Procrastination on Social Networking Sites: Combating by Design

Procrastination refers to a voluntary postponement that prevents people from performing their tasks and can hurt productivity and wellbeing. Procrastination might occur due to a lack of motivation to perform tasks or due to the low self-control that people might have over their time and task management. Social Networking Sites (hereafter SNS) are designed to enable their users to engage in online interaction for different purposes such as increasing popularity or exploring information. SNS embed influence and persuasion techniques to attract users which can make them a medium for procrastination where some users fail to maintain a desirable level of self-control over their usage. However, we argue that advances in persuasive technology and gamification techniques can be utilised to augment SMS and help users to regain self-control over their procrastination. Implementing these techniques correctly means that users can still enjoy accessing SNS while maintaining a desirable level of control over their procrastination. Building these antiprocrastination tools, however, is a challenging design activity due to their potential of triggering negative side-effects such as reactance and workarounds, and affecting the overall user experience. In this paper, we conduct user studies, consisting of an exploratory stage using focus groups, diary study and interviews and followed by a design stage based mainly on codesign sessions. Our studies’ participants self-declared having a problematic degree of procrastination on SNS, to explore procrastination countermeasure techniques that can augment the future designs of SNS and how best to apply them

HTN planning: Overview, comparison, and beyond

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique.<br/

Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed

Procrastination on social networking sites: types, triggers, and socio-technical countermeasures.

Procrastination has become an important field in academic research and refers to a voluntary delay in performing tasks that need to be done. Procrastination can lead to negative consequences such as low academic performance, low work productivity and anxiety. Numerous studies have examined the factors that may lead people to procrastinate, such as low self-efficacy, low self-regulation and low self-esteem. Social networking sites (SNSs) may facilitate procrastination; for example, notifications could be a distraction that promotes procrastination for people, preventing them from performing their original tasks. This Thesis aims to understand how procrastination on SNS occurs, the role of SNS design in triggering it and how to engineer social media to combat it through existing and novel features. Then, this knowledge will be used to develop a method to combat procrastination on SNS. This method will be informed by psychological theories as well as technical and socio-technical countermeasures. To achieve this goal, a mixed methods approach was conducted with SNS users, including focus groups and diary studies, co-design sessions and surveys. The results of these studies helped to develop a method that helps users to gain more control over their procrastination on SNS. The developed method is supported by persuasive techniques including reminders and suggestions, which help to persuade users to change their usage style without forcing them toward the change. Finally, the developed method was evaluated with SNS users who self-declared as procrastinators on SNS. The evaluation study examines five aspects: clarity, procrastination awareness, coverage, effectiveness and acceptance. The results demonstrated that the combating procrastination on SNS method (D-Crastinate) helps to improve users’ control over their procrastination

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics

A Comparative analysis of Partial Order Planning and Task Reduction Planning

Although task reduction (HTN) planning historically preceded partial order (PO) planning, and is believed to be more general than the latter, very little comparative analysis of the two planning formalisms has been done. Part of the reason for this has been the lack of systematic understanding of the functionalities provided by HTN planning over and above that of partial order planning. In thi