Generalized planning studies the representation, computation and evaluation of solutions
that are valid for multiple planning instances. These are topics studied since the early days …

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

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 …

Abstract Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty as a belief …

Current classical planners are very successful in finding (nonoptimal) plans, even for large
planning instances. To do so, most planners rely on a preprocessing stage that computes a …

This paper investigates three different technologies for solving a planning and scheduling
problem of deploying multiple robots in a retirement home environment to assist elderly …

Generalized planning is the task of generating a single solution (a generalized plan) that is
valid for multiple planning instances. In this paper we introduce a novel formalism for …

Can an asset manager plan the optimal timing for her/his hedging strategies given market
conditions? The standard approach based on Markowitz or other more or less sophisticated …

Generalized planning is the task of generating a single solution that is valid for a set of
planning problems. In this paper we show how to represent and compute generalized plans …

One of the current limitations for large-scale use of planning technology in real world
applications is the lack of software platforms to integrate the full spectrum of planning-related …