If you ask someone on the street for directions
to the nearest coffee shop, they don't usually
look at you and say, well, put your left foot
forward, shift your weight, now flex your knee
20 degrees. Right, that would be terrifying.
And, you know, completely exhausting. They just
say, take a left. It's brick build. Exactly.
They give you the layout and they just trust
your brain to figure out the actual. physical
mechanics of getting there. Yeah, they don't
micromanage your joints. But for decades, I mean,
we forced computers to do exactly that. We communicated
with them using that exhausting foot -by -foot
method. But today, we are looking at a completely
different paradigm. So welcome to another deep
dive specially tailored for you, the learner.
Today, we're skipping the dense academic textbooks
and jumping straight into a really fascinating
source, a comprehensive Wikipedia article on
logic programming. It's a great piece. It really
is. And the mission of this deep dive is to extract
the absolute best nuggets from this text. We're
going to demystify what logic programming is,
explore this dramatic history behind it, and
uncover how it actually mimics human thought
and human law. Yeah, and the goal today is really
to look at the why behind the code, not just
how it works, but why this approach even exists
and why it matters. OK, let's unpack this. Yeah.
Because before we can understand why this sparked
an actual academic war, we need to know how it
actually works. Right. So the fundamental shift
here is moving from procedural to declarative
programming. Declarative, meaning? Meaning you
don't give the computer step -by -step instructions.
That foot -by -foot method, that's procedural.
You're dictating the control flow. But logic
programming is declarative. You just give the
computer a set of sentences in logical form,
facts, and rules, and you just let it figure
out the solution. So you're just describing the
problem, basically. Exactly. You describe what
the problem is, not how to solve it. If you look
at the structure of a rule in this system, it
looks sort of backwards. You write the conclusion
first. So let's say conclusion A. Then you write
a colon and a dash, followed by the conditions.
Let's call them B and C. Wait, a colon and a
dash, like a sideways smiley face. Yeah, a lot
of people see that. But think of that colon and
dash as the computer's way of saying the word
I F. So it translates to A is true, I F, B and
C are true. Oh, I see. Yeah. And A is the head
of the rule, the goal. B and C are the body.
And these are known as horn clauses. Horn clauses.
Right. Which is just a formal way to describe
rules that have one clear, definite conclusion.
And if you just have A with no conditions, that's
just a fact. Then you just ask the system queries,
and it does a deduction. OK, let me try to make
this concrete using the family tree example from
the source text. Sure, go ahead. So if I tell
the computer, Charles is William's parent. That's
a fact, right? Yep, a base fact. And then I give
it a rule, like a grandparent is a parent of
a parent. In a normal procedural language, I
have to write a loop to search the whole database,
hold it in memory, cross -reference everything.
Which takes a lot of manual coding, yeah. Right.
But here, I don't need to write a loop. I just
ask the query, who is William's grandparent?
And the computer does the logical heavy lifting.
It just connects the dots. That is the exact
elegance of it. It searches its database of horn
clauses and figures it out. So if it's that elegant...
I mean, why wasn't this the only way we programmed
computers from the start? Because it caused a
massive academic rivalry. Really? Oh, yeah. In
the late 1960s and early 1970s, the AI community
was completely split. On one side, you had the
declarative camp. Which our sources say was mostly
Stanford and Edinburgh, right? Right. With guys
like John McCarthy, Cordell Green, Robert Kowalski.
Exactly. They believed knowledge should be represented
logically. But on the other side, you had the
procedural camp, mainly at MIT. Ah, MIT. Marvin
Minsky and Seymour Paper. Right, and they wanted
step -by -step control. They looked at pure logic
and thought it was way too inefficient for real
-world computing. Wait, so you're telling me
that one of the most important programming languages
in AI history, Prologue, basically emerged because
two rival academic factions couldn't agree on
whether to give computers rules or instructions.
That is exactly what happened. The declarative
camp wanted mathematical purity, and the procedural
camp wanted control, using things like backtracking,
where the computer tries a path and rewinds if
it hits a dead end. Like a maze. Yeah, exactly
like a maze. But the two sides finally reconciled,
thanks to Elaine Kolmerauer and Robert Kowalski.
This was in Marseille, right? Around 1971 or
72? Yes. Holmerhauer was working on a French
question answering system. He invited Kowalski
over, and together they realized you could actually
execute pure logic as a controlled series of
SNPs. They combined the two philosophies. Wow.
And that reconciliation led directly to the creation
of Prolog in 1972. Which stands for programming
in logic. You got it. And this whole reconciliation
led to a really fundamental realization about
problem solving, which Kowalski actually captured
in an equation. Algorithm equals logic plus control.
The equation of the century, some call it. It
means any algorithm has two parts. The logic
is the knowledge, the rules and facts, and the
control is the strategy the machine uses to process
it. So logic is knowing the recipe for a cake.
And control is whether you start by gathering
the ingredients, which the text calls forward
reasoning, or visualizing the finished cake and
working backward to find out what you need, which
is backward reasoning. That's a perfect analogy.
Backward reasoning is top -down, like Prologue
uses. It reduces goals down to base facts. Forward
reasoning is bottom -up, deriving all possible
facts from the base. Does it really make a big
difference which one you use? Oh, absolutely.
