Usually, programming a computer is kind of a
master and servant dynamic. You write a line
of code, and the machine just executes that line.
You give it this really rigid sequence of steps,
and it follows them blindly to the letter. Yeah,
that's the standard paradigm. But there is a
programming language that flips this relationship
entirely. You don't tell the machine what to
do step by step. You just tell it what is true.
you let it figure out the rest. Which is a huge
mental shift. Oh, absolutely. So today, for you,
the learner listening right now, we are diving
into a stack of sources detailing a language
that has been quietly powering artificial intelligence
and complex databases for, well... Over 50 years,
we're unpacking Prolog. It's quite a legacy.
It really is. And whether you are a seasoned
software engineer or just someone fascinated
by how machines think, this deep dive into our
sources is going to challenge your most basic
assumptions about problem solving. OK, let's
unpack this. To truly grasp what Prolog represents,
you really have to look at it as a bridge. A
bridge. Yeah. On one side, there is formal human
logic, like the way philosophers and mathematicians
describe the world using relations, variables,
absolute truths. Right, like formal proofs. Exactly.
And then on the other side, you have cold, hard
machine execution. Prologue was designed to be
the infrastructure connecting those two worlds.
It allows a user to compute complex answers by
simply querying a set of logical relations. The
sources show this language didn't just emerge
in a vacuum, though. It was essentially born
into a philosophical battleground in the 1970s.
Oh yeah, it was highly contested. The story starts
around 1972 at the University of Aix -Marseille
in France. Two researchers, Alain Colmerauer
and Philippe Roussel, developed this new system.
And the name itself is basically a dead giveaway
to its purpose. Right, it's an abbreviation for
the French programmation en logique. Programming
in logic. Exactly. And the academic foundations
beneath it were pretty dense. The language was
built on Robert Kowalski's procedural interpretation
of what are known as horn clauses. Okay, horn
clauses. That sounds intense. It's jargon. But
to strip that away, a horn clause is essentially
a specific, highly restricted type of logical
formula. Think of it as a strict if -then structure.
Like, if this happens, then that happens. Sort
of. It allows you to say, if condition A and
condition B are met, then conclusion C is definitively
true. And the motivation here was deeply practical.
How so? Well, these researchers wanted to reconcile
two competing ideas in computer science. On one
hand, using pure logic as a declarative way to
represent knowledge, and on the other, the procedural
step -by -step representation of knowledge that
was, you know, dominating North America at the
time. And that tension brings us to what the
sources describe as the transatlantic rivalry.
Reading through the historical context, the computer
science world was practically split in two. It
really was. European AI researchers fiercely
championed Prologue, while American AI researchers
were firmly entrenched in a different language
called Lisp. Yeah, the Lisp versus Prologue debate.
And these weren't polite academic disagreements
either. There were fierce, almost nationalistic
debates over which language was superior. Because
it was a debate about the very nature of intelligence,
really, and how to digitally model it. The American
approach with Lisp was highly functional and
flexible, focusing on list processing. But the
European approach with Prologue was basically
betting the entire house on formal logic as the
singular foundation of artificial intelligence.
To make this tangible for you listening, think
about it like cooking. Traditional procedural
programming, which is what most of the software
world still uses today with languages like C,
Python, or Java, is like giving a chef a hyper
-specific step -by -step recipe. Down to the
exact measurements. Right. Take two eggs, crack
them into a bowl, whisk for 10 seconds, turn
the stove to medium. You are micromanaging the
entire process. Trolog, being a declarative language,
operates on a completely different paradigm.
It's a totally different mindset. It is like
walking into the kitchen and just describing
the perfect omelet to the chef. An omelet is
made of eggs. It is fluffy. It is cooked. Yep.
You define the parameters of the truth, and then
you leave the kitchen. The chef figures out how
to make it happen. That analogy perfectly captures
the shifting control flow. You are defining the
relationships and the end goal, not the mechanics
of the execution. And if we connect this to the
bigger picture, this idea was so revolutionary
at the time that it sparked some massive, incredibly
ambitious technological gambles. Oh, like the
Japanese project. Exactly. The most famous one,
heavily detailed on our sources, was the Japanese
fifth -generation computer systems project. the
FGCS. This launched in the early 1980s. And they
went all in. They essentially bet the future
of their entire computing industry on logic programming.
They didn't just write software, they built their
entire operating system on a variant of Prolog
called kernel language. And their goal for this
hardware was just I mean, they were aiming for
up to 1 billion logical inferences per second,
a GLPS. Right, a GLPS. And keep in mind the technological
baseline of that era. In 1982, standard computers
were operating at around like 10 ,000 to 100
,000 logical inferences per second. So they were
trying to jump light years ahead. The Japanese
project was attempting to scale that metric up
by orders of magnitude. And they weren't just
writing better code to achieve this. They were
literally hardwiring Prolog's unique execution
model into the physical silicon architecture
of the machines. Designing physical hardware
specifically to process logical deductions is
such a wild leap. It makes me wonder how this
logic engine actually works under the hood. It's
actually surprisingly elegant. Yeah. The sources
break down the mechanics, and it starts with
a really simple foundation. In Prolog, everything
is based on a single data type called the term.
