Syntax, Semantics and Rules of Inference
The propositional calculus (PC) is a formal language that adequately represents the set of valid (truth preserving) inferences which depend on coordinate expressions such as and, or, not, if…then…, if and only if. From the optic of PC, we are only interested in those inferences whose validity depends on the role of these expressions. Hence, we abstract away from what particular sentences mean. Here is a simple example:
(1)a. Man U have won the European Cup and
b. Therefore, Man U have won the European Cup.
is clearly valid - it is not possible for b. to be false and for a. to be true;
alternatively, if a. is true, then b. must be true.
But the validity of (1) doesn’t depend on the fact that we are talking about
(2) P & Q
PC is able to represent only those inferences such (2) - those whose validity just depends on the logical constants.
Logical constants = expressions whose meaning is constant, i.e., not variable.
Truth functions = the constants express functions from truth values to truth values.
The language of PC thus includes variables which range over sentences (P, Q, R, S,…) and logical constants.
The standard logical constants of PC =
¬ = not
& = and
v = or
→ = if…, then…
↔ = if and only if (iff)
(i) The identifications here will later be questioned, but, pro tem, think of the constants as expressing the corresponding English terms.
(ii) We shall see later that the five constants mark a redundancy. It is easy to show that we can express the remaining three constants by using either (i) ‘¬’ and ‘&’ or (ii) ‘¬’ and ‘v’ or (iii) ‘¬’ and ‘→’. In fact, we only need one of two logical constants here not listed: ‘↓’ (dagger) and ‘|’ (stroke). Such joys are to come.
Sentences of PC include all and only those symbol strings which are lawful combinations of constants and variables. For example:
(‘Bill isn’t tall’)
b. P → (Q & ¬R)
(‘If Harry is short, then Bill is tall and Mary isn’t blonde’)
c. (P ↔ Q) → ((P → Q) & (Q → P))
(‘If Harry is tall iff Sarah is blonde, then if Harry is tall, then Sarah is blonde, and if
Sarah is blonde, then Harry is tall’)
An excursus on scope
The use of brackets in PC is to avoid scope ambiguities. English, as we saw, is rife with ambiguity, but in PC, we rigorously avoid it - brackets serve this end, as they could do in a regimented version of English.
(3)a. Mad dogs and Englishmen go out in the mid-day sun.
b. [Mad [dogs and Englishmen]] go out in the mid-day sun.
c. [[Mad dogs] and Englishmen] go out in the mid-day sun.
Here is an example from PC
(4)a. P → P & Q
b. (P → P) & Q
c. P → (P & Q)
The latter two formulae are quite distinct: b. is a conjunction which ‘says’ two things are the case (if P, then P and Q); c. is a conditional which ‘says’ if something (P) is the case, then something else (P & Q) is the case. More formally, b. is stronger, c. is weaker.
Strong = takes more for it to be false.
Weak = takes less for it to be true.
Think of it this way: a formula which ‘says’ more is more likely to be false; it makes a greater demand on the world, as it were - saying more equals strength. A formula which says little, places a weak demand on the world - saying little equals weakness. In general, a conditional is always weaker than a conjunction.
If we were just faced with a., the convention is to read it as if it were c., i.e., we take the weaker constant to be the main constant - the constant which defines the kind of formula we have.
Main constant = constant with widest possible scope.
Scope = the scope of an expression is the smallest formula of which it is a part.
It follows that the constant with widest possible scope has the whole containing formula in its scope. For example, in b., ‘→’ scopes just over ‘P → P’, for that is the smallest formula it is a part of. On the other hand, the smallest formula which contains ‘&’ is the whole of b. The case is reversed for c. Explicit marking of scope via brackets removes ambiguity.
Our definition of scope presupposes that we can be precise about what is and is not a formula of PC.
2. Syntax of PC
The syntax of a natural language such as English must be discovered. When we are dealing with formal languages, we can stipulate the syntax, for the languages are our own creations. Of course, this doesn’t mean that we can stipulate anything we please, for we want the language to do a ‘job’ and an acceptable formula should reflect that objective. In the present case, we want PC to codify valid inferences which turn on the constants.
