a complex thinker
Daphne Koller discusses probabilistic relational modeling,
artificial intelligence, and her new work with biologists.
IN APRIL, DAPHNE Koller, a professor
of computer science at Stanford University, received the first-ever ACM-Infosys
Foundation Award in Computing Sciences for her groundbreaking approach
to artificial intelligence.
How did your interest in relational
logic and probability first develop?
What really sparked my interest
was when I went to UC-Berkeley for
my post-doc, and I realized how useful
probabilistic modeling methods are—
techniques like Bayesian networks—
but, on the other hand, how brittle
[Probability is] a language that’s
very restricted in its expressive power
because it can only refer to specific,
At Berkeley, I remember speaking
with a graduate student who was building a model for vehicle traffic on a freeway. He had this wonderful model that
was getting great results, but it only
applied to a three-lane freeway. If he
wanted to add another lane, it would
take him two weeks to do the model.
And that’s where logic entered the
The language of logic allows you
to come up with much more general
rules for describing the properties of
objects and their interactions with
each other. It’s a good way of extending the power of probability to something that’s more expressive and
That would be the synthesis known
as probabilistic relational modeling.
What we did is take this language of
objects and relations and add the ability to make probabilistic statements,
which enables you to represent probability distributions over networks of
It also enables you to create mod-
els of considerably more complex sys-
Yes, and that’s been a focus of my
work ever since. The world is very complex: people interact with other people
as well as with objects and places. If
you want to describe what’s going on,
you have to think about networks of
things that interact with one another.
We’ve found that by opening the lens
a little wider and thinking not just
about a single object but about everything to which it’s tied, you can reach
much more informed conclusions.
Which was an insight you brought
to the field of artificial intelligence…
Well, I wasn’t the only one involved.
There had been two almost opposing
threads of work in artificial intelligence: there were the traditional AI
folks, who grew up on the idea of logic
as the most expressive language for
representing the complexities of our
world. On the other side were people
who came in from the cognitive reasoning and machine learning side,
who said, “Look, the world is noisy
and messy, and we need to somehow
deal with the fact that we don’t know
things with certainty.” And they were
both right, and they both had important points to make, and that’s why
they kept arguing with each other.
How did probabilistic relational
modeling help settle the dispute?
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