By: Johannes Korbmacher
Logical conditionals
In our discussion of Boolean algebra so far, we’ve avoided dealing with conditionals: “if …, then …"statements which we formalize as $$A\to B.$$ In this chapter, you’ll learn about how conditionals are defined and used in propositional logic.
First, you’ll learn about the truthfunctional way of thinking about ifthen statements, the socalled
Then, we move on to inference patterns that are important for AI applications, in particular:
We conclude with an outlook and remarks on nonmaterial conditionals.
The material conditional
How should we treat statements like $$\mathsf{RAIN}\to \mathsf{WET}$$ in Boolean algebra? This, it turns out, is not an easy question.
The easy part is this:

If $\nu(\mathsf{RAIN})=1$ and $\nu(\mathsf{WET})=1$, we want $\nu(\mathsf{RAIN}\to \mathsf{WET})=1$.

If $\nu(\mathsf{RAIN})=1$ and $\nu(\mathsf{WET})=0$, we want, $\nu(\mathsf{RAIN}\to \mathsf{WET})=0$.
But this only gives us a partial table for the truthfunction $\Rightarrow:\Set{0,1}^2\to \Set{0,1}$ which interprets $\to$:
So, what do we do if $\nu(\mathsf{RAIN})=0$? It turns out that we want to say $\nu(\mathsf{RAIN}\to \mathsf{WET})=1$ regardless of the truthvalue of $\mathsf{WET}$.
One of the reasons for this is the connection between valid inference and the conditional. To understand, we need the concept of a tautology or logical truth.
In Boolean algebra, a tautology is any formula that is true under all valuations. Consider, for example, $\mathsf{RAIN}\lor\mathsf{SUN}$—it’s either raining or not raining. No matter the value $\nu(\mathsf{RAIN})$ of $\mathsf{RAIN}$, we have $\nu(\mathsf{RAIN}\lor\mathsf{SUN})=1$:
 If $\nu(\mathsf{RAIN})=1$, then $\nu(\mathsf{RAIN}\lor\mathsf{SUN})=1+0=1$
 If $\nu(\mathsf{RAIN})=0$, then $\nu(\mathsf{RAIN}\lor\mathsf{SUN})=0+1=1$
If a formula $A$, like $\mathsf{RAIN}\lor\mathsf{SUN}$, is a logical truth we write $\vDash A$ to indicate this. So, what we’ve just shown is that $$\vDash\mathsf{RAIN}\lor\mathsf{SUN}.$$
Now the thing is that if we say that that $\nu(A\to B)=1$ whenever $\nu(A)=0$, we get the following beautiful connection between validity and the conditional: $$P_1, P_2, \dots\vDash C\Leftrightarrow\ \vDash (P_1\land P_2\land \dots)\to C$$ In words, an inference is valid if and only if the conditional formed from taking the conjunction of the premises as the ifpart and the conclusion as the thenpart is logically valid.
The truthfunction that’s given by the following table is known as the material conditional:
Our first motivation for treating $\to$ as a material conditional is the above connection between valid inference and the conditional, which is also known as the deduction theorem. One interesting implication of the deduction theorem is that it gives us an interesting variant of the truthtable method.
The idea is that in order to test whether $P_1,P_2, \dots\vDash C$ we can also just test whether the formula $(P_1\land P_2\land \dots)\to C$ gets value 1 under each valuation. Take our example from the previous chapter again: $$(\mathsf{RAIN}\lor \mathsf{BIKE}),\neg\mathsf{RAIN}\vDash\mathsf{BIKE}$$ We can test this inference by doing the truthtable for the following formula: $$(\mathsf{RAIN}\lor \mathsf{BIKE})\land\neg\mathsf{RAIN}\to\mathsf{BIKE}$$
Since the formula in question receives value 1 under all valuations, it is a logical truth. This means that the inference in question is valid. The deduction theorem has many further logical implications, but we won’t focus on those here.
An additional justification comes from the foundations of programming, which is a foundational topic for AI, as we’ve seen in Chapter 3. Formal languages . Consider the following pseudo code:
for n in range(1,10)
if n is divisible by 2:
print "Even!"
This program loops through all the numbers from 0 to 10 and for the even ones (2,4,6,8,10) it prints even. How should we think about happens “under the hood” when the program evaluates the following expression?
if 1 is divisible by 2:
print "Even!"
While we don’t want the computer to print “Even!”, we also don’t want to think of the whole expression as false or erroneous.
Next, you’ll learn about inference patterns involving conditionals which are important in AI applications.
Forward chaining
The forward chaining method is, essentially, a series of applications of the reasoning pattern known in logical theory as modus ponens (MP). It’s an important method for automated theorem proving, which deals with automating valid inference.
MP is following inference pattern: $$A,A\to B\vDash B$$ So, for example, $$\mathsf{RAIN},\mathsf{RAIN}\to\mathsf{WET}\vDash \mathsf{WET}$$ This pattern is perhaps the most paradigmatic logical reasoning pattern out there.
There are different ways in which we can justify the pattern, but let’s use a truthtable for a particularly comprehensive explanation:
Having established that MP works, let’s talk about forward chaining. The idea of forward chaining is to repeatedly use MP
Let’s look at a toy example to illustrate. We’re at home it’s rainy and not warm (logician for: cold) outside. We’re wondering what to do. Our setup is as follows:

