readers with these explanations, but it seems there are severe errors somewhere in the basics of the logic we are taught, and that is one of the reasons for such problems as Boolean Satisfiability being not solved yet.
The best contribution on this question is given by Serge Rogatch.
Boolean logic applies only where the result of quantifying(or evaluation) is either true or false and the relationship between boolean logic propositions is based on this fact.
So there must exist a relationship or connection between the propositions.
In higher order logic, the relationship is not just a case of on/off, 1/0 or +voltage/-voltage, the evaluation of a worded proposition is more complex. If no relationship exists between the worded propositions, then implication for worded propositions is not equivalent to boolean logic propositions.
While the implication truth table always yields correct results for binary propositions, this is not the case with worded propositions which may not be related in any way at all.
~A V B truth table:
A B Result/Evaluation
1 1 1
1 0 0
0 1 1
0 0 1
Worded proposition A: The moon is made of sour cream.
Worded proposition B: Tomorrow I will win the lotto.
A B Result/Evaluation
1 ? ?
As you can see, in this case, you can't even determine the state of B which will decide the result. Does this make sense now?
In this truth table, proposition ~A always evaluates to 1, therefore, the last two rows don't apply. However, the last two rows always apply in boolean logic.
http://thenewcalculus.weebly.com
Here's a compact statement:
Suppose we have two statements, A and B, each of which could either be true or false. Without any further information, there are 2 x 2 = 4 possibilities: "A and not B", "B and not A", "neither A nor B", and "both A and B".
Now impose the additional restriction that "if A, then also B". After imposing this restriction, the expression "x -> y", where -> is the "implication" operator, denotes whether it is still possible for A == x and B == y. The only outcome that is no longer possible after this additional restriction is A == 1 and B == 0, since that contradicts the restriction itself. Hence, we have 1 -> 0 is zero, and every other pair is 1.