The text uses the Fibonacci sequence to show
this. Oh, right, where each number is the sum
of the two before it. Yeah. If you use backward
reasoning to find a large Fibonacci number, the
computer starts at the top and says, OK, I need
n minus 1 and n minus 2. And then for n minus
1, it needs n minus 2 and n minus 3. So it's
calculating n minus 2 twice. Exactly. It redundantly
calculates the same numbers over and over. Its
exponential complexity, the computer will just
crash. Yikes. But if you just change the control
to forward reasoning, keeping the exact same
logic, it starts at 0 and 1 and just adds them
up linearly. It takes almost no time at all.
Wow, so no recomputation. It just walks up the
sequence. But real life isn't a neat math problem
with perfect facts. How does it handle missing
information? That is where we get to a concept
called negation as failure. Negation as failure.
Right. In this paradigm, if the computer cannot
prove a positive condition is true, it just assumes
it's false. Here's where it gets really interesting.
Doesn't this mean the computer is just jumping
to conclusions based on ignorance? Is that safe?
Well, it makes it a non -monotonic logic, meaning
conclusions can be withdrawn when new information
appears. It's highly adaptable. Can we use the
Tom the thief example from the article? Let's
do it. So say we have a rule. A person is punished
if they're a thief unless they are a minor. OK.
We tell the system, Tom is a thief. It checks
if he's a minor, finds no evidence of it, so
it assumes he's not. Verdict. Tom is punished.
So we jump to that conclusion, but then what
if we learn he's 15? We input that fact. The
system immediately withdraws the punishment conclusion
because now the minor condition is met. The new
verdict is Tom is rehabilitated. And if we learn
he's a violent offender. We add a rule that violence
defeats rehabilitation. The system reevaluates,
the exception triggers, and the punishment is
reinstated. It's just arguing with itself. It's
bouncing between rules and exceptions. Exactly.
And that exact tendency to jump to conclusions
and revise them later isn't a flaw. It actually
perfectly aligns with how the human brain works.
Really? Yeah. Cognitive scientists like Paul
Thagard and Keith Stenning, who are cited in
The Source, argue that this is psychologically
plausible. Because humans use defaults and exceptions
all the time. Right. If I say bird, you assume
it flies. If I say penguin, you pull up the exception.
You don't crash. And Stenning even says this
kind of backward reasoning has an appealing neural
implementation. That makes total sense. So instead
of writing endless if -then loops to cover every
single edge case, we are basically giving the
computer a law degree. like feeding it the Civil
Code and letting it argue with itself to find
the verdict. That is quite literally what they
did. In the 1980s, they digitized the British
Nationality Act using logic programming. Oh,
wow. And today, Japan has a modern system called
Proleg. Right, the prologue -based legal reasoning
system. Yes. And it uses roughly 2 ,500 rules
from the Japanese Civil Code. It models human
legal arguments perfectly. But wait, if it's
this good... Why did it kind of fade out? The
text talks about the Japanese fifth -generation
computer systems project, the FGCS, in the 80s.
Yeah, they poured massive money into trying to
use concurrent logic programming for parallel
AI, meaning multiple processors working at the
same time. But it failed. It did. It turns out,
forcing logic to commit to choices in parallel
hardware ruined the mathematical purity. It introduced
timing bugs. And meanwhile, regular, general
-purpose computers just got incredibly fast.
So standard procedural programming just outpaced
it in raw speed. Exactly. It couldn't compete.
But the paradigm didn't die, right? Because the
source text goes into how it splintered into
these modern flavors. Like, uh... Datalog. Right.
Datalog is huge for databases. Standard relational
databases like SQL choke on recursive queries
like finding an ancestor 15 generations back.
Because it doesn't know how many times to loop.
Exactly. But Datalog handles recursive rules
natively and efficiently. And then there's ASP,
answer set programming. The text describes it
as a generate and pest method. Yes. ASP is amazing
for constraint problems. Take map coloring. You
need to color a map red, green, or blue, but
no touching countries can be the same color.
Procedurally, that's a nightmare of checking
every single border. But in ASP, you just generate
every possible color combination for the universe
and then apply a constraint test to filter out
any maps where touching countries share a color.
Generate the universe, then destroy the ones
that break the rules. That is wild. It's incredible.
powerful. But the last one mentioned is inductive
logic programming or ILP, and this one flips
the script completely. It does. ILP is a form
of machine learning. Instead of giving it the
rules, you give it the facts and the answers,
positive and negative examples, and you make
the machine learn the rules. If inductive logic
programming is about inducing the general rules
from a bunch of positive and negative examples,
isn't that essentially exactly how you or I learned
what a dog or a car was? when we were toddlers.
It's exactly the same mechanism. You show a kid
a dog, say dog. Show a cat, say not dog. Eventually,
they induce the rule of what a dog is. That's
fascinating. And Stuart Russell, a major AI researcher,
notes in the text that this kind of concept invention
is key to reaching true human -level AI. Wow.
We have cut so much ground today. from the simple
idea of just giving computers declarative rules,
to the academic turf wars at MIT and Stanford,
all the way to modeling the Japanese civil code
and mimicking toddler cognition. So what does
this all mean? What does this leave us today?
Well, it leaves us with a really important thought
to mull over. Right now, our modern AI, like
massive neural networks, they're incredibly powerful,
but they're completely unpredictable black boxes.
Right, they just... guess based on statistics.
Exactly. So if logic programming so closely mirrors
human cognitive rules, defaults, and the legal
system, might the future of AI require us to
combine our modern neural networks with this
classic rule -based logic programming? Just to
keep the AI's reasoning explainable. Explainable
and actually aligned with human law. blending
the statistical guessing with an actual logical
law degree. Love that. Well, be you the learner.
Thank you so much for joining us on this deep
dive. Stay curious, keep questioning how things
work, and we'll see you next time.