scholarly journals Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators

2005 ◽  
Vol 24 ◽  
pp. 581-621 ◽  
Author(s):  
A. Botea ◽  
M. Enzenberger ◽  
M. Mueller ◽  
J. Schaeffer
Keyword(s):  
Recent Progress ◽  
State Of The Art ◽  
Structural Information ◽  
Relevant Information ◽  
Large Reduction ◽  
Ai Planning ◽  
Speed Up ◽  
Problem Instances ◽  
International Planning ◽  
Automated Methods

Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.

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 Neural Networks for Predicting Algorithm Runtime Distributions

2018 ◽  
Author(s):  
Katharina Eggensperger ◽  
Marius Lindauer ◽  
Frank Hutter
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Combinatorial Problems ◽  
Problem Instance ◽  
Ai Planning ◽  
Algorithm Selection ◽  
Problem Instances ◽  
Runtime Distributions ◽  
Better Than ◽  
Do So

Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance. Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts. Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs. To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations. In an empirical study involving five algorithms for SAT solving and AI planning, we show that neural networks predict the true RTDs of unseen instances better than previous methods, and can even do so when only few runtime observations are available per training instance.

scholarly journals The 3rd International Planning Competition: Results and Analysis

2003 ◽  
Vol 20 ◽  
pp. 1-59 ◽  
Author(s):  
D. Long ◽  
M. Fox
Keyword(s):  
Statistical Analysis ◽  
Ai Planning ◽  
Relative Difficulty ◽  
Comparative Performance ◽  
Planning And Scheduling ◽  
International Conference ◽  
The Third ◽  
The Future ◽  
Problem Instances ◽  
International Planning

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series.

Review on biomass feedstocks, pyrolysis mechanism and physicochemical properties of biochar: State-of-the-art framework to speed up vision of circular bioeconomy

2021 ◽  
Vol 297 ◽  
pp. 126645
Author(s):  
Gajanan Sampatrao Ghodake ◽  
Surendra Krushna Shinde ◽  
Avinash Ashok Kadam ◽  
Rijuta Ganesh Saratale ◽  
Ganesh Dattatraya Saratale ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
State Of The Art ◽  
Pyrolysis Mechanism ◽  
Biomass Feedstocks ◽  
Speed Up

scholarly journals Solid-state nanopore systems: from materials to applications

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yuhui He ◽  
Makusu Tsutsui ◽  
Yue Zhou ◽  
Xiang-Shui Miao
Keyword(s):  
Solid State ◽  
Recent Progress ◽  
State Of The Art ◽  
Ion Selectivity ◽  
Dna Origami ◽  
Fundamental Interest ◽  
Reverse Electrodialysis ◽  
Promising Application ◽  
Flow Through ◽  
Wall Materials

AbstractIon transport and hydrodynamic flow through nanometer-sized channels (nanopores) have been increasingly studied owing to not only the fundamental interest in the abundance of novel phenomena that has been observed but also their promising application in innovative nanodevices, including next-generation sequencers, nanopower generators, and memristive synapses. We first review various kinds of materials and the associated state-of-the-art processes developed for fabricating nanoscale pores, including the emerging structures of DNA origami and 2-dimensional nanopores. Then, the unique transport phenomena are examined wherein the surface properties of wall materials play predominant roles in inducing intriguing characteristics, such as ion selectivity and reverse electrodialysis. Finally, we highlight recent progress in the potential application of nanopores, ranging from their use in biosensors to nanopore-based artificial synapses.

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 Mobile Blended Learning with eSquirrel

2015 ◽  
Vol 8 (4Β) ◽  
Author(s):  
Michael Maurer
Keyword(s):  
Blended Learning ◽  
Mobile Learning ◽  
Recent Progress ◽  
State Of The Art ◽  
The State ◽  
Educational Institutions ◽  
Classroom Teaching ◽  
Learning Platform ◽  
Start Up ◽  
Innovative Solution

This article outlines the state of the art of mobile blended learning apps. It describes recent progress in this area, and explains the potential of mobile blended learning for schools and educational institutions. Furthermore, it presents an innovative solution, eSquirrel, which is developed by an Austrian inter-disciplinary start-up. eSquirrel is a blended learning platform that combines mobile learning with gamification. It blends the concepts of classroom teaching, eLearning and learning from books into a native Android and iOS course app, and enables teachers to learn their students’ progress.

scholarly journals ConnectIt

2020 ◽  
Vol 14 (4) ◽  
pp. 653-667
Author(s):  
Laxman Dhulipala ◽  
Changwan Hong ◽  
Julian Shun
Keyword(s):  
Experimental Evaluation ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Graph Connectivity ◽  
Connected Components ◽  
Sampling Strategies ◽  
Spanning Forest ◽  
Speed Up ◽  
Minimum Spanning Forest ◽  
Edge Sampling

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

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