521 research outputs found
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Matheuristics:survey and synthesis
In integer programming and combinatorial optimisation, people use the term matheuristics to refer to methods that are heuristic in nature, but draw on concepts from the literature on exact methods. We survey the literature on this topic, with a particular emphasis on matheuristics that yield both primal and dual bounds (i.e., upper and lower bounds in the case of a minimisation problem). We also make some comments about possible future developments
DEM Timetabling Project ? Development/implementation of an algorithm to support the creation of timetables
This work presents the development of an algorithm to support the process of creating academic
timetables, specifically aimed at solving the University Course Timetabling Problem. To date, this
problem is solved manually in Instituto Superior de Engenharia do Porto, where professors and
engineers face the complex task of creating timetables based on schedules from previous years.
The proposed solution aimed to support the process of creating timetables at ISEP, reducing the
time and human resources required for this task. The developed algorithm uses an integer
programming approach and can consider a variety of constraints and preferences of both faculty
and students. It was designed to adapt and optimize the timetable creation process as needs evolve,
ensuring future demands can be easily accommodated.
The algorithm implementation was based on the Python programming language and the Pyomo
library, offering a flexible and efficient approach to optimizing resource allocation. Additionally, the
system is designed to import data from real-world sources, simplifying the integration of crucial
information.
The result assigned all the 128 one-hour classes among the week, presenting the faculty member,
the classroom assigned and the type of class according to each course. This research presents
feasible solutions that need improvement on the demanding conditions and restrictions imposed
by ISEP. The computational results obtained offered a significantly decrease in the time resource
used, compared to the manual work previously done
Recommended from our members
Designing for Publicness: Partnership, Publicness and Participatory Design of Free Online Learning Materials
The thesis uses the design free open online learning materials (Open Educational Resources – OER and Open Educational Practices – OEP) in Higher Education (HE)partnerships with intermediary organisations (IOs) to explore issues in design and publicness. Using critical approaches to publicness and speculative, participatory and critical design theory, it looks inside learning partnerships to gain a deeper understanding of learning design practice. The objective is to inform learning design practice within OER/OEP at a practical and theoretical level and contribute to a broader understanding of design in organisations.
The research stems from long-term engagement with opening up design practice as part of designing open learning materials. What emerges through these partnerships is that financial pressures appear to drive IOs to use free online learning to mitigate the loss of capacity and maintain the learning provisions associated with their mission. However, it became clear that something more was happening in these design spaces than making learning materials as design questions around who is the course for reached into the organisations operationally and strategically.
To address the central question around how design questions become strategic questions the thesis follows a series of learning partnerships with IOs. It argues they open up design because they are connected to "a public" learning providers could not otherwise reach. The research methods are structured a design typology which
is itself is an outcome of the thesis, suggesting design questions involve:
• Inquiry in design which are auto-ethnographic reflections on design practice;
• Inquiry for design, action research to support design practice including interviews, workshops and participant observation;
• Inquiry through design, the combination of auto-ethnographic and action research to develop "designerly ways knowing".
The thesis shows how "a public" is folded into the design process and proposes a way for researchers and practitioners to explore this. It finds blurring boundaries can create a tension between listening to and reflecting the voice of learners in the design process and the organisation's values where those values challenge existing social and structural relations. The thesis argues design surfaces existing strategic questions about the publicness of the organisation and the partnership.
The thesis highlights the practical insights that arise from focusing on critical theories of publicness and design. First setting out an approach for examining design practice, focusing on how "a public" is imagined and brought into being through the design process. Second, by highlighting dilemmas for learning partnerships that emerge from the thesis. The outcomes are relevant to academics and practitioners within HE interested in the management of online learning design
those engaged in partnership work, and people interested in design and organisations
Knowledge extraction from unstructured data
Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models
Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort
Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen
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