Abstract We review the 2014 International Planning Competition (IPC-2014), the eighth in a
series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess …

Modern robots are increasingly capable of performing “basic” activities such as localization,
navigation, and motion planning. However, for a robot to be considered intelligent, we would …

We present PROST, a probabilistic planning system that is based on the UCT algorithm by
Kocsis and Szepesvari (2006), which has been applied successfully to many areas of …

In this paper, we introduce the Action Schema Network (ASNet): a neural network
architecture for learning generalised policies for probabilistic planning problems. By …

We address the problem of computing a policy for fully observable non-deterministic (FOND)
planning problems. By focusing on the relevant aspects of the state of the world, we …

In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …

Planning is motivated by acting. Most of the existing work on automated planning
underestimates the reasoning and deliberation needed for acting; it is instead biased …

This paper investigates hindsight optimization as an approach for leveraging the significant
advances in deterministic planning for action selection in probabilistic domains. Hindsight …

In this article we review the 2011 International Planning Competition. We give an overview
of the history of the competition, discussing how it has developed since its first edition in …

We address one-shot imitation learning, where the goal is to execute a previously unseen
task based on a single demonstration. While there has been exciting progress in this …