Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Practical preparation for a life of good citizenship or just a waste of time? A study of student engagement with the American liberal arts curriculum at an international university in London

The American liberal arts tradition offers students the opportunity to take a broad range of course modules, learn about diverse cultures and take part in programmes and services that expand their ways of thinking and learning. (Carnegie, 2018) A liberal arts education claims its ultimate goal is to develop the individual to play an active role in his/her local and global community by teaching global citizenship and motivating graduates to continue a life of learning (AACU, 2019). This study considers a first-year cohort of international students entering university for the first time in London. It considers their motivations, expectations and ideas of what a liberal arts education will do for them and how these motivations and expectations develop and change in the first year. This study also focuses on engagement strategies that the institution has developed to promote skill-building and the development of global citizenship and analyses how effective these are in retaining students from year one to year two. Ultimately, the study seeks to discover if a liberal arts education at its early stages does indeed do what its students perceive it to do and whether or not the practicalities of training for good citizenship hold value and meaning to the students taking part on the course. Liberal arts education has weathered a number of trends in higher education over the years. From a focus on technical training and skills-based learning in the 1980s, to a shift back to personal development in the 1990s, the offer of general education courses has always occupied a place in universities around the world.(Menard, 2010) But as tuition fees increase and demands for student employment also rise, liberal arts programmes seem threatened. Supporters wonder if there is a place for them anymore in preparing young people for life beyond university. (Knight, 2008) The outcomes of this study are mixed. Based on the students’ expectations, feedback, engagement and final evaluations of their first-year programme at Richmond the American International University in London there appear to be two conflicting results. Students who engaged actively and persistently with the programme saw value and use in developing skills and personal qualities that they believed would be useful in helping them achieve their academic and personal goals. Those who did not engage with the programme or engaged only peripherally, saw little value in the first-year experience and struggled to relate to its intended outcomes

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Information-theoretic environment modeling for mobile robot localization

To enhance robotic computational efficiency without degenerating accuracy, it is imperative to fit the right and exact amount of information in its simplest form to the investigated task. This thesis conforms to this reasoning in environment model building and robot localization. It puts forth an approach towards building maps and localizing a mobile robot efficiently with respect to unknown, unstructured and moderately dynamic environments. For this, the environment is modeled on an information-theoretic basis, more specifically in terms of its transmission property. Subsequently, the presented environment model, which does not specifically adhere to classical geometric modeling, succeeds in solving the environment disambiguation effectively. The proposed solution lays out a two-level hierarchical structure for localization. The structure makes use of extracted features, which are stored in two different resolutions in a single hybrid feature-map. This enables dual coarse-topological and fine-geometric localization modalities. The first level in the hierarchy describes the environment topologically, where a defined set of places is described by a probabilistic feature representation. A conditional entropy-based criterion is proposed to quantify the transinformation between the feature and the place domains. This criterion provides a double benefit of pruning the large dimensional feature space, and at the same time selecting the best discriminative features that overcome environment aliasing problems. Features with the highest transinformation are filtered and compressed to form a coarse resolution feature-map (codebook). Localization at this level is conducted through place matching. In the second level of the hierarchy, the map is viewed in high-resolution, as consisting of non-compressed entropy-processed features. These features are additionally tagged with their position information. Given the identified topological place provided by the first level, fine localization corresponding to the second level is executed using feature triangulation. To enhance the triangulation accuracy, redundant features are used and two metric evaluating criteria are employ-ed; one for dynamic features and mismatches detection, and another for feature selection. The proposed approach and methods have been tested in realistic indoor environments using a vision sensor and the Scale Invariant Feature Transform local feature extraction. Through experiments, it is demonstrated that an information-theoretic modeling approach is highly efficient in attaining combined accuracy and computational efficiency performances for localization. It has also been proven that the approach is capable of modeling environments with a high degree of unstructuredness, perceptual aliasing, and dynamic variations (illumination conditions; scene dynamics). The merit of employing this modeling type is that environment features are evaluated quantitatively, while at the same time qualitative conclusions are generated about feature selection and performance in a robot localization task. In this way, the accuracy of localization can be adapted in accordance with the available resources. The experimental results also show that the hybrid topological-metric map provides sufficient information to localize a mobile robot on two scales, independent of the robot motion model. The codebook exhibits fast and accurate topological localization at significant compression ratios. The hierarchical localization framework demonstrates robustness and optimized space and time complexities. This, in turn, provides scalability to large environments application and real-time employment adequacies

On the relationship between student approaches to learning and the use of technology in blended learning environments: a cross-case study analysis

As blended learning becomes ever more pervasive in the context of technological advances claimed to enhance learning, it is important to evaluate the impact of these advances on the quality of student experiences. Early phenomenographic research in academic, face-to-face environments extracted qualitatively different characteristics of student approaches to learning and revealed associations between approaches to learning and the quality of learning outcomes. Relatively little, however, is currently known about the attributes of these approaches in blended learning environments where online facilitation and resources supplement face-to-face teaching. The thesis therefore aims to explore the relationship between student approaches to learning (deep, strategic, surface) and the use of technology in blended settings. The research question was addressed by conducting four case studies across distinct subject areas in a single higher education institution. The findings were analysed within each case study and subsequently across all four studies to expose their relatability. The results show that the existence of a student-centred approach to teaching can induce extended use of selected facilities in the online environment by students who adopt a deep approach. Similarly, a strategic approach can be consistent with higher level of online activity, provided that the teacher approach places significant emphasis on assessment and student achievement. The current cross-case analysis makes a two-fold contribution: firstly, it underlines the relational nature of student approaches to learning when using technology in blended learning settings; secondly, it indicates that teacher approaches to teaching in the face-to-face context can impact more on student approaches to learning online than any features of the technology per se. The implications of these assertions are discussed in terms of disciplinarity, teaching and programme design, and the quality of student experiences in a changing university landscape

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum