Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Learning archetypes as tools of Cybergogy for a 3D educational landscape: a structure for eTeaching in Second Life

This paper considers issues of validity and credibility of eTeaching using a 3D Virtual World as a delivery medium of eLearning pertaining to the transfer of authentic real life skills. It identifies the game like qualities perceived therein, recognising that these very attributes may, when experienced superficially, be a contributing factor to the potential educational demise of the platform. It goes on to examine traditional educational theories in the light of the affordances of a virtual world seeking to adapt and apply them to the construction of a new conceptual framework of a pedagogy reflecting the affordances and understanding of on-line learning which incorporates the implementation of Learning Archetypes (models of activities) to maximise the essence of a virtual world, in as much as it is able to facilitate learning experiences delimited by physical world constraints. It builds upon these ideas to develop a working model of Cybergogy and Learning Archetypes in 3D with a view to making it available to people who wish to demonstrate theoretically robust lesson and course planning. The model is then applied to three examples of eTeaching, developed as Case Studies for the purpose of critically evaluating the model, which is found to be operationally effective, accurate and flexible. Conclusions are drawn that identify the merits and challenges of implementing such a model of Cybergogy into eTeaching and eLearning conducted in Second Life®

Contribution of systems thinking and complex adaptive system attributes to sustainable food production: Example from a climate-smart village

Climate-smart agriculture (CSA) conceptually has the potential to contribute to the sustainable development goals of achieving zero hunger, reducing land degradation, eliminating poverty, tackling climate change, and promoting gender equality. The scaling-up needed to achieve goals of CSA represents a challenge, as it entails understanding synergies between often opposing socioeconomic and environmental priorities and trade-offs over temporal and spatial scales. In this paper, we tested new approaches to support scaling-up of sustainable food production through investigating the contribution of systems thinking as a conceptual approach and complex adaptive system (CAS) attributes as a framework for analysis of CSA. This was done through examining (i) to what extent CSA represents a CAS and (ii) what contribution systems thinking and CAS attributes can make to understanding and scaling-up sustainable food production systems through CSA. The CSA situation was conceptualized through systems thinking sessions with women farmers in the climate-smart village (CSV) of Doggoh-Jirapa, northern Ghana, and was guided by the Distinctions, Systems, Relationships and Perspectives (DSRP) framework. Systems thinking, and CAS attributes provide system-wide understanding of elements, dynamics and trade-offs over temporal and spatial scale in selected agri-food systems. As such it could aid horizontal and vertical scaling-up by informing policy developoment and selection of a context-specific portfolio of technologies and practices at landscape and farm levels to achieve synergies between goals. In this study, systems thinking enabled women farmers in the CSV to identify income-generating and tree planting activities, with desirable simultaneous system-wide impact. The paper calls for further testing of tools, approaches, and methods that enable dynamic systems thinking to inform scaling-up efforts, while embracing the transdisciplinary nature and complexity of CSA as a constituent of the food production system

Architecture of Social Learning and Knowing: Using Social Learning and Knowing Perspectives and Design Thinking to Frame and Create Change in a Workplace Redesign Project

There is a consensus among many theorists and practitioners from the fields of architecture, learning, and organizations that the ability to orchestrate learning and knowledge practices in the workplace creates potential for new and valuable ideas to emerge. However, due to the changing nature of the learning and knowing landscape in the knowledge economy, the role of the physical space pertaining to learning and knowing practices needs to be reexamined. To do so, and to make theories of learning and knowledge relevant to the physical space, this research study (1) uses a strand of theories and perspectives emerged in the past 30 years that frames learning and knowing as social and situated processes as opposed to strictly cognitive functions; and (2) complements the aforementioned theories and perspectives with architects’ and environmental design researchers’ normative views and empirical findings about the physicality of places that are supportive of learning and knowing practices. This theoretical and practical plug-and-play between the two realms of knowledge resulted in the dissertation’s research question: Can we impact boundary mechanisms, as practices or artifacts that can be the source of continuity across various social unites in an organization, through ‘physical space’ and the process of ‘making the physical space’? To address the research question, this dissertation proposes ‘architecture of social learning and knowing’ as a trinary solution comprised of (1) design thinking methodology as a form of action research, rooted in the neo-pragmatic philosophy, for cultivating sustainable change in an organization’s learning and knowledge practices or producing new ones from scratch; (2) a toolset that combines people-space analytics, ethnographic research methods, and ethnographic thick description to not only map and record the change in users’ work practices, but also encourage their engagement as a way of generating insights; and (3) a theoretical lens inspired by social theories of learning and knowing for framing and understanding the change in the organization. This study was conducted in the Milwaukee office of a national architecture firm where the redesign of the workplace was framed as an opportunity to rethink the way work happens. A total of 63 people participated in different phases of a design thinking process to re-imagine their workplace of the future. During the earlier phases of the process, a series of empathy-building exercises and workshops were conducted to generate insights for participatory ideation. After studying the options generated during ideation, a full-scale prototype or mock-up of the new workplace was designed and built in an area as large as 8000 sqf inside the office. Using a combination of sensor-network technology and location tracking, participants’ social networks and spatial behavior were mapped before and after installing the mock-up to study the potential change in the quantity and quality of the organization’s boundary mechanisms. Results from the mapping study showed a significant increase in the employees’ brokering behavior and space utilization as well as change in certain groups of users’ spatial behavior after installing the mock-up. These results were then shared and discussed with a smaller group of participants to make sense of the changes captured during the mapping study. Eventually, the thick description revealed the emergence of four types of peripheral participation as different forms of boundary mechanisms. The first set of findings showed that workplace redesign project had had an impact on participants’ types of interactions and not the quantity of their interactions. In other words, after installing the mock-up, the quantity of interactions did not increase, yet more people manifested brokering behavior. The second set of findings indicated that in cultivating new learning and knowledge practices, the impact of making-process preceded the impact of product. The study showed that some new learning and knowing practices were often negotiated and created during the participatory and emancipatory process of ‘making’ the physical space. It was during this phase that users were empowered to challenge existing practices and were equipped to imagine different ways of conducing work. Consequently, on the methodological level, design thinking was discussed as a refined version of action research with a focus on the neo-pragmatic human inquiry and producing new systems from scratch. Finally, in addition to the framing of the architecture of social learning and knowing, this research advances the social theories of learning and knowing by introducing new constructs, expands the action research method by incorporating the element of design into its framing, and contributes to the literature on the planning and design of work environments by introducing a shift from network view to community view in understanding workplace important outcomes