A term can be an atom. which is just a simple
lowercase name like red or apple. It can be a
number. It can be a variable which acts as a
capitalized placeholder like x or person. Or
it can be a compound term which nests these things
together to show relationships. Those terms are
the building blocks. And a Prolog program itself
doesn't look like normal code. There are no loops,
no print statements, no step -by -step functions.
None of the usual stuff. Right. It is really
just a database of facts and rules. A fact is
a simple, unconditional assertion of truth. So
you could write a fact into the system that simply
says Socrates is human. Just a bold, undeniable
statement loaded right into the database. Exactly.
Then you introduce rules. A rule dictates that
a certain conclusion is true only if a specific
set of other facts are true. Okay. Using our
previous example, you could write a rule, all
humans are mortal. Translated into Prolog syntax,
you are essentially telling the machine X is
mortal, I, F, X is human. So the machine now
has a database of universal truths and conditional
rules. And the user interacts with this by submitting
a query. Right, you ask it a question. Yeah,
after the computer. Like, is Socrates mortal?
The sources mention the engine attempts to prove
this by finding a refutation of the negated query
using a method called SLD resolution. Which is
a mouthful. Yeah, let me try to translate that
jargon. because it sounds like a legal defense
strategy or something. From what I gather, the
computer basically assumes your query is false
and then searches through its database of facts
to find a contradiction. That's spot on. It is
like a detective proving a suspect is guilty
by proving their alibi is physically impossible.
That is an excellent way to conceptualize SLD
resolution. Prologue assumes Socrates is not
mortal. It then looks at the rule X is mortal
if X is human. OK. It looks at the fact Socrates
is human. It realizes the assumption that Socrates
is not mortal creates a logical contradiction.
Therefore, the original query must be true. So
it proves it by exhausting the alternative. But
wait, if I just give the computer a massive sprawling
pile of thousands of facts and rules and it just
blindly searches for contradictions to answer
my query, what happens when it goes down the
wrong path? Now we're getting to the catch. Right.
If I ask a highly complex question with dozens
of variables, mathematically that sounds like
a nightmare for processing power. Surely, in
the real world, that relentless searching would
just bring a computer to its knees from the sheer
volume of possibilities. You've identified the
core mechanical challenge of the language. This
leads directly to Prolog's defining execution
strategy, which is called chronological backtracking.
Chronological backtracking? Yes. The system doesn't
guess randomly. It explores the options systematically,
mapping out a massive decision tree. Imagine
navigating a really complex maze. OK, so if I
am the prolog engine in this maze, and I hit
an intersection with three possible paths. I
create a choice point. Exactly. I decide to take
the left path, binding my variables to the facts
down that corridor. But let's say I hit a dead
end. The facts don't support the rule. Instead
of crashing or throwing an error, the engine
just stops. It stops and then it backtracks.
It undoes every single variable binding it made
since that last intersection. It literally walks
backward through the maze to the previous choice
point and then tries the middle path. If that
fails, it backtracks again and tries the right
path. That's relentless. It searches this massive
tree of possibilities dynamically moving backwards
and forwards until it either constructs a flawless
logical proof or exhausts every single conceivable
option and returns a definitive false. So it
will stubbornly try every combination until it
finds the truth. Conceptually that sounds beautiful.
It is pure, relentless logic. It is. But computationally,
backtracking sounds incredibly heavy. Which brings
us to the biggest historical criticisms of Prologue
outlined in our sources, and the somewhat controversial
compromises programmers had to make to get it
to function in the real world. Yeah, the purity
of the logic engine came with a severe cost.
Because it relied on this exhaustive search method,
software development Prologue historically suffered
a massive performance penalty compared to conventional,
procedural languages like C. just runs straight
down the compiled instructions, while Prolog
is busy wandering through a maze, checking every
single branch in dead -end. Precisely. If you
are executing a deterministic computation, meaning
there is only one correct path to the answer,
and you don't need the machine to explore multiple
alternatives, Prolog's built -in backtracking
mechanism is a colossal waste of computing resources.
It's doing too much work. Way too much. It spends
time setting up choice points and preparing to
backtrack for scenarios that will never actually
happen. So if the backtracking is the bottleneck,
I imagine programmers had to find a way to put
blinders on the engine. They needed a mechanism
to tell the system, hey, you found a good enough
path, stop looking at the other doors. They did,
and they called it the cut. The cut? The cut
is a specific operator, an exclamation point
in the syntax, that a programmer manually inserts
into the code. It explicitly commands the prolog
engine, once you make this choice, commit to
this path. Do not backtrack past this point.
