2,989 research outputs found
Inquiry Project Laboratory: The Collaborative Problem Solving and Critical Thinking on Laboratory
Collaborative problem-solving is one of the important soft skills in project-based laboratory activities to equip students with critical thinking skills. A study that systematically reviews literature relevant to the successful implementation of the Inquiry Project Laboratory (IPro-Lab) which is carried out in 5 main stages. A systematic review of relevant qualitative research findings using an integrative approach to gain a deeper understanding (meta-synthesis) through summarization techniques (meta-aggregation). The review stage begins with extracting relevant studies, identifying important findings, categorizing findings, and developing a conceptual framework. The studies and reviews are focused on the success of the IPro-Lab practicum in equipping critical thinking, creative, collaboration and communication skills. In addition, it was also found that the successful track record of the IPro-Lab practicum has been tested and valid to support the formation of scientific thinking, solving complex problems and the needs of the 21st century. IPro-Lab also provides opportunities for the self-development of teachers so that they are more innovative, creative and structure
Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)
1st Doctoral Consortium at the European Conference on
Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020
Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish
in diverse real-world applications, understanding these black box models has
become paramount. In response, Explainable AI (XAI) has emerged as a field of
research with practical and ethical benefits across various domains. This paper
not only highlights the advancements in XAI and its application in real-world
scenarios but also addresses the ongoing challenges within XAI, emphasizing the
need for broader perspectives and collaborative efforts. We bring together
experts from diverse fields to identify open problems, striving to synchronize
research agendas and accelerate XAI in practical applications. By fostering
collaborative discussion and interdisciplinary cooperation, we aim to propel
XAI forward, contributing to its continued success. Our goal is to put forward
a comprehensive proposal for advancing XAI. To achieve this goal, we present a
manifesto of 27 open problems categorized into nine categories. These
challenges encapsulate the complexities and nuances of XAI and offer a road map
for future research. For each problem, we provide promising research directions
in the hope of harnessing the collective intelligence of interested
stakeholders
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
False textual information detection, a deep learning approach
Many approaches exist for analysing fact checking for fake news identification, which is the focus of this thesis. Current approaches still perform badly on a large scale due to a lack of authority, or insufficient evidence, or in certain cases reliance on a single piece of evidence.
To address the lack of evidence and the inability of models to generalise across domains, we propose a style-aware model for detecting false information and improving existing performance. We discovered that our model was effective at detecting false information when we evaluated its generalisation ability using news articles and Twitter corpora.
We then propose to improve fact checking performance by incorporating warrants. We developed a highly efficient prediction model based on the results and demonstrated that incorporating is beneficial for fact checking. Due to a lack of external warrant data, we develop a novel model for generating warrants that aid in determining the credibility of a claim. The results indicate that when a pre-trained language model is combined with a multi-agent model, high-quality, diverse warrants are generated that contribute to task performance improvement.
To resolve a biased opinion and making rational judgments, we propose a model that can generate multiple perspectives on the claim. Experiments confirm that our Perspectives Generation model allows for the generation of diverse perspectives with a higher degree of quality and diversity than any other baseline model.
Additionally, we propose to improve the model's detection capability by generating an explainable alternative factual claim assisting the reader in identifying subtle issues that result in factual errors. The examination demonstrates that it does indeed increase the veracity of the claim.
Finally, current research has focused on stance detection and fact checking separately, we propose a unified model that integrates both tasks. Classification results demonstrate that our proposed model outperforms state-of-the-art methods
Modelling learning behaviour of intelligent agents using UML 2.0
This thesis aims to explore and demonstrate the ability of the new standard of
structural and behavioural components in Unified Modelling Language (UML 2.0 / 2004)
to model the learning behaviour of Intelligent Agents. The thesis adopts the research
direction that views agent-oriented systems as an extension to object-oriented systems. In
view of the fact that UML has been the de facto standard for modelling object-oriented
systems, this thesis concentrates on exploring such modelling potential with Intelligent
Agent-oriented systems. Intelligent Agents are Agents that have the capability to learn and
reach agreement with other Agents or users. The research focuses on modelling the
learning behaviour of a single Intelligent Agent, as it is the core of multi-agent systems.
During the writing of the thesis, the only work done to use UML 2.0 to model
structural components of Agents was from the Foundation for Intelligent Physical Agent
(FIPA). The research builds upon, explores, and utilises this work and provides further
development to model the structural components of learning behaviour of Intelligent
Agents. The research also shows the ability of UML version 2.0 behaviour diagrams,
namely activity diagrams and sequence diagrams, to model the learning behaviour of
Intelligent Agents that use learning from observation and discovery as well as learning
from examples of strategies. The research also evaluates if UML 2.0 state machine
diagrams can model specific reinforcement learning algorithms, namely dynamic
programming, Monte Carlo, and temporal difference algorithms. The thesis includes user
guides of UML 2.0 activity, sequence, and state machine diagrams to allow researchers in
agent-oriented systems to use the UML 2.0 diagrams in modelling the learning components
of Intelligent Agents.
The capacity for learning is a crucial feature of Intelligent Agents. The research
identifies different learning components required to model the learning behaviour of
Intelligent Agents such as learning goals, learning strategies, and learning feedback
methods. In recent years, the Agent-oriented research has been geared towards the agency
dimension of Intelligent Agents. Thus, there is a need to conduct more research on the
intelligence dimension of Intelligent Agents, such as negotiation and argumentation skills.
The research shows that behavioural components of UML 2.0 are capable of
modelling the learning behaviour of Intelligent Agents while structural components of
UML 2.0 need extension to cover structural requirements of Agents and Intelligent Agents.
UML 2.0 has an extension mechanism to fulfil Agents and Intelligent Agents for such
requirements. This thesis will lead to increasing interest in the intelligence dimension
rather than the agency dimension of Intelligent Agents, and pave the way for objectoriented
methodologies to shift more easily to paradigms of Intelligent Agent-oriented
systems.The British
Council, the University of Plymouth and the Arab-British Chamber Charitable Foundation
Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives
Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future
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