We have a knowledge base that contains a series of conditional rules:
 $\mathsf{SUN}\to \mathsf{BIKE}$
 $\mathsf{RAIN}\to \mathsf{CAR}$
 $\mathsf{WARM}\to \mathsf{SHIRT}$
 $\mathsf{COLD}\to \mathsf{COAT}$
 $\mathsf{BIKE}\to \mathsf{HELMET}$
 $\mathsf{CAR}\to \mathsf{KEYS}$

Additionally, we have some known facts:
 $\mathsf{RAIN}$
 $\mathsf{COLD}$
The forward chaining method checks through our knowledge base to see whether the antecedents (ifpart) of any of our conditional rules contains a known fact.
We find for example, the rule:
 $\mathsf{RAIN}\to \mathsf{CAR}$
Since $\mathsf{RAIN}$ is among our known facts, we can reason as follows:
$$\mathsf{RAIN},\mathsf{RAIN}\to \mathsf{CAR}\vDash\mathsf{CAR}$$
As a result, we add $\mathsf{CAR}$ to our known facts. This makes the following rule applicable:
 $\mathsf{CAR}\to \mathsf{KEYS}$
Again, using MPstyle reasoning, we proceed:
$$\mathsf{CAR},\mathsf{CAR}\to \mathsf{KEYS}\vDash\mathsf{KEYS}$$
We thus add $\mathsf{KEYS}$ to our known facts. In a similar fashion, we add $\mathsf{COAT}$ to our known facts arriving at the final known facts:
 $\mathsf{RAIN}$
 $\mathsf{COLD}$
 $\mathsf{CAR}$
 $\mathsf{KEYS}$
 $\mathsf{COAT}$
This is all we can get “out” of our known facts using our knowledge base. AI researchers typically think of forwardchaining as a data driven inference method: we start with some known facts and try to see what else we can derive from the known facts. We don’t have goal in mind. This is different with the following method.
Backward chaining
The backward chaining method is, as the name suggests, forward chaining “the other way around”. Instead of starting from the data and extracting new information from it, backward chaining starts with a goal, a question we’re trying to answer. This makes backward chaining a goal directed method.
We can explain how backward chaining works using our same example as above:

Our knowledge base contains:
 $\mathsf{SUN}\to \mathsf{BIKE}$
 $\mathsf{RAIN}\to \mathsf{CAR}$
 $\mathsf{WARM}\to \mathsf{SHIRT}$
 $\mathsf{COLD}\to \mathsf{COAT}$
 $\mathsf{BIKE}\to \mathsf{HELMET}$
 $\mathsf{CAR}\to \mathsf{KEYS}$

