The majority of search-based HTN planning systems can be divided into those searching a
space of partial plans (a plan space) and those performing progression search, ie, that build …

HTN planning provides an expressive formalism to model complex application domains. It
has been widely used in realworld applications. However, the development of …

In recent years, we have witnessed tremendous progress in deep reinforcement learning
(RL) for tasks such as Go, Chess, video games, and robot control. Nevertheless, other …

Translation-based approaches to planning allow for solving problems in complex and
expressive formalisms via the means of highly efficient solvers for simpler formalisms. To be …

In this planner abstract, we introduce the version of the Powerlifted (Corrêa et al. 2020)
planning system used in the IPC 2023. Powerlifted is a lifted planner that works directly on …

We address the problem of propositional planning extended with the class of soft temporally
extended goals supported in PDDL3, also called qualitative preferences since IPC-5. Such …

Planning via SAT has proven to be an efficient and versatile planning technique. Its
declarative nature allows for an easy integration of additional constraints and can harness …

Macro-operators, macros for short, are a well-known technique for enhancing performance
of planning engines by providing “short-cuts” in the state space. Existing macro learning …

Satisficing heuristic search such as greedy best-first search (GBFS) suffers from local
minima, regions where heuristic values are inaccurate and a good node has a worse …

Abstract Role-Based Access Control (RBAC) policies are at the core of Cybersecurity as
they ease the enforcement of basic security principles, eg, Least Privilege and Separation of …