Behavioral pattern

Résumé

In software engineering, behavioral design patterns are design patterns that identify common communication patterns among objects. By doing so, these patterns increase flexibility in carrying out communication. Design patterns Examples of this type of design pattern include: ;Blackboard design pattern : Provides a computational framework for the design and implementation of systems that integrate large and diverse specialized modules, and implement complex, non-deterministic control strategies ;Chain of responsibility pattern : Command objects are handled or passed on to other objects by logic-containing processing objects ;Command pattern : Command objects encapsulate an action and its parameters ;"Externalize the stack" : Turn a recursive function into an iterative function that uses a stack ;Interpreter pattern : Implement a specialized computer language to rapidly solve a specific set of problems ;Iterator pattern : Iterators are used to access the elements of an aggr

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What if Social Robots look for Productive Engagement?

Barbara Bruno, Pierre Dillenbourg, Jauwairia Nasir

In educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human-human-robot setup with

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students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will 1) distinguish teams based on engagement that is conducive of learning; and 2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labeling based on the data.

2021

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Self-organized collective behavior has been analyzed in diverse types of gregarious animals. Such collective intelligence emerges from the synergy between individuals, which behave at their own time and spatial scales and without global rules. Recently, robots have been developed to collaborate with animal groups in the pursuit of better understanding their decision-making processes. These biohybrid systems make cooperative relationships between artificial systems and animals possible, which can yield new capabilities in the resulting mixed group. However, robots are currently tailor-made to successfully engage with one animal species at a time. This limits the possibilities of introducing distinct species-dependent perceptual capabilities and types of behaviors in the same system. Here, we show that robots socially integrated into animal groups of honeybees and zebrafish, each one located in a different city, allowing these two species to interact. This interspecific information transfer is demonstrated by collective decisions that emerge between the two autonomous robotic systems and the two animal groups. The robots enable this biohybrid system to function at any distance and operates in water and air with multiple sensorimotor properties across species barriers and ecosystems. These results demonstrate the feasibility of generating and controlling behavioral patterns in biohybrid groups of multiple species. Such interspecies connections between diverse robotic systems and animal species may open the door for new forms of artificial collective intelligence, where the unrivaled perceptual capabilities of the animals and their brains can be used to enhance autonomous decision-making, which could find applications in selective "rewiring" of ecosystems.

2019

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Context: Exploring the design space is an important process in a design task. In this study, we considered design space exploration for the learners in vocational education and training (VET). The goal of the study was to investigate how they explore the design space while focusing on the effect of a graph-like interface on the learner’s understanding of the design space. With florists as the target profession, we investigated how the apprentices explore design variations, what they would gain from such activity, and how we can support this process. Approach: We developed a web application called BloomGraph that allows learners to explore design variations. It provides a graph-based interface that enables the systematic variation of design. Using the BloomGraph application, we conducted an experimental study with 44 florist apprentices in Switzerland to investigate the effect of the graph-based interface which provides a structured way of exploring the design space. The experimental group was given the graph-based interface to explore design variations while the control group had a linear-based interface. We compared them in terms of the number of bouquets explored, time of exploration, diversity of bouquets explored, and the learning gain in terms of the understanding of the design space measured using pre and post-tests. We also analyzed the strategies adopted by the participants for the graph navigation and the visual exploration behavior using the eye gaze data. Findings: Our analysis shows that the graph-based interface fosters a better understanding of the size of the design space and more efficient navigation towards a goal design in terms of the number of intermediate designs but with longer exploration of each intermediate design compared to the linear-based interface. Regarding the behavioral patterns in graph exploration, the participants who showed more strategic behavior in the design choices acquired a better understanding of the design space. Additionally, we trained a model that predicts the next choice of a learner using eye tracking data. It provides a reasonable accuracy that opens new possibilities for future studies. Conclusion: The findings of this study support the feasibility of design space exploration as a digital activity for VET learners and show how the learners can benefit from it. The contribution of the paper includes the validation of the idea with florist apprentices and the demonstration of how the process can be supported using a structured interface and the learner behavior analysis. This paper shows how a design exploration activity can provide an added value in the learning of an apprentice in a design-related VET system.

2021

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What if Social Robots look for Productive Engagement?

Barbara Bruno, Pierre Dillenbourg, Jauwairia Nasir

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2021

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