Our known facts are:
 $\mathsf{RAIN}$
 $\mathsf{COLD}$
But instead of just wondering what we can derive, we’re specifically asking the question whether we should take our helmet. That is, we have a set of goals that contains: $$\mathsf{HELMET}$$
With this specific question in mind, we go through the rules and see whether there’s a rule that would allow us to infer $\mathsf{HELMET}$. We find:
 $\mathsf{BIKE}\to \mathsf{HELMET}$
If we would know that $\mathsf{BIKE}$, we could infer $\mathsf{HELMET}$. So we temporarily add $\mathsf{BIKE}$ to our goals. Again, we check whether we find a corresponding rule:
 $\mathsf{SUN}\to \mathsf{BIKE}$
We now add $\mathsf{SUN}$ to our temporary goals. But at this point our search terminates. We can’t find any rule that gives us $\mathsf{SUN}$. We couldn’t reach our goals
Now suppose, instead, we ask whether we should take our keys and add $\mathsf{KEYS}$ to our goals. We can then backwards chain the rules:
 $\mathsf{RAIN}\to \mathsf{CAR}$
 $\mathsf{CAR}\to \mathsf{KEYS}$
We arrive at a goal that’s among our known facts, viz. $\mathsf{RAIN}$. This means that using a series of MP’s and rules 2 and 6, we can derive our ultimate goal: $\mathsf{KEYS}$. Our KB has settled the question.
But wait a moment, have we established yet that we shouldn’t take a helmet? It turns out that this is not such an easy question. We’ve shown that our knowledge base doesn’t settle the question. But there a lot of questions it doesn’t settle, should we treat them all as false?
The socalled closedworld assumption (CWA) in AI says yes. In short, the CWA tells us to treat nonderivable statements as false, meaning we’d conclude $\neg\mathsf{HELMET}$ from the fact that we can’t derive $\mathsf{HELMET}$. The CWA plays a fundamental role in knowledge representation.
Conditionals in programming
Before we move to the limitations of the logical conditional, let’s briefly talk about conditionals in programming.
If you’ve ever written a computer program, you’ve very likely written something like this:
if CONDITION:
EFFECT1
elif:
EFFECT2
else:
EFFECT3
For example, a solution to the famous fizz buzz programming exercise looks something like this:
for n in range(1,101):
if n is divisible by 3 and n is divisible by 5:
print("FizzBuzz")
elif n is divisible by 3:
print("Fizz")
elif n is divisble by 5:
print("Buzz")
else:
print(n)
This program loops through all the numbers from 1 to 100 and tests for each number whether it’s divisible by both 3 and 5, in which case it prints “FizzBuzz”, if that’s not the case but the number is divisible by 3, the program prints “Fizz”, if that’s also not the case but the number is divisible by 5, the program prints “Buzz”, and if all else fails, it prints the number.
This results in a sequence that begins like this:
$$1,2,Fizz,4,Buzz,Fizz,7,8,Fizz,Buzz,\dots$$
The material conditional can help us understand what’s going on here. Each of the conditions is a Boolean condition: it evaluates either to true false. The “ifthenelse” statements can therefore be modelled by the following material conditionals:
 $\mathsf{DIV\_3}\land \mathsf{DIV\_5}\to \mathsf{PRINT\_FIZZBUZZ}$
 $\mathsf{DIV\_3}\land \neg\mathsf{DIV\_5}\to \mathsf{PRINT\_FIZZ}$
 $\neg\mathsf{DIV\_3}\land \mathsf{DIV\_5}\to \mathsf{PRINT\_BUZZ}$
 $\neg\mathsf{DIV\_3}\land \neg\mathsf{DIV\_5}\to \mathsf{PRINT\_NUMBER}$
These are, essentially, a knowledge base. If we change the known facts corresponding to the actual divisibility facts of the number, we can derive what to print using standard inference techniques, such as forward/backward chaining.
Conditionals are a fundamental reason for the expressive power of programming languages and at their very heart lies the behavior of the material conditional.
Nonmaterial conditionals
Before we conclude the chapter, we should talk about the limitations of the logical conditional.
The most important limitation is that $\Rightarrow$ is not a good model of many uses of the phrase “if …, then …” in natural language. For example, typically, people think that conditionals like “If the moon is made of cheese, then pigs can fly” are false. The reason is, arguably, that the material of the moon has nothing to do with the abilities of pigs. But if we treat ifthen as the truthfunction $\Rightarrow$, since the moon is not made of green cheese, the conditional would need to get the value $0\Rightarrow 0=1$.
This and similar phenomena have lead to the development of various conditional logics, which aim to provide a more adequate formalization of various ifthen clauses. Of these logics, especially the logic of counterfactual conditionals, which are conditionals that talk about what would have been, play an important role in the representation of causal knowledge and in XAI.
Further readings
You can find a more detailed discussion of algorithmic implementations of forward and backward chaining in Ch. 7.5 of Russel and Norvig’s “Artificial Intelligence. A Modern Approach.” (see the detailed reference in Chapter 1. Logic and AI ).
Notes:
Last edited: 24/09/2024