Current research projects
General research topics
- Learning representations for planning: the ability to plan, which is
crucial in intelligent systems, relies on models that describe how the world
and sensors work. These models are usually expressed in declarative languages
that make the structure of problems explicit, and support reuse and effective
computation. A key open question is how these model representations can be
learned automatically. The problem ranges from learning symbolic
representations from non-symbolic data, to learning hierarchies of (learned or
symbolic) representations supporting planning at different levels of
abstraction.
- Planning models, algorithms, and techniques: planning models come in
different forms depending on the assumptions about actions, states, and
sensing. Classical planning is planning under the assumption of deterministic
actions, a full initial state, and goal states to be reached. Other forms of
planning like MDP and POMDP planning relax some of these assumptions or
address other aspects like continuous state spaces and actions. The challenge
is to develop scalable algorithms and techniques for addressing the variety of
planning models.
- Planning and reinforcement learning: reinforcement learning (RL) is a
generalization of planning where the planning models are not assumed to be
known and goals are replaced by rewards to be maximized. In model-based RL,
the RL problem is split into two: learning the models and then using them for
planning. In model-free RL, a controller is obtained directly from trial and
error without the need for learning a model. Some of the biggest AI
breakthroughs in recent years have been in Deep RL where the value and policy
functions are represented by deep neural networks whose weights are learned by
trial and error. The current limitation of these methods is that they require
huge amounts of data and that the policy and value functions learned do not
generalize well. The use of latent model-representations that are learned from
data without supervision is aimed at addressing these limitations and is
closely connected with the problem of learning planning representations from
data.
- Generalized planning: in the standard planning setting new problems are
solved from scratch. In generalized planning, on the other hand, one looks for
general plans or policies that provide solutions to many problems from the
same domain. For this, suitable formulations, models, and algorithms are
needed. Generalized planning provides another angle from which to study the
connection between learning and planning, as in reinforcement learning one is
also interested in learning things that have some generality and apply to many
problem instances.
- Model-based vs. model-free intelligence: the topics of learning,
representation, and planning are also at the center of the big split in AI
between model-free approaches based on learners, and model-based approaches
based on solvers. Truly intelligent systems must involve both, very much like
human intelligence, which is often described in terms of a fast reactive
System (1) and a slow deliberative System (2), which are tightly integrated
(See Daniel Kahneman 2011). For this integration, the models used by solvers
such as planners have to be learned automatically. This integration is a key
challenge in AI and a central goal for the research group.