• Corpus ID: 51996246

Best-First Width Search in the IPC 2018 : Complete , Simulated , and Polynomial Variants

@inproceedings{Francs2018BestFirstWS,
  title={Best-First Width Search in the IPC 2018 : Complete , Simulated , and Polynomial Variants},
  author={Guillem Franc{\`e}s and Nir Lipovetzky},
  year={2018}
}
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-based algorithms, as it strikes a good balance between an effective exploration based on a measure of state novelty and the exploitation provided by traditional goal-directed heuristics. Several conceptually interesting BFWS variants have recently been shown to offer state-of-the-art performance, including a polynomial… 

Tables from this paper

Width-Based Backward Search

A front-to-end bidirectional search k-BDWS-e and its front- to-front variant is proposed by integrating forward and backward k-BFWS(f5) with the additional intersection check between expanded states whose novelty is 1 in the opposite Close list.

Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning

The area of width-based planning is summarized, current and future research directions are surveyed, and polynomial guarantees on their runtime and memory consumption are provided.

Best-First Width Search for Lifted Classical Planning

This work adapts best-first width search to the lifted setting and shows that this yields state-of-the-art performance for hard-to-ground planning tasks.

HTN Planning as Heuristic Progression Search

This article introduces two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and shows that both are sound and complete and introduces a method to apply arbitrary classical planning heuristics to guide the search in HTN planning.

Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

This work introduces several online-refinement search algorithms, based on hill-climbing and greedy best-first search, and evaluates them with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness.

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

A new classical planning heuristic is provided that solves the most puzzles among the baselines, and although it is 35 times faster than the best baseline planner, it remains below human-level performance.

Tracking Branches in Trees - A Propositional Encoding for Solving Partially-Ordered HTN Planning Problems

This work proposes the first propositional encodings which are able to solve general, i.e., partially-ordered, HTN planning problems, based on a previous encoding for totally-ordered problems, and outperforms existing HTN planners significantly.

Automatic Configuration of Benchmark Sets for Classical Planning

An automated method is proposed for creating benchmarks that automatically configure the parameters of benchmark domains and shows that the resulting benchmark set improves empirical comparisons by allowing to differentiate between planners more easily.

Automatic Instance Generation for Classical Planning

Autoscale is introduced, an automatic tool that selects instances for a given domain and shows that the resulting benchmark set is superior to the IPC set and has the potential of improving empirical evaluation of planning research.

Bringing Order to Chaos - A Compact Representation of Partial Order in SAT-Based HTN Planning

This paper introduces a novel encoding of HTN Planning as SAT, which results in a planner that outperforms previous SAT-based approaches as well as the state-of-the-art in search-based HTN planning.

References

SHOWING 1-10 OF 34 REFERENCES

Best-First Width Search: Exploration and Exploitation in Classical Planning

This work forms a simple and general computational framework where standard goal-oriented search (exploitation) and width-based search (structural exploration) are combined to yield a search scheme, best-first width search, that is better than both and which results in classical planning algorithms that outperform the state-of-the-art planners.

A Polynomial Planning Algorithm that Beats LAMA and FF

This work shows that incomplete versions of BFWS(f5), where nodes with novelty greater than k are pruned, are not only polynomial but have an empirical performance that is better than both BFWS (f5) and state-of-the-art planners.

Width-based Algorithms for Classical Planning: New Results

Two simple extension to Iterated Width and Serialized IW are introduced that narrow the performance gap with state-of-the-art planners.

Width and Serialization of Classical Planning Problems

A width parameter is introduced that bounds the complexity of classical planning problems and domains, along with a simple but effective blind-search procedure that runs in time that is exponential in the problem width, resulting in a 'blind' planner that competes well with a best-first search planner guided by state-of-the-art heuristics.

The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

It is found that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial, and in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future.

The FF Planning System: Fast Plan Generation Through Heuristic Search

A novel search strategy is introduced that combines hill-climbing with systematic search, and it is shown how other powerful heuristic information can be extracted and used to prune the search space.

Width-Based Planning for General Video-Game Playing

This paper uses the IW(1) algorithm for selecting actions in the games of the general video-game AI competition (GVG-AI), which, unlike classical planning problems and the Atari games, are stochastic.

Structure and inference in classical planning

It is shown that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account.

Planning as satisfiability: Heuristics

Pushing the Envelope: Planning, Propositional Logic and Stochastic Search

Stochastic methods are shown to be very effective on a wide range of scheduling problems, but this is the first demonstration of its power on truly challenging classical planning instances.