Syntax (for formal languages) = a definition of ‘formula of the language’
Here is an adequate definition for ‘formula of PC’.
(i) P, Q, R,… are formulae of PC.
(ii) If X is a formula of PC, then ¬X,
X & X,
X v X,
and X↔X are formulae of PC.
(iii) These are all the formulae of PC.
(‘X’ is a meta-variable which ranges over formulae of PC - it is not a part of PC itself.)
(Df-PC) is recursive, which means that we can cycle through (ii) to generate all the formulae of PC. In other words, the definition provides us with a finite implicit definition of the infinite set of formulae of PC.
Q1: In (i), we appear to commit ourselves to an indefinite number of variables, and so, if we tried to write out the definition in full, it would no longer be finite, i.e., we couldn’t write it out. Can you see a way of complicating the definition to avoid this problem? (Hint: we just need to discriminate between an infinity of variables, we don’t actually need an infinity of variables.)
Q2: Is (iii) required? What job is it doing?
3. Semantics of PC (Truth Tables)
The semantics for a formal language tell us what the formulae mean, i.e., truth conditions. Since we have abstracted away from the meaning of particular sentences, we simply assume that that any given variable can take the value of True (T) or False (F). We work out the truth conditions of all further formulae, following the syntax, as a function of the meaning of the logical constants, which compositionally form complex formulae from, ultimately, Ps and Qs. We define the meaning of each constant via a basic truth table, with the a truth table for each further formulae being provided as a function of the basic truth tables. We say that a constant expresses a truth function: it takes truth values as argument and produces truth values as values.
Let ‘A’ and ‘B’ be meta-variables which range over PC formulae of arbitrary complexity. For example, the truth table for ‘A & B’ applies to any formulae whose main constant is ‘&’.
¬ & v → ↔
¬ A A & B A v B A → B A ↔ B
F T T T T T T T T T T T T T
T F T F F T T F T F F T F F
F F T F T T F T T F F T
F F F F F F F T F F T F
(i) ‘¬A’ is true iff ‘A’ is false.
(ii) ‘A & B’ is true iff ‘A’ is true and ‘B’ is true.
(iii) ‘A v B’ is true iff either ‘A’ is true or ‘B’ is true.
(iv) ‘A → B’ is true iff it’s not the case that ‘A’ is true and ‘B’ is false.
(v) ‘A ↔ B’ is true iff either ‘A’ is true and ‘B’ is true, or ‘A’ is false and ‘B’ is false.
Each truth table details all possible ways in which the given formula could be assigned the value T or F. That is, a truth table exhausts the values a truth function can have, given all possible arguments. In general, for a formula of n variables, its table will contain 2n rows, each specifying a way the ‘world’ could be such that the formula receives a value. So, with ‘¬P’ we have 21 = 2; with two variable formulae, we have 22 = 4. For the formula, ‘P → (Q → R)’, we have 23 = 8. And so on.
The above tables provide you with all the information you require to specify a truth table for any formula of PC.
An Excursus on the Material Conditional
The conditional can cause problems. First we need some definitions:
Sufficient condition = A is a sufficient condition for B iff A alone, regardless of anything
else, is enough - suffices - for B.
Necessary condition = A is a necessary condition for B iff without A, then no B.
(i) A’s being a sister is a sufficient condition for A’s being a sibling; it is not a necessary condition, for if A were a brother, A would still be a sibling.
(ii) A’s being female is a necessary condition for A’s being a sister; it is not a sufficient condition, for A need not have any siblings at all.
(iii) A’s being a female sibling is a necessary and sufficient condition for A’s being a sister.
A conditional ‘A → B’, expresses a sufficient condition from A to B, and a necessary condition from B to A. In English, there are different formulations of these relations:
(i) If A, then B (A sufficient for B)
(ii) A, if B (B sufficient for A)
(iii) Only if A, B (B sufficient for A)
(iv) A, only if B (A is sufficient for B)
Tautology (logical truth) = a formula whose main column is all Ts, i.e., a function whose
only value is T.
A v ¬A
T T F T
T T F T
F T T F
F T T F
Inconsistency (contradiction, logical falsehood) = a formula whose main column is all
Fs, i.e., a function whose only value
A & ¬ A
T F F T
T F F T
F F T F
F F T F
Contingency = a formula whose main column features both Ts and Fs, i.e., a function
whose value can be both T and F.
