scholarly journals Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning

2020 ◽  
Author(s):  
Silvan Sievers ◽  
Florian Pommerening ◽  
Thomas Keller ◽  
Malte Helmert
Keyword(s):  
Admissible Heuristics

Cost partitioning is a method for admissibly combining admissible heuristics. In this work, we extend this concept to merge-and-shrink (M&S) abstractions that may use labels that do not directly correspond to operators. We investigate how optimal and saturated cost partitioning (SCP) interact with M&S transformations and develop a method to compute SCPs during the computation of M&S. Experiments show that SCP significantly improves M&S on standard planning benchmarks.

scholarly journals Solving Delete Free Planning with Relaxed Decision Diagram Based Heuristics

2020 ◽  
Vol 67 ◽  
pp. 607-651
Author(s):  
Margarita Paz Castro ◽  
Chiara Piacentini ◽  
Andre Augusto Cire ◽  
J. Christopher Beck
Keyword(s):  
Linear Programming ◽  
Empirical Analysis ◽  
State Of The Art ◽  
The State ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Admissible Heuristics ◽  
The Cost ◽  
Construction Algorithms ◽  
Diagram Representation

We investigate the use of relaxed decision diagrams (DDs) for computing admissible heuristics for the cost-optimal delete-free planning (DFP) problem. Our main contributions are the introduction of two novel DD encodings for a DFP task: a multivalued decision diagram that includes the sequencing aspect of the problem and a binary decision diagram representation of its sequential relaxation. We present construction algorithms for each DD that leverage these different perspectives of the DFP task and provide theoretical and empirical analyses of the associated heuristics. We further show that relaxed DDs can be used beyond heuristic computation to extract delete-free plans, find action landmarks, and identify redundant actions. Our empirical analysis shows that while DD-based heuristics trail the state of the art, even small relaxed DDs are competitive with the linear programming heuristic for the DFP task, thus, revealing novel ways of designing admissible heuristics.

scholarly journals Quantitatively relating abstractness to the accuracy of admissible heuristics

1995 ◽  
Vol 74 (1) ◽  
pp. 165-175 ◽  
Author(s):  
Armand Prieditis ◽  
Robert Davis
Keyword(s):  
Admissible Heuristics

scholarly journals Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning

2019 ◽  
Author(s):  
Jendrik Seipp
Keyword(s):  
New Method ◽  
Pattern Selection ◽  
Admissible Heuristics ◽  
Selection For ◽  
Selection Algorithms ◽  
Pattern Databases ◽  
Combining Information

Pattern databases are the foundation of some of the strongest admissible heuristics for optimal classical planning. Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning. Previous work selected patterns and computed saturated cost partitionings over the resulting pattern database heuristics in two separate steps. We introduce a new method that uses saturated cost partitioning to select patterns and show that it outperforms all existing pattern selection algorithms.

scholarly journals Machine discovery of effective admissible heuristics

1993 ◽  
Vol 12 (1-3) ◽  
pp. 117-141 ◽  
Author(s):  
Armand E. Prieditis
Keyword(s):  
Machine Discovery ◽  
Admissible Heuristics

scholarly journals Multi-Agent Path Finding for Large Agents

2019 ◽  
Vol 33 ◽  
pp. 7627-7634 ◽  
Author(s):  
Jiaoyang Li ◽  
Pavel Surynek ◽  
Ariel Felner ◽  
Hang Ma ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Tree Search ◽  
Multiple Constraints ◽  
Path Finding ◽  
Multiple Points ◽  
Admissible Heuristics ◽  
Single Location ◽  
Multi Agent ◽  
High Level

Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a twolevel tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MCCBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.

Criticizing solutions to relaxed models yields powerful admissible heuristics

1992 ◽  
Vol 63 (3) ◽  
pp. 207-227 ◽  
Author(s):  
Othar Hansson ◽  
Andrew Mayer ◽  
Moti Yung
Keyword(s):  
Admissible Heuristics

scholarly journals Online Speedup Learning for Optimal Planning

2012 ◽  
Vol 44 ◽  
pp. 709-755 ◽  
Author(s):  
C. Domshlak ◽  
E. Karpas ◽  
S. Markovitch
Keyword(s):  
Decision Rule ◽  
Search Space ◽  
Optimal Planning ◽  
Goal State ◽  
World State ◽  
Admissible Heuristics ◽  
State Space Search ◽  
Novel Method ◽  
Planning Task ◽  
The Cost

Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible heuristics have been developed, each with its own strengths and weaknesses, and it is well known that there is no single "best'' heuristic for optimal planning in general. Thus, which heuristic to choose for a given planning task is a difficult question. This difficulty can be avoided by combining several heuristics, but that requires computing numerous heuristic estimates at each state, and the tradeoff between the time spent doing so and the time saved by the combined advantages of the different heuristics might be high. We present a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its benefits. Using an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for learning a classifier with that decision rule as the target concept, and employ the learned classifier to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms the standard method for combining several heuristics via their pointwise maximum.

scholarly journals Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search

2019 ◽  
Author(s):  
Jiaoyang Li ◽  
Ariel Felner ◽  
Eli Boyarski ◽  
Hang Ma ◽  
Sven Koenig
Keyword(s):  
Success Rate ◽  
Recent Work ◽  
Path Finding ◽  
Admissible Heuristics ◽  
Multi Agent ◽  
High Level

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Path Finding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependencies between agents. Empirically, CBS with either new heuristic significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50.

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