Recent discoveries in automated planning are broadening the scope of planners, from toy
problems to real applications. However, applying automated planners to real-world …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Generalized planning is concerned with the computation of general policies that solve
multiple instances of a planning domain all at once. It has been recently shown that these …

Generalized planning is concerned with the computation of plans that solve not one but
multiple instances of a planning domain. Recently, it has been shown that generalized plans …

Many traditional solution approaches to relationally specified decision-theoretic planning
problems (eg, those stated in the probabilistic planning domain description language, or …

A fundamental problem in reinforcement learning is balancing exploration and exploitation.
We address this problem in the context of model-based reinforcement learning in large …

Statistical relational models combine aspects of first-order logic and probabilistic graphical
models, enabling them to model complex logical and probabilistic interactions between …

We introduce DTProbLog, a decision-theoretic extension of Prolog and its probabilistic
variant ProbLog. DTProbLog is a simple but expressive probabilistic programming language …

It has been observed that many classical planning domains with atomic goals can be solved
by means of a simple polynomial exploration procedure, called IW, that runs in time …