Examples are provided by our basic truth tables.
(i) The negation of a tautology is an inconsistency.
(ii) The negation of an inconsistency is a tautology.
(iii) The negation of a contingency is a contingency.
3.1. Definitions - Reducing the Constants
As mentioned above, our five constants signal a redundancy. We can easily show that we only need two constants: either (i) ‘¬’ and ‘&’ or (ii) ‘¬’ and ‘v’ or (iii) ‘¬’ and ‘→’. That is to say, we can contextually define the remaining three truth functions in terms of a given two truth functions. Remember, a truth table defines a constant, so if we can express the truth table for constant ‘$’ without using ‘$’, then we have adequately defined the function expressed by ‘$’. Such definitions allow us to eliminate redundant constants; equally, they allow us to introduce constants.
Here are the definitions if we take ‘¬’ and ‘&’ as given, where ‘↔df’ means ‘logically equivalent by definition’:
(i) A v B ↔df ¬(¬A & ¬B)
(ii) A → B ↔df ¬(A & ¬B)
(iii) A↔ B ↔df ¬(A & ¬B) & ¬(B & ¬A)
Here are the definitions if we take ‘¬’ and ‘v’ as given:
(iv) A & B ↔df ¬(¬A v ¬B)
(v) A → B ↔df ¬A v B
(vi) A↔ B ↔df ¬(¬(¬A v B) v ¬(¬B v A))
Here are the definitions if we take ‘¬’ and ‘→’ as given:
(vii) A & B ↔df ¬(B → ¬A)
(viii) A v B ↔df ¬B→ A
(ix) A↔ B ↔df ¬((A→ B) → ¬(B→ A))
Q: To check the correctness of these definitions, provide truth tables for (i)-(ix) using ‘↔df’ as the main constant. (The subscript is merely notational, and may be happily deleted.)
3.2. Stroke and Dagger
It was mentioned above that we can express every truth function via either of two constants: ‘|’ (stroke) and ‘↓’(dagger).
Stroke (‘Sheffer’s Stroke’ or NAND)
Stroke is a function of two arguments. Its truth table is:
A | B
T F T
T T F
F T T
F T F
which is equivalent to the table for ‘¬(P & Q).’
‘A | B’ is true iff it’s not the case that both ‘A’ is true and ‘B’ is true.
The stroke definitions of the five main logical constants are as follows:
(i) ¬A ↔df A | A
(ii) A & B ↔ df (A | B) | (A | B)
(iii) A v B ↔ df (A | A) | (B | B)
(iv) A → B ↔ df ((A | A) | (B | B)) | (B | B)
(v) A ↔ B ↔ df [(((A | A) | (B | B)) | (B | B)) | (((B | B) | (A | A)) | (A | A))] |
[(((A | A) | (B | B)) | (B | B)) | (((B | B) | (A | A)) | (A | A))]
Q: To check the correctness of the definitions, provide truth tables for the stroke formulae.
Dagger (‘Pierce’s Dagger’ or NOR)
Dagger is a function of two arguments. Its truth table is:
A ↓ B
T F T
T F F
F F T
F T F
‘A ↓ B’ is true iff neither ‘A’ is true nor ‘B’ is true
iff ‘A’ is false and ‘B’ is false.
So, the truth table above is the same as that for ‘¬(A v B)’ and ‘¬A & ¬ B’.
The dagger definitions of the five main constants are as follows:
(i) ¬A ↔df A ↓ A
(ii) A & B ↔df (A ↓ A) ↓(B ↓ B)
(iii) A v B ↔ df (A ↓ B) ↓ (A ↓ B)
(iv) A → B ↔ df [(((A ↓ A) ↓ (B ↓ B)) ↓(A↓A))] ↓ [(((A ↓ A) ↓ (B ↓ B)) ↓ (A ↓ A))]
(v) A ↔ B ↔ df [(((A ↓ A) ↓ (A ↓ A)) ↓ (B ↓ B))] ↓ [(((A ↓ A) ↓ (A ↓ A)) ↓ (B ↓ B))]
Q: To check the definitions, provide truth tables for the dagger formulae.
(i) A | B ↔ df [(A ↓ A) ↓ (B ↓ B)] ↓ [(A ↓ A) ↓ (B ↓ B)] (i.e., negation of dagger &)
(ii) A ↓B ↔ df [(A | A) | (B | B)] | [(A | A) | (B | B)] (i.e., negation of stroke v)
Q: To check the correctness of the definitions, provide truth tables for the formulae treating ‘↔ df’ as the main constant. (Again, delete the notational subscript.)
4. Checking for Validity via Truth Tables
We take an argument to be any collection of assumptions from which a conclusion is drawn, where it is understood that if the assumptions are true, then the conclusion must also be true - equivalently it is not possible for the conclusion to be false and the premises all to be true. From the definition of ‘→’, we see that any argument can be expressed by a conditional, where the assumptions are the antecedent (if there is more that one assumption, then the antecedent will be the conjunction of them) and the conclusion will be the consequent.
Can we check the truth table for a conditional to see whether the corresponding argument is valid?
Yes. If the conditionalisation of an argument is a tautology, then the corresponding argument is valid.
Why? Because a conditional is false exactly when its antecedent is true and its consequent is false. This is precisely the conditions under which the corresponding argument would be invalid. Therefore, if this case doesn’t arise, then the argument isn’t invalid, i.e., it’s valid; or, equivalently, the main column of the truth table for the conditional will not feature a F, i.e., the conditional will be a tautology. Thus, a necessary and sufficient condition for an argument to be valid is that its conditionalisation is a tautology.
P → Q
(ii) Conditionalisation and Truth Table
(P & (P → Q)) → Q
T T T T T T T
T F T F F T F
F F F T T T T
F F F T F T F
Note: A conditionalisation whose truth table marks it as contingent is not a ‘sometimes’ valid argument - there is no such notion; e.g., ‘¬P & (P → Q) → Q’.
A Non-Truth Table Method to Check for Validity (an algorithm)
(i) Conditionalise the argument.
(ii) Assume the conditional is false, i.e., put a F under the main constant.
(iii) On the assumption of (ii), the antecedent of the conditional is true and the consequent
false. Therefore, put a T under the main constant of the antecedent and put a F under
the main constant of the consequent.
(iv) Proceed, guided by the basic truth tables, to put a T or F under each variable and
constant of the conditional.
(v) If each variable and constant of the conditional can be assigned a T or F, then the
conditional is not a tautology, for the assignment constitutes a row of the complete
truth table under which the conditional is false, i.e., the conditional is not a tautology,
i.e., the corresponding argument is not valid.
(vi) If a complete assignment can’t be made (i.e., if a basic truth table is contradicted),
then the assumption of (ii) is false, i.e., the complete truth table for the conditional
doesn’t contain a F in its main column, i.e., the conditional is a tautology, i.e., the
corresponding argument is valid.
(i) (P & (P → Q)) → Q
T T T T F F F
4 2 6 5 7 1 3
(ii) ¬ P & (P → Q) → Q
T F T F T F F F
4 5 2 7 6 8 1 3
Q: The method tells us whether a conditional formula is a tautology. Does it tell us whether a formula is (i) an inconsistency or (ii) a contingency? Give reasons.
5. Rules of Inference and Proof Theory for PC
Rule of inference = a rule which allows us to transform or combine formulae under the
preservation of validity (truth), i.e., if the input to the rule is true, then
its output is also true.
A formal proof = a finite sequence of PC formulae such that each member is either an
assumption or a transformation or combination of preceding formulae
according to the rules of inference. The last member of the sequence
is the conclusion and is such that the conjunction of its negation and
and the assumptions upon which it rests is a contradiction.
Rules of inference
(i) Rule of Assumption (A)
Insert any formula at any stage into a proof. The assumed formula rests upon the assumption of itself.
(ii) Double Negation (DN)
a. ¬¬A b. A
(‘Two negations cancel each other out’.)
The derived formula rests upon the assumptions upon which the transformed formula rests.
(ii) &-Introduction (&I)
A & B
(‘One can derive the conjunction of any two preceding formulae’)
The derived formula rests upon the assumptions upon which the individual conjoined formulae rest.
