We present the first approach capable of learning domain-independent planning heuristics
entirely from scratch. The heuristics we learn map the hypergraph representation of the …

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 …

Recently, neural heuristics based on deep learning have reported encouraging results for
solving vehicle routing problems (VRPs), especially on independent and identically …

The performance of an algorithm often critically depends on its parameter configuration.
While a variety of automated algorithm configuration methods have been proposed to …

Neural networks (NN) have been shown to be powerful state-value predictors in several
complex games. Can similar successes be achieved in classical planning? Towards a …

Automated planning is one of the foundational areas of AI. Since no single planner can work
well for all tasks and domains, portfolio-based techniques have become increasingly …

A key challenge in satisficing planning is to use multiple heuristics within one heuristic
search. An aggregation of multiple heuristic estimates, for example by taking the maximum …

The multi-objective optimization (MOO) of complex systems remains a challenging task in
engineering domains. The methodological approach of applying MOO algorithms to …

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 …

Learning a well-informed heuristic function for hard task planning domains is an elusive
problem. Although there are known neural network architectures to represent such heuristic …