Wow. Yeah, it forcibly introduces a deterministic
path into a non -deterministic engine. The irony
there is thick. I mean, you come to a language
like prologue for the absolute purity of declarative
logic. You just want to describe the problem
to the chef and let them work. Right. But to
make the program run fast enough so the computer
doesn't freeze, you have to dirty your hands
with procedural hacks. You have to step back
into the kitchen and start micromanaging the
search tree, manually locking doors in the maze.
It is a profound philosophical compromise. The
sources explicitly note that relying on cuts
destroys one of Prologue's main attractions.
It's purely declarative nature. Because now you're
giving it steps again. Exactly. Once you start
aggressively inserting cuts, you have to read
the program procedurally to understand what it
is doing. You have to trace the execution steps
in your head rather than simply reading the logical
truths. That's a shame. Furthermore, you lose
the ability to run programs backwards and forwards,
which is arguably one of the most magical properties
of pure logic. programming. Let's expand on that
magic because the concept of running code backwards
is entirely foreign to someone used to standard
programming. Oh, it's fascinating. In Pure Prolog,
a single logical relationship can be queried
in multiple directions. Imagine you write a standard
rule for concatenating or joining two lists together.
In a procedural language, you give the function
two lists, and it spits out the combined list.
In pure Prolog, you can do that, but you can
also do the reverse. Wait, really? Yeah. You
can give the engine the combined list, leave
the two input lists as blank variables, and ask
the engine to find all the possible ways to split
that large list back into two smaller ones. That
is amazing. The exact same block of code computes
both forwards and backwards. It just evaluates
the logical relationship from different starting
points. But the moment you add a procedural cut
to optimize the performance, that bi -directional
flexibility is permanently broken. So you trade
mathematical beauty for execution speed. And
the sources point out this wasn't the only hurdle
for the language. Building massive enterprise
-level applications, what they call programming
in the large, was incredibly difficult due to
the severe module fragmentation. Right. Yes.
For a very long time, the creators of major Prolog
compilers couldn't agree on a standardized module
system. And a module system is basically how
you break a massive software program into smaller,
manageable, isolated pieces that can talk to
each other safely, right? Correct. Without a
standard, code written for one Prolog system
often just wouldn't work on another. Reading
the timeline in our sources is almost comical.
The ISO standard for the core Prolog language
was published in 1995. But part two, which standardized
the module system, didn't arrive until the year
2000. It took forever. And part three of the
ISO standard, which covers definite clause grammars,
a crucial tool for parsing language, that was
just published in June 2025. I know, it's unbelievable.
It took the international community over 50 years
to standardize that specific part of the language.
Looking at the performance penalties, the procedural
hacks, and a half century of standardization
delays, it begs the question, Did Prolog just
fail? Did the procedural languages like C and
Java completely win the war? Prolog didn't fail.
It evolved to survive. How so? The academic and
developer community recognized the severe processing
bottlenecks associated with backtracking, and
they developed brilliant algorithmic solutions.
One of the most significant breakthroughs was
a technique called tabling, which is a form of
memoization. Memo -ization, that's essentially
the computer taking notes as it works, correct?
Exactly. It is a space -time trade -off. Systems
like XSB and modern SWI Prolog implemented tabling
to save the logic engine from endlessly repeating
its own work. OK. As Prolog evaluates a complex
query and navigates the maze, it stores its intermediate
findings in memory in a literal table. If it
wanders down a path and encounters the exact
same subgoal it has already solved, it doesn't
recalculate it. It just looks up the answer in
the table. So you sacrifice some memory space
to drastically cut down the execution time. That
sounds like a standard optimization, but how
does it save the system from crashing? Because
it solves the fatal flaw of infinite recursive
loops. Imagine you are building a family tree
application in Prolog, and you ask the engine
to find all of someone's ancestors. In a pure
depth -first search, if the data has a circular
loop, say, two distant cousins married, creating
a loop in the lineage, the Prolog engine will
get trapped in that circle. It'll just keep backtracking
and stepping forward infinitely until the computer
runs out of memory and crashes. Oh, I see. Tabling
breaks the loop. The engine checks the table,
sees it has already evaluated that specific person,
realizes it is in a cycle, and forces the search
down a different path. It makes the language
vastly more robust for complex, messy reasoning
tasks. Tabling saves it from endless loops. And
that ability to handle massive, complex webs
of logic without crashing is exactly why Prologue
didn't die out, but instead became a secret weapon
for modern tech giants. It definitely found its
specific uses. Despite the academic debates and
the reliance on cuts, Prologue found incredibly
specific high -stakes niches where traditional
procedural languages completely stumbled. Let's
look at where this 1970s logic engine is hiding
in plain sight today. Prologue's unique architecture
makes it perfectly suited for domains that require
complex rule evaluation, namely, expert systems,
theorem proving, and natural language processing.