(iii) &-Elimination (&E)
A & B
(‘One can derive either conjunct form a preceding conjunction’)
Either of the derived formulae rest upon the assumptions upon which the conjunction rests.
(iv) v-Introduction (vI)
A v B
(‘One can derive a disjunction which has a disjunct as any preceding formula’)
The derived formula rests upon the assumptions upon which the given disjunct rests.
(v) v-Elimination (vE)
A v B
(‘One can derive a formula from a disjunction so long as one can derive the formula from both disjuncts of the initial disjunction’)
The derived formula rests upon the assumptions of (i) the initial disjunction, (ii) each disjunct as assumption, and (iii) each conclusion from each disjunct assumption, minus assumptions upon which rest two or more formula on which the rule operates.
(vii) Modus Ponendo Ponens (MPP)
A → B
(‘One can derive the consequent of a conditional if the antecedent of the conditional is also given’)
The derived formula rests upon the assumptions upon which the conditional and its antecedent rest.
(viii) Conditional Proof (CP)
(‘One can derive a conditional from the assumption of its antecedent if one can derive the conditional’s consequent from the assumption’)
The derived formula rests upon the assumptions upon which the assumption and its derived formula rest, minus any assumptions upon which both rest.
(ix) Reductio ad Absurdum (RAA)
¬A & A
(‘One can derive the negation of an assumption if, from that assumption, one can derive a contradiction’)
The derived formula rests upon the assumptions upon which the initial assumption and the contradiction rest, ninus any assumptions upon which both rest.
(x) Equivalence definition (↔df)
a. A ↔ B_______ b. A→ B & B →A
A→ B & B →A A ↔ B
(‘One can rewrite a bi-conditional as a conjunction of conditionals, from right to left and left to right, and vice versa.)
The derived formula rests upon the assumptions upon which the given formula rests.
A simple sample derivation
P & Q ├ P & (P v Q) (‘A ├ B’ means ‘B is derivable from A via rules of inference’.)
1(1) P & Q A
1(2) Q 1&E
1(3) P v Q 2vI
1(4) P 1&E
1(5) P & (P v Q) 3,4&I
Explanation, from left to right
(i) The first numbers indicate assumptions upon which the corresponding formula rests.
In this example, all formulae rest just on the first and only assumption.
(ii) The bracketed number is the number of the line.
(iii) In the middle, we have the derivation itself.
(iv) On the right, we have the rules and the line numbers to which they apply. So, look at
line (5). This tells us that the formula is the result of the rule &-introduction applied
to lines (3) and (4).
We can apply our algorithm to check that the derivation is valid.
P & Q → P & (P v Q)
T T T F T F T T T
4 2 5 1 6 3 X
A more complex example
We know that A v B ↔df ¬(¬A & ¬B). So, if our rules are adequate, we should be able to derive ‘P v Q’ from ‘¬(¬P & ¬Q)’, and vice versa.
P v Q ├ ¬(¬P & ¬Q)
1 (1) P v Q A (The initial assumption)
2 (2) ¬P & ¬Q A (Assumption for RAA)
3 (3) P A (The first assumption for vE)
2 (4) ¬P 2&E (Derivation for the purposes of forming a contradiction)
2,3 (5) P & ¬P 3,4&I (A contradiction)
3 (6) ¬(¬P & ¬Q) 2,5RAA (The application of RAA)
7 (7) Q A (The second assumption of vE)
2 (8) ¬Q 2&E (Derivation for the purposes of forming a contradiction)
2,7 (9) Q & ¬Q 7,8&I (A contradiction)
7 (10) ¬(¬P & ¬Q) 2,9RAA (The application of RAA)
1 (11) ¬(¬P & ¬Q) 1,3,6,7,10vE (The application of vE - end of proof)
Q1: Prove ¬(¬P & ¬Q) ├ P v Q. (Hint: begin by assuming ‘¬P v Q’ for the purposes of RAA. For those who are stuck, a proof is given in Lemmon, p.38.)
