An Ontology-based Approach to Student Skills in Multiagent e-Learning Systems

The main idea of our approach is that the domain ontology is not only the instrument of learning but an object of examining student skills. We propose for students to build the domain ontology of examine discipline and then compare it with etalon one. Analysis of student mistakes allows to propose them personalized recommendations and to improve the course materials in general. For knowledge interoperability we apply Semantic Web technologies. Application of agent-based technologies in e-learning provides the personification of students and tutors and saved all users from the routine operations

Initiating organizational memories using ontology network analysis

One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory

Real “Smart Cities”: Insights from Civitas PROSPERITY

A city does not need to be smart, but to allow people be, behave, live and work smart(er). Furthermore, smart should not be necessarily equalled to high technology, but to the sound management, communication and use of available resources, be they tangible or intangible. Anyway our evolution cannot be limited to technology, even if the latter has become unavoidable. If not accompanied by a comprehensive perspective and coherent management, technology may rather block than facilitate resilience and sustainable urban development. Not always the most technically advanced and expensive solutions are the best (most effective) ones or frequently they cannot work alone, needing to be complemented by soft / lower-cost measures. Moreover,even if the actual “smart city” paradigm would be accepted, there do not seem to be enough resources (especially primary ones) to provide high-tech for everybody (WWF, 2018). In this case high-tech might be replaced by smart-tech staying for innovative solutions of best coping with given situations no matter the level of scientific, cultural, economic and behavioural advancement. These are some of the conclusions of a recent ongoing project funded through Horizon 2020, pleading for a global integrated perspective and providing the appropriate tools to sustainably shape and enhance it. Being built in response to the challenge “Real Smart Cities. Best practices and concepts for the future”, the present contribution informs on how Civitas PROSPERITY (applied research project) integrated these principles and produced innovation in the field of Sustainable Urban Mobility Plans (SUMP). The focus is on bright solutions that can be equally extended and applied in other fields of urban management beyond mobility, such as energy, land-use, cultural heritage etc

Ontological Engineering and Mapping in Multiagent Systems Development

Multiagent systems have received much attention in recent years due to their advantages in complex, distributed environments. Previous work at the Air Force Institute of Technology has developed a methodology for analyzing, designing, and developing multiagent systems, called Multiagent Systems Engineering (MaSE). MaSE currently does not address the information domain of the system, which is an integral part of designing proper system execution. This research extends the MaSE methodology to include the use of ontologies for information domain specification. The extensions allow the designer to specify information flow by using objects from the ontology as parameters in agent conversations. The developer can then ensure system functionality by verifying that each agent has the information required to accomplish the system goals. To fully describe the system design, the developer must describe the relationships between the system ontology and any agent component ontologies. This research also developed a ranking model to assist the user with creating such mappings, to show the relationships between the objects in the ontologies

Intelligent Agents as a Modeling Paradigm

Intelligent software agents have been used in many applications because they provide useful integrated features that are not available in “traditional” types of software (e.g., abilities to sense the environment, reason, and interact with other agents). Although the usefulness of agents is in having such capabilities, methods and tools for developing them have focused on practical physical representation rather than accurate conceptualizations of these functions. However, intelligent agents should closely mimic aspects of the environment in which they operate. In the physical sciences, a conceptual model of a problem can lead to better theories and explanations about the area. Therefore, we ask, can an intelligent agent conceptual framework, properly defined, be used to model complex interactions in various social science disciplines? The constructs used in the implementation of intelligent agents may not be appropriate at the conceptual level, as they refer to software concepts rather than to application domain concepts. We propose to use a combina- tion of the systems approach and Bunge’s ontology as adapted to information systems, to guide us in defining intelligent agent concepts. The systems approach will be used to define the components of the intelligent agents and ontology will be used to understand the configurations and interrelationships between the components. We will then provide a graphical representation of these concepts for modeling purposes. As a proof of concept for the proposed conceptual model, we applied it to a marketing problem and imple- mented it in an agent-based programming environment. Using the conceptual model, the user was able to quickly visualize the complex interactions of the agents. The use of the conceptual representation even sparked an investigation of previously neglected causal factors which led to a better understanding of the problem. Therefore, our intelligent agent framework can graphically model phenomena in the social sciences. This work also provides a theoretically driven concept of intelligent agent components and a definition of the inter- relationships between these concepts. Further research avenues are also discussed

Towards engineering ontologies for cognitive profiling of agents on the semantic web

Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

Models of Interaction as a Grounding for Peer to Peer Knowledge Sharing

Most current attempts to achieve reliable knowledge sharing on a large scale have relied on pre-engineering of content and supply services. This, like traditional knowledge engineering, does not by itself scale to large, open, peer to peer systems because the cost of being precise about the absolute semantics of services and their knowledge rises rapidly as more services participate. We describe how to break out of this deadlock by focusing on semantics related to interaction and using this to avoid dependency on a priori semantic agreement; instead making semantic commitments incrementally at run time. Our method is based on interaction models that are mobile in the sense that they may be transferred to other components, this being a mechanism for service composition and for coalition formation. By shifting the emphasis to interaction (the details of which may be hidden from users) we can obtain knowledge sharing of sufficient quality for sustainable communities of practice without the barrier of complex meta-data provision prior to community formation

Computational estimate visualisation and evaluation of agent classified rules learning system

Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm in the estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results, it is gathered that the system has performed according to its design specification