The most prominent example in our sources is
IBM Watson. When Watson went on the game show
Jeopardy and systematically crushed the human
champions, Prologue was functioning as a critical
component of its cognitive architecture. The
developers at IBM needed a language capable of
expressing highly complex pattern matching rules
over natural language parse trees. And to break
down why a parse tree is basically a logic puzzle,
think of a sentence like the quick brown fox.
Procedural code struggles to understand the contextual
relationship between those arbitrary words. Just
too rigid for that. Exactly. But if you mathematically
map that sentence into a tree of grammatical
rules, where a noun definitively connects to
an adjective which modifies a verb, suddenly
messy human language becomes a strict logic equation.
And Prologue was specifically designed to instantly
hunt through massive trees of logical rules.
Right. That meant it could parse jeopardy clues,
identify the subject, and evaluate the grammatical
relationships faster than any other language
IBM experimented with. What's fascinating here
is the IBM engineers explicitly stated that Prologue
was the ideal choice due to its simplicity and
expressiveness for that exact task. It allowed
them to write clean, declarative rules for English
grammar rather than thousands of lines of procedural
code trying to anticipate every possible sentence
structure. That's incredible. But it isn't solely
utilized by supercomputers. We see it in modern
open source infrastructure as well. Consider
TerminusDB. Oh, right. It's an open source graph
database designed for collaboratively building
massive knowledge graphs. And the entire system
is implemented in Prolog. A knowledge graph is...
at its core, a massive web of connected facts.
It links people, places, concepts. It makes perfect
sense. I mean, what better tool to query and
navigate a sprawling database of logical relations
than a programming language built entirely around
logical relations? Exactly. It's the perfect
fit. Here's where it gets really interesting,
though. Buried deep in the history section of
our sources is my absolute favorite application.
We've talked about IBM Watson parsing Jeopardy!
clues, but we also have to talk about Sega. The
video game company. Yes. In 1986, Sega released
the Sega AI computer exclusively for the Japanese
market, and the operating system utilized Prolog.
They utilized it specifically for reading Japanese
natural language inputs via a specialized touchpad.
It is wild to trace that lineage. A theoretical
language born in the university in France in
1972, built entirely on the philosophy of formal
logic, ends up parsing Japanese language inputs
on a Sega console in the 1980s, and then helps
an IBM supercomputer win Jeopardy! decades later.
It really underscores the deep versatility of
the language. It highlights a fundamental truth
about computer science, really. Prolog is Turing
complete. Meaning what, exactly? That means anything
you can compute with C, Java, or Python, you
can technically compute with Prolog. It simply
approaches the computation from a completely
different paradigm. It views computation as a
sequence of logical state transitions, navigating
a landscape of truths, rather than a linear sequence
of hardware instructions. fundamentally shifts
how you approach a problem. And that brings us
to the end of our sources today. Wrapping up
this deep dive, it's clear that Prolog isn't
just a relic of the AI Cold War, nor is it just
an academic curiosity. Far from it. It is a living
testament to the idea that sometimes the best
way to solve an incredibly complex problem isn't
to meticulously tell the computer how to do every
little thing. Sometimes the most powerful approach
is to just teach the machine what is true, step
out of the way, and let it find the path through
the maze itself. For you listening, engaging
with the concepts behind a language like Prologue,
even if you never write a single line of code,
expands your critical thinking. It trains you
to separate the definition of a problem from
the execution of its solution. In our modern,
highly procedural world where we are constantly
focused on the steps to achieve something, focusing
purely on the truth of the system is a rare and
valuable perspective. It rewires your brain to
look at the destination before plotting the steps.
It does. And analyzing this history leaves us
with a lingering question about the future. We
discussed how the Japanese fifth -generation
project failed in the 1980s because they tried
to build physical hardware specifically optimized
for prologue's logic and standard general -purpose
chips eventually outpaced them. Right, the general
-purpose chips went out. But today, the computing
demands of modern AI are hitting physical limits.
Researchers are now developing neuromorphic chips
hardware that physically mimics the synapses
of a biological brain rather than relying on
binary math. This raises a fascinating possibility.
As we hit the ceiling of general purpose chips,
could the future of artificial intelligence be
a return to that 1980s dream? Could the next
major leap in AI involve taking prologue's philosophy
of pure declarative logic and hardwiring it directly
into these new artificial digital brains? Hardwiring
a digital brain not to calculate math, but to
instantly deduce truth. Taking the ultimate declarative
language and fusing it with neuromorphic hardware.
That is a completely new frontier to consider.
Thank you so much for joining us on this deep
dive into the source material. Keep asking questions,
keep looking beyond the procedural steps, and
we'll catch you next time.