The need for multiple plans has been established by various planning applications. In some,
solution quality has the predominant role, while in others diversity is the key factor. Most …

Width-based search algorithms have recently emerged as a simple yet effective approach to
planning. Best-First Width Search (BFWS) is one of the most successful satisficing width …

Lifted planners are useful to solve tasks that are too hard to ground. Still, computing
informative lifted heuristics is difficult: directly adapting ground heuristics to the lifted setting …

It has been shown recently that heuristic and width-based search can be combined to
produce planning algorithms with a performance that goes beyond the state-of-the-art. Such …

Acting in robotics is driven by reactive and deliberative reasonings which take place in the
competition between execution and planning processes. Properly balancing reactivity and …

Width-based search algorithms seek plans by prioritizing states according to a suitably
defined measure of novelty, that maps states into a set of novelty categories. Space and time …

Width-based algorithms search for solutions through a general definition of state novelty.
These algorithms have been shown to result in state-of-the-art performance in classical …

Best-first width search (BFWS) is a recent approach to satisficing planning that combines
traditional heuristics with novelty measures to achieve a balance between exploration and …

Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI:
reinforcement learning (RL), AI planning (AP), foundation models, and operations research …

Heuristic search is a powerful approach for solving planning problems and numeric
planning is no exception. In this paper, we boost the performance of heuristic search for …