Q2: Prove that the remainder of our definitions hold (Lemmon doesn’t contain the proofs):
(i) P → Q ├ ¬(P & ¬Q)
(ii) P↔ Q ├ ¬(P & ¬Q) & ¬(Q & ¬P)
(ii) P & Q ├ ¬(¬P v ¬Q)
(iv) P → Q ├ ¬P v Q
(v) P↔ Q ├ ¬(¬(¬P v Q) v ¬(¬Q v P))
(vi) P & Q ├ ¬(Q → ¬P)
(vii) P v Q ├ ¬Q→ P
(viii) P↔ Q ├ ¬((P→ Q) → ¬(Q→ P))
Hints for proving
(i) If the formula to be proved is a conditional, then assume its antecedent, and from that
prove its consequent. Then by CP, one will have proved the formula.
(ii) If the formula to be proved is a negation (i.e., its main constant is ‘¬’), then assume
the formula negated. Generate a contradiction from your assumption, then by RAA,
negate the assumption to leave you with the formula that was to be proved.
(iii) If you start with an equivalence, then immediately break it up via ‘↔df’. The proofs
here tend to be longer, but no more complex.
(iv) The more interesting proofs require you to make unobvious assumptions. Always
think about what is to be proved. If, say, its main constant is a negation, then you will
probably require RAA. If you can’t see how to get a contradiction, then work
(v) Again, the more interesting proofs will involve embedded arguments - RAAs within
RAAs within vEs. With practice, you will enjoy the challenge.
Given that a conditional is true if its consequent is true, we should be able to prove
Q ├ P → Q
1 (1) Q A
2 (2) P A
1,2(3) Q & P 1,2&I
1,2(4) Q 3&E
1 (5) P → Q 2,4CP
Similarly, if the antecedent of a conditional is false, then the conditional is true; hence, we should be able to prove
¬P ├ P → Q
1 (1) ¬P A
2 (2) P A
3 (3) ¬Q A
1,3 (4) ¬P & ¬Q 1,3&I
1,3 (5) ¬P 4&E
1,2,3(6) P & ¬P 2,5&I
1,2 (7) ¬¬Q 3,6RAA
1,2 (8) Q 7DN
1 (9) P→ Q 2,8CP
(See Lemmon, pp.58-60 for a discussion of these cases. His method of proof is much less elegant than that provided.)
A theorem is a formula that can be proved resting upon no assumptions. If our system is adequate, all and only tautologies should be theorems.
From any given proof, we can prove a theorem by CP.
Taking the example from just above,
├ Q → (P → Q)
1 (1) Q A
2 (2) P A
1,2(3) Q & P 1,2&I
1,2(4) Q 3&E
1 (5) P → Q 2,4CP
(6) Q → (P → Q) 1,5CP
Not all theorems have a conditional form; thus, we can’t employ CP to prove all theorems:
├ ¬(P & ¬ P)
1(1) P & ¬P A
(2) ¬(P & ¬ P) 1,1RAA
Q: Prove ‘P v ¬P’ as a theorem. (Hint: Assume ‘¬(P v ¬P)’ for the purposes of RAA. If you are stuck, Lemmon, pp.52-3, provides a proof and discussion.)
5.1. Truth Tables and Completeness of PC
PC is complete (and consistent) iff all and only tautologies are provable as theorems.
Lemmon (chp.5) provides a meta-theoretic proof. For our purposes, a simpler way of showing that PC is complete is to derive the properties of the basic truth tables. The truth tables provide an adequate test for a formula being a tautology. Thus, if our proof theory (rules of inference) can derive all of the basic truth tables, then we would have shown that we can derive every tautology. Can we show that we can only derive tautologies as theorems? Yes, on the assumption that our proof theory is consistent.
We derive a conditional for each row of each basic truth table. The antecedent of each conditional is a conjunction: if ‘P/Q’ is true in a row, we have ‘P/Q’ as a conjunct; if ‘P/Q’ is false in a row, we have ‘¬P/¬Q’ as a conjunct. The consequent of each conditional is a formula of the type whose truth table we are deriving. If the row renders the formula true, our consequent is the formula; if the row renders the formula false, our consequent is the negation of the formula.
(Lemmon doesn’t contain the proofs to follow.)
Derivation of the truth function of Negation:
(i) ├ ¬¬P → P (ii) ├ P → ¬¬P
1(1) ¬¬P A 1(1) P A
1(2) P 1DN 1(2) ¬¬P 1DN
(3) ¬¬P → P 1,2CP (3) P → ¬¬P 1,2CP
Derivation of the Truth Function of Conjunction
(i) ├ P & Q → P & Q (First row: T T T)
1(1) P & Q A
(2) P & Q → P & Q 1,1CP
(ii) ├ P & ¬Q → ¬(P & Q) (second row: T F F)
1 (1) P & ¬Q A
2 (2) P & Q A
2 (3) Q 2&E
1 (4) ¬Q 1&E
1,2(5) Q & ¬Q 3,4&I
1 (6) ¬(P & Q) 2,5RAA
(7) P & ¬Q → ¬(P & Q) 1,6CP
The derivations for the remaining two rows follow the same pattern as (ii)
(iii) ├ ¬P & Q → ¬(P & Q) (Third row: F F T)
(iv) ├ ¬P & ¬Q → ¬(P & Q) (Fourth row: F F F)
Q: Prove (iii) and (iv)
Derivation of the Truth Function of Disjunction
(i) ├ P & Q → P v Q (First row: T T T)
1(1) P & Q A
1(2) P 1&E
1(3) P V Q 2vI
(4) P & Q → P v Q 1,1CP
The derivations for the second and third rows follow the same pattern; the fourth row is more tricky.
(ii) ├ P & ¬Q → P v Q (Second row: T T F)
(iii) ├ ¬P & Q → P v Q (Third row: F T T)
(iv) ├ ¬P & ¬Q → ¬(P v Q) (Fourth row: F F F)
Q: Prove (ii)-(iv)
Derivation of the Truth Function of Material Implication
(i) ├ P & Q → P → Q (First row: T T T)
1 (1) P & Q A
2 (2) P A
1 (3) Q 1&E
1,2(4) P & Q 2,3&I
1,2(5) Q 4&E
1 (6) P → Q 2,5CP
(7) P & Q → P → Q 1,6CP
(ii) ├ ¬Q → ¬(P → Q) (Second row: T F F)
1 (1) P & ¬Q A
2 (2) P → Q A
1 (3) P 1&E
1,2(4) Q 2,3MPP
1 (5) ¬Q 1&E
1,2(6) Q & ¬Q 4,5&I
1 (7) ¬(P → Q) 2,6RAA
(8) P & ¬Q → ¬(P → Q) 1,7CP
The remaining two derivations are
(iii) ├ ¬P & Q → P → Q (Third row: F T T)
(iv) ├ ¬P & ¬Q → P → Q (Fourth row: F T F)
Q: Prove (iii)-(iv).
Derivation of the Truth Function of Material Equivalence
(i) ├ Q → P ↔ Q (First row: T T T)
1 (1) P & Q A
2 (2) P A
1 (3) Q 1&E
1,2 (4) P & Q 2,3&I
1,2 (5) Q 4&E
1 (6) P → Q 2,5CP
7 (7) Q A
1 (8) P 1&E
1,7 (9) P & Q 7,8&I
1,7 (10) P 9&E
1 (11) Q → P 7,10CP
1 (12) P → Q & Q → P 6,11&I
1 (13) P ↔ Q 12 ↔ df
(14) P & Q → P ↔ Q 1,13CP
(ii) ├ P & ¬Q → ¬(P ↔ Q) (Second row: T F F)
1 (1) P & ¬Q A
2 (2) P ↔ Q A
2 (3) P → Q & Q → P 2 ↔ df
2 (4) P → Q 3&E
1 (5) P 1&E
1,2(6) Q 4,5MPP
1 (7) ¬Q 1&E
1,2(8) Q & ¬Q 6,7&I
1 (9) ¬(P ↔ Q) 2,8RAA
(10) P & ¬Q → ¬(P ↔ Q) 1,9CP
The remaining two derivations, following the same pattern, are
(iii) ├ ¬P & Q → ¬(P ↔ Q) (Second row: F F T)
(iv) ├ ¬P & ¬Q → P ↔ Q (Second row: F T F)
Q: Prove (iii)-(iv).