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

2019 ◽  
Vol 33 ◽  
pp. 7520-7529 ◽  
Author(s):  
Gregor Behnke ◽  
Daniel Höller ◽  
Susanne Biundo
Keyword(s):  
Partial Order ◽  
Propositional Logic ◽  
State Of The Art ◽  
Partial Ordering ◽  
Compact Representation ◽  
Planning Techniques ◽  
Model Complex ◽  
Htn Planning ◽  
Sat Solver ◽  
Planning Problems

HTN planning provides an expressive formalism to model complex application domains. It has been widely used in realworld applications. However, the development of domainindependent planning techniques for such models is still lacking behind. The need to be informed about both statetransitions and the task hierarchy makes the realisation of search-based approaches difficult, especially with unrestricted partial ordering of tasks in HTN domains. Recently, a translation of HTN planning problems into propositional logic has shown promising empirical results. Such planners benefit from a unified representation of state and hierarchy, but until now require very large formulae to represent partial order. In this paper, we introduce a novel encoding of HTN Planning as SAT. In contrast to related work, most of the reasoning on ordering relations is not left to the SAT solver, but done beforehand. This results in much smaller formulae and, as shown in our evaluation, in a planner that outperforms previous SAT-based approaches as well as the state-of-the-art in search-based HTN planning.

scholarly journals Grounding of HTN Planning Domain

2017 ◽  
Vol 26 (05) ◽  
pp. 1760021 ◽  
Author(s):  
Abdeldjalil Ramoul ◽  
Damien Pellier ◽  
Humbert Fiorino ◽  
Sylvie Pesty
Keyword(s):  
Expert Knowledge ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Planning Problem ◽  
World State ◽  
Hierarchical Task Network ◽  
Htn Planning ◽  
Speed Up ◽  
International Planning ◽  
Planning Problems

Many Artificial Intelligence techniques have been developed for intelligent and autonomous systems to act and make rational decisions based on perceptions of the world state. Among these techniques, HTN (Hierarchical Task Network) planning is one of the most used in practice. HTN planning is based on expressive languages allowing to specify complex expert knowledge for real world domains. At the same time, many preprocessing techniques for classical planning were proposed to speed up the search. One of these technique, named grounding, consists in enumerating and instantiating all the possible actions from the planning problem descriptions. This technique has proven its effectiveness. Therefore, combining the expressiveness of HTN planning with the efficiency of the grounding preprocessing techniques used in classical planning is a very challenging issue. In this paper, we propose a generic algorithm to ground the domain representation for HTN planning. We show experimentally that grounding process improves the performances of state of the art HTN planners on a range of planning problems from the International Planning Competition (IPC).

scholarly journals Efficient SAT-based Proof Search in Intuitionistic Propositional Logic

2021 ◽  
pp. 217-233
Author(s):  
Camillo Fiorentini
Keyword(s):  
Propositional Logic ◽  
Simple Structure ◽  
State Of The Art ◽  
Search Procedure ◽  
Intuitionistic Propositional Logic ◽  
Proof Search ◽  
Sat Solver

AbstractWe present an efficient proof search procedure for Intuitionistic Propositional Logic which involves the use of an incremental SAT-solver. Basically, it is obtained by adding a restart operation to the system by Claessen and Rosén, thus we call our implementation . We gain some remarkable advantages: derivations have a simple structure; countermodels are in general small; using a standard benchmarks suite, we outperform and other state-of-the-art provers.

scholarly journals HTN Planning as Heuristic Progression Search

2020 ◽  
Vol 67 ◽  
pp. 835-880 ◽  
Author(s):  
Daniel Höller ◽  
Pascal Bercher ◽  
Gregor Behnke ◽  
Susanne Biundo
Keyword(s):  
State Of The Art ◽  
Classical Model ◽  
Empirical Evaluation ◽  
Partial Ordering ◽  
Planning Model ◽  
Limited Information ◽  
Planning Systems ◽  
Current State ◽  
Htn Planning ◽  
Partial Plans

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, i.e., that build the solution in a forward manner. So far, all HTN planners that guide the search by using heuristic functions are based on plan space search. Those systems represent the set of search nodes more effectively by maintaining a partial ordering between tasks, but they have only limited information about the current state during search. In this article, we propose the use of progression search as basis for heuristic HTN planning systems. Such systems can calculate their heuristics incorporating the current state, because it is tracked during search. Our contribution is the following: We introduce two novel progression algorithms that avoid unnecessary branching when the problem at hand is partially ordered and show that both are sound and complete. We show that defining systematicity is problematic for search in HTN planning, propose a definition, and show that it is fulfilled by one of our algorithms. Then, we introduce a method to apply arbitrary classical planning heuristics to guide the search in HTN planning. It relaxes the HTN planning model to a classical model that is only used for calculating heuristics. It is updated during search and used to create heuristic values that are used to guide the HTN search. We show that it can be used to create HTN heuristics with interesting theoretical properties like safety, goal-awareness, and admissibility. Our empirical evaluation shows that the resulting system outperforms the state of the art in search-based HTN planning.

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

scholarly journals abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

2020 ◽  
Author(s):  
Yan Gao ◽  
Yongzhuang Liu ◽  
Yanmei Ma ◽  
Bo Liu ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Error Correction ◽  
Partial Order ◽  
Directed Acyclic Graph ◽  
State Of The Art ◽  
Single Instruction Multiple Data ◽  
Multiple Sequence ◽  
Software Interface ◽  
Multiple Data ◽  
Long Read ◽  
Read Error Correction

AbstractSummaryPartial order alignment, which aligns a sequence to a directed acyclic graph, is now frequently used as a key component in long-read error correction and assembly. We present abPOA (adaptive banded Partial Order Alignment), a Single Instruction Multiple Data (SIMD) based C library for fast partial order alignment using adaptive banded dynamic programming. It can work as a stand-alone multiple sequence alignment and consensus calling tool or be easily integrated into any long-read error correction and assembly workflow. Compared to a state-of-the-art tool (SPOA), abPOA is up to 15 times faster with a comparable alignment accuracy.Availability and implementationabPOA is implemented in C. A stand-alone tool and a C/Python software interface are freely available at https://github.com/yangao07/[email protected] or [email protected]

scholarly journals Dolius: A Distributed Parallel SAT Solving Framework

10.29007/hvqt ◽  
2018 ◽  
Author(s):  
Gilles Audemard ◽  
Benoît Hoessen ◽  
Saïd Jabbour ◽  
Cédric Piette
Keyword(s):  
Shared Memory ◽  
State Of The Art ◽  
Divide And Conquer ◽  
Sat Solving ◽  
Sat Solvers ◽  
Sat Solver

Over the years, parallel SAT solving becomes more and more important. However, most of state-of-the-art parallel SAT solvers are portfolio-based ones. They aim at running several times the same solver with different parameters. In this paper, we propose a tool called Dolius, mainly based on the divide and conquer paradigm. In contrast to most current parallel efficient engines, Dolius does not need shared memory, can be distributed, and scales well when a large number of computing units is available. Furthermore, our tool contains an API allowing to plug any SAT solver in a simple way.

scholarly journals Data-driven planning via imitation learning

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1632-1672 ◽  
Author(s):  
Sanjiban Choudhury ◽  
Mohak Bhardwaj ◽  
Sankalp Arora ◽  
Ashish Kapoor ◽  
Gireeja Ranade ◽  
...  
Keyword(s):  
Partial Information ◽  
State Of The Art ◽  
Imitation Learning ◽  
Data Driven ◽  
Sequential Decision ◽  
Efficient Manner ◽  
World State ◽  
Performance Guarantees ◽  
The World ◽  
Planning Problems

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to [Formula: see text] more reward than state-of-the art information-gathering heuristics and a [Formula: see text] speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

scholarly journals Autoencoder-based patch learning for real-world image denoising

2019 ◽  
Vol 13 ◽  
pp. 174830261988139
Author(s):  
Fei Chen ◽  
Haiqing Chen ◽  
Xunxun Zeng ◽  
Meiqing Wang
Keyword(s):  
Image Denoising ◽  
Real World ◽  
State Of The Art ◽  
Compact Representation ◽  
Natural Image ◽  
Great Success ◽  
Image Patch ◽  
Self Similarity ◽  
Coding Scheme ◽  
World Image

Internal patch prior (e.g. self-similarity) has achieved a great success in image denoising. However, it is a challenging task to utilize clean external natural patches for denoising. Natural image patch comes from very complex distributions which are hard to learn without supervision. In this paper, we use an autoencoder to discover and utilize these underlying distributions to learn a compact representation that is more robust to realistic noises. By exploiting learned external prior and internal self-similarity jointly, we develop an efficient patch sparse coding scheme for real-world image denoising. Numerical experiments demonstrate that the proposed method outperforms many state-of-the-art denoising methods, especially on removing realistic noise.

scholarly journals Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review

2018 ◽  
Vol 267 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Teun van Gils ◽  
Katrien Ramaekers ◽  
An Caris ◽  
René B.M. de Koster
Keyword(s):  
State Of The Art ◽  
Order Picking ◽  
Picking Systems ◽  
Planning Problems
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