879 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
Congress\u27s Anti-Removal Power
Statutory restrictions on presidential removal of agency leadership enable agencies to act independently from the White House. Yet since 2020, the U.S. Supreme Court has held two times that such restrictions are unconstitutional precisely because they prevent the President from controlling policymaking within the executive branch. Recognizing that a supermajority of the Justices now appears to reject or at least limit the principle from Humphreyâs Executor that Congress may prevent the President from removing agency officials based on policy disagreement, scholars increasingly predict that the Court will soon further weaken agency independence if not jettison it altogether.
This Article challenges that conventional wisdom. True, the Court is skeptical of statutory restrictions on the Presidentâs removal power. But statutory removal restrictions are not the only tools available to achieve agency independence. Instead, the Constitution provides Congress with what we dub the anti-removal powerâthe ability to discourage the White House from using its removal power. For example, because the Senate has plenary authority under the Appointments Clause to withhold its consent for executive branch nominees, there is no guarantee that the Senate will confirm a replacement if the President removes the incumbent for a poor reason. As Alexander Hamilton explained, the âsilent operationâ of that uncertainty often allows Congress to prevent removal in the first place. Similarly, James Madison acknowledged during the Decision of 1789 that although the Constitution (in his view) forbids statutory removal restrictions, Congress has means to make removal costly for the President, which should âexcite serious reflections beforehand in the mind of any man who may fill the presidential chair.â
Importantly, moreover, Congress can strengthen its anti-removal power by, among other things, enacting reason-giving requirements, raising cloture thresholds, and preventing presidential evasion of the Appointments Clause. Using history, real-world examples, and game theory, we demonstrate how Congress can create a level of agency independence without the use of statutory