Improving Automated Planning with Machine Learning

2010 ◽  
pp. 599-620
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Improving Automated Planning with Machine Learning

2012 ◽  
pp. 1355-1373
Author(s):  
Susana Fernández Arregui ◽  
Sergio Jiménez Celorrio ◽  
Tomás de la Rosa Turbides
Keyword(s):  
Machine Learning ◽  
Automated Planning ◽  
Machine Learning Techniques ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Search Processes ◽  
Action Models ◽  
New Challenges ◽  
Definition Of

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

scholarly journals An overview of machine learning techniques in constraint solving

2021 ◽  
Author(s):  
Andrei Popescu ◽  
Seda Polat-Erdeniz ◽  
Alexander Felfernig ◽  
Mathias Uta ◽  
Müslüm Atas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Constraint Solving ◽  
Advantages And Disadvantages ◽  
Online Sales ◽  
Search Heuristics ◽  
Learning Techniques ◽  
Search Processes ◽  
Future Work

AbstractConstraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales scenarios. Constraint solvers apply, for example, search heuristics to assure adequate runtime performance and prediction quality. Several approaches have already been developed showing that machine learning (ML) can be used to optimize search processes in constraint solving. In this article, we provide an overview of the state of the art in applying ML approaches to constraint solving problems including constraint satisfaction, SAT solving, answer set programming (ASP) and applications thereof such as configuration, constraint-based recommendation, and model-based diagnosis. We compare and discuss the advantages and disadvantages of these approaches and point out relevant directions for future work.

scholarly journals Using Argument Mining for Legal Text Summarization

2020 ◽  
Author(s):  
Huihui Xu ◽  
Jaromír Šavelka ◽  
Kevin D. Ashley
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Text Summarization ◽  
Machine Learning Techniques ◽  
Legal Argument ◽  
Legal Text ◽  
Argumentative Text ◽  
Advantages And Disadvantages ◽  
Learning Techniques

Argument mining, a subfield of natural language processing and text mining, is a process of extracting argumentative text portions and identifying the role the selected texts play. Legal argument mining targets the argumentative parts of a legal text. In order to better understand how to apply legal argument mining as a step toward improving case summarization, we have assembled a sizeable set of cases and human-expert-prepared summaries annotated in terms of legal argument triples that capture the most important skeletal argument structures in a case. We report the results of applying multiple machine learning techniques to demonstrate and analyze the advantages and disadvantages of different methods to identify sentence components of these legal argument triples.

scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

scholarly journals Air Temperature Forecasting Using Machine Learning Techniques: A Review

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4215 ◽  
Author(s):  
Jenny Cifuentes ◽  
Geovanny Marulanda ◽  
Antonio Bello ◽  
Javier Reneses
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Regional Scale ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Industrial Sectors ◽  
Learning Techniques ◽  
Temperature Forecasting ◽  
The Government

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

A review of machine learning for automated planning

2012 ◽  
Vol 27 (4) ◽  
pp. 433-467 ◽  
Author(s):  
Sergio Jiménez ◽  
Tomás De La Rosa ◽  
Susana Fernández ◽  
Fernando Fernández ◽  
Daniel Borrajo
Keyword(s):  
Machine Learning ◽  
Scale Up ◽  
Automated Planning ◽  
Related Field ◽  
Domain Specific ◽  
Hand Off ◽  
Action Models ◽  
Control Knowledge ◽  
The One ◽  
Definition Of

AbstractRecent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

Case-based reasoning for safety assessment of critical software

2020 ◽  
pp. 1-17
Author(s):  
Habib Hadj-Mabrouk
Keyword(s):  
Machine Learning ◽  
Rail Transport ◽  
Machine Learning Techniques ◽  
Transport Systems ◽  
Risk Level ◽  
Case Based Reasoning ◽  
Learning Techniques ◽  
Future System ◽  
Depth Analysis ◽  
Case Based

The commissioning of a new guided or automated rail transport system requires an in-depth analysis of all the methods, techniques, procedures, regulations and safety standards to ensure that the risk level of the future system does not present any danger likely to jeopardize the safety of travelers. Among these numerous safety methods implemented to guarantee safety at the system, automation, hardware and software level, there is a method called “Software Errors and Effects Analysis (SEEA)” whose objective is to determine the nature and the severity of the consequences of software failures, to propose measures to detect errors and finally to improve the robustness of the software. In order to strengthen and rationalize this SEEA method, we have agreed to use machine learning techniques and in particular Case-Based Reasoning (CBR) in order to assist the certification experts in their difficult task of assessing completeness and the consistency of safety of critical software equipment. The main objective consists, from a set of data in the form of accident scenarios or incidents experienced on rail transport systems (experience feedback), to exploit by automatic learning this mass of data to stimulate the imagination of certification experts and assist them in their crucial task of researching scenarios of potential accidents not taken into account during the design phase of new critical software. The originality of the tool developed lies not only in its ability to model, capitalize, sustain and disseminate SEEA expertise, but it represents the first research on the application of CBR to SEEA. In fact, in the field of rail transport, there are currently no software tools for assisting SEEAs based on machine learning techniques and in particular based on CBR.

scholarly journals A review of learning planning action models

2018 ◽  
Vol 33 ◽  
Author(s):  
Ankuj Arora ◽  
Humbert Fiorino ◽  
Damien Pellier ◽  
Marc Métivier ◽  
Sylvie Pesty
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning To Learn ◽  
Learning Techniques ◽  
Action Models ◽  
The 1960S ◽  
Continuous Field ◽  
Activity Domain ◽  
Planning Action ◽  
Open Issues

AbstractAutomated planning has been a continuous field of study since the 1960s, since the notion of accomplishing a task using an ordered set of actions resonates with almost every known activity domain. However, as we move from toy domains closer to the complex real world, these actions become increasingly difficult to codify. The reasons range from intense laborious effort, to intricacies so barely identifiable, that programming them is a challenge that presents itself much later in the process. In such domains, planners now leverage recent advancements in machine learning to learn action models, that is, blueprints of all the actions whose execution effectuates transitions in the system. This learning provides an opportunity for the evolution of the model toward a version more consistent and adapted to its environment, augmenting the probability of success of the plans. It is also a conscious effort to decrease laborious manual coding and increase quality. This paper presents a survey of the machine learning techniques applied for learning planning action models. It first describes the characteristics of learning systems. It then details the learning techniques that have been used in the literature during the past decades, and finally presents some open issues.

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