Stuart Russell -- Research areas
I am interested in building systems that can act intelligently in the real
world. To this end, I work (with various students, postdocs, and
collaborators) on a broad spectrum of topics in AI. These can
be grouped under the following headings:
- Foundations: Rationality and Intelligence
Provably intelligent systems based on the mathematical framework of
bounded optimality. Topics include quasioptimal control of
search and composition of real-time systems.
- The long-term future of
Will the capabilities of AI systems exceed those of
humans? If they do, what does that imply for the future of humanity?
Can we couple the development of AI with guarantees that AI systems
will benefit humanity rather than destroying us? These are serious
questions that lead to deep research issues requiring our attention,
in much the same way as the containment of fusion reactions has become
a major topic in nuclear fusion research.
- Learning probability models
Topics include learning static and dynamic Bayesian networks and
related models and learning with prior knowledge. Applications include
speech recognition, computational biology, and human driver modelling.
- First-order, open-universe
First-order languages, such as
first-order logic, assume worlds composed of objects and relations.
Whereas closed-univserse languages such as Prolog and database systems
assume a fixed, known universe of objects, each uniquely named,
open-universe languages allow for the possibility of unknown objects
and identity uncertainty. As such, they are suitable for application
domains such as computer vision, natural language understanding, web
information extraction, computer security, and multitarget tracking,
where the set of objwcts is not given in advance. An open-universe
probability model or OUPM specifies a probability distribution
over possible worlds under the open-universe assumption. The BLOG
language provides a formal syntax, semantics, and inference capability
- State estimation
State estimation (also known as filtering, tracking, belief update,
and situation assessment) is the problem of figuring out what state the world is in,
given a sequence of percepts. It is a core problem for all intelligent systems.
We have investigated both probabilistic state estimation
and nondeterministic logical state estimation; one current project
looks at the game of Kriegspiel, a version of
chess in which one cannot see any of the opponent's pieces.
- Hierarchical reinforcement learning
Intelligent behavior does not appear to consist of a completely
unstructured sequence of actions; instead, it seems to have
hierarchical structure in that each primitive action is part of some
higher-level activity, and so on up to very high-level activities such
as "get a PhD" and "earn enough money to retire to Bali".
Hierarchical reinforcement learning is about methods for learning
structured behaviors and using the structure of behavior to learn
faster and to reuse the results of learning in new contexts.
- Intelligent agent architectures
This topic combines all of the preceding topics in order to design
complete intelligent systems. We also examine general structural properties of
intelligent agents, including the connection between functional
decomposition of agents and additive decomposition of reward functions.
Some older projects (PNPACK, BATmobile, RoadWatch) are described here.