scholarly journals Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning

2021 ◽  
Author(s):  
Nir Lipovetzky
Keyword(s):  
State Of The Art ◽  
General Definition ◽  
Future Research ◽  
Memory Consumption ◽  
Research Directions ◽  
Model Free ◽  
Future Research Directions ◽  
Common Problems ◽  
Definition Of ◽  
Simulation Engines

Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines. Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.

Positive Workplaces

2009 ◽  
pp. 578-588
Author(s):  
Fred Luthans ◽  
Carolyn M. Youssef
Keyword(s):  
Organizational Behavior ◽  
Psychological Capital ◽  
Future Research ◽  
Positive Organizational Behavior ◽  
Performance Impact ◽  
Research Directions ◽  
Future Research Directions ◽  
And Performance ◽  
Definition Of ◽  
Psychological Capacities

Over the years, both management practitioners and academics have generally assumed that positive workplaces lead to desired outcomes. Unlike psychology, considerable attention has also been devoted to the study of positive topics such as job satisfaction and organizational commitment. However, to place a scientifically based focus on the role that positivity may play in the development and performance of human resources, and largely stimulated by the positive psychology initiative, positive organizational behavior (POB) and psychological capital (PsyCap) have recently been introduced into the management literature. This chapter first provides an overview of both the historical and contemporary positive approaches to the workplace. Then, more specific attention is given to the meaning and domain of POB and PsyCap. Our definition of POB includes positive psychological capacities or resources that can be validly measured, developed, and have performance impact. The constructs that have been determined so far to best meet these criteria are efficacy, hope, optimism, and resiliency. When combined, they have been demonstrated to form the core construct of what we term psychological capital (PsyCap). A measure of PsyCap is being validated and this chapter references the increasing number of studies indicating that PsyCap can be developed and have performance impact. The chapter concludes with important future research directions that can help better understand and build positive workplaces to meet current and looming challenges.

Social media and consumer engagement: a review and research agenda

2016 ◽  
Vol 10 (4) ◽  
pp. 268-287 ◽  
Author(s):  
Victor Barger ◽  
James W. Peltier ◽  
Don E. Schultz
Keyword(s):  
Social Media ◽  
Recent Literature ◽  
Future Research ◽  
Consumer Engagement ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
Antecedents And Consequences ◽  
Area Of Concern ◽  
Definition Of

Purpose In “Social media’s slippery slope: challenges, opportunities and future research directions”, Schultz and Peltier (2013) asked “whether or how social media can be used to leverage consumer engagement into highly profitable relationships for both parties”. The purpose of this article is to continue this discussion by reviewing recent literature on consumer engagement and proposing a framework for future research. Design/methodology/approach The paper reviews the marketing literature on social media, paying particular attention to consumer engagement, which was identified as a primary area of concern in Schultz and Peltier (2013). Findings A significant amount of research has been conducted on consumer engagement since 2010. Lack of consensus on the definition of the construct has led to fragmentation in the discipline, however. As a result, research related to consumer engagement is often not identified as such, making it difficult for academics and practitioners to stay abreast of developments in this area. Originality/value This critical review provides marketing academics and practitioners insights into the antecedents and consequences of consumer engagement and offers a conceptual framework for future research.

scholarly journals Social commerce—state-of-the-art and future research directions

2016 ◽  
Vol 26 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Catherine Baethge ◽  
Julia Klier ◽  
Mathias Klier
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Social Commerce ◽  
Research Directions ◽  
Future Research Directions

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

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