scholarly journals The Complexity of Optimal Monotonic Planning: The Bad, The Good, and The Causal Graph

2013 ◽  
Vol 48 ◽  
pp. 783-812 ◽  
Author(s):  
C. Domshlak ◽  
A. Nazarenko
Keyword(s):  
Polynomial Time ◽  
State Of The Art ◽  
Optimal Planning ◽  
Graph Topology ◽  
Causal Graph ◽  
Time Optimal ◽  
Domain Independent ◽  
Positive Results

For almost two decades, monotonic, or ``delete free,'' relaxation has been one of the key auxiliary tools in the practice of domain-independent deterministic planning. In the particular contexts of both satisficing and optimal planning, it underlies most state-of-the-art heuristic functions. While satisficing planning for monotonic tasks is polynomial-time, optimal planning for monotonic tasks is NP-equivalent. Here we establish both negative and positive results on the complexity of some wide fragments of optimal monotonic planning, with the fragments being defined around the causal graph topology. Our results shed some light on the link between the complexity of general optimal planning and the complexity of optimal planning for the respective monotonic relaxations.

scholarly journals A Refined Understanding of Cost-optimal Planning with Polytree Causal Graphs

2019 ◽  
Author(s):  
Christer Bäckström ◽  
Peter Jonsson ◽  
Sebastian Ordyniak
Keyword(s):  
Complexity Analysis ◽  
Domain Size ◽  
Research Area ◽  
Optimal Planning ◽  
Causal Graph ◽  
New Methods ◽  
Causal Graphs ◽  
Important Research Area ◽  
Novel Method ◽  
Domain Independent

Complexity analysis based on the causal graphs of planning instances is a highly important research area. In particular, tractability results have led to new methods for constructing domain-independent heuristics. Important early examples of such results were presented by, for instance, Brafman & Domshlak and Katz & Keyder. More general results based on polytrees and bounding certain parameters were subsequently derived by Aghighi et al. and Ståhlberg. We continue this line of research by analyzing cost-optimal planning for instances with a polytree causal graph, bounded domain size and bounded depth. We show that no further restrictions are necessary for tractability, thus generalizing the previous results. Our approach is based on a novel method of closely analysing optimal plans: we recursively decompose the causal graph in a way that allows for bounding the number of variable changes as a function of the depth, using a reording argument and a comparison with prefix trees of known size. We then transform the planning instances into tree-structured constraint satisfaction instances.

Time-optimal Planning for the Quadrotor Transportation System Landing a Payload on the Mobile Platform

2021 ◽  
Author(s):  
Xiao Liang ◽  
Jinjiang Gao ◽  
Jianda Han
Keyword(s):  
Transportation System ◽  
Mobile Platform ◽  
Optimal Planning ◽  
Time Optimal

A Temporal Planning System for Time-Optimal Planning

2001 ◽  
pp. 379-392 ◽  
Author(s):  
Antonio Garrido ◽  
Eva Onaindía ◽  
Federico Barber
Keyword(s):  
Planning System ◽  
Optimal Planning ◽  
Temporal Planning ◽  
Time Optimal

scholarly journals Efficient Lifted Planning with Regression-Based Heuristics

2021 ◽  
Author(s):  
Hadi Qovaizi
Keyword(s):  
Organic Synthesis ◽  
State Of The Art ◽  
Transition System ◽  
Modern State ◽  
Synthetic Approach ◽  
Best First Search ◽  
Planning Task ◽  
International Planning ◽  
Domain Independent ◽  
Novel Algorithm

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

Analysis of solutions to the time-optimal planning and execution problem

2012 ◽  
Vol 5 (4) ◽  
pp. 245-258
Author(s):  
Thomas Allen ◽  
Steven Scheding
Keyword(s):  
Optimal Planning ◽  
Time Optimal

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

2018 ◽  
Vol 14 (3) ◽  
pp. 134-166 ◽  
Author(s):  
Amit Singh ◽  
Aditi Sharan
Keyword(s):  
Fuzzy Logic ◽  
Semantic Web ◽  
Linked Data ◽  
State Of The Art ◽  
Fuzzy Programming ◽  
Data Sources ◽  
Fuzzy Approach ◽  
Entity Linking ◽  
Efficient Information ◽  
Domain Independent

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

scholarly journals Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

2020 ◽  
Vol 20 (5) ◽  
pp. 609-624
Author(s):  
ANTONIUS WEINZIERL ◽  
RICHARD TAUPE ◽  
GERHARD FRIEDRICH
Keyword(s):  
Knowledge Representation ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Negative Effects ◽  
First Time ◽  
Domain Independent ◽  
Large Improvement ◽  
Answer Set ◽  
Art Techniques

AbstractAnswer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

Polynomial time optimal algorithms for time slot assignment of variable bandwidth systems

1994 ◽  
Vol 2 (3) ◽  
pp. 247-251 ◽  
Author(s):  
P. Barcaccia ◽  
M.A. Bonuccelli
Keyword(s):  
Polynomial Time ◽  
Time Slot ◽  
Optimal Algorithms ◽  
Variable Bandwidth ◽  
Slot Assignment ◽  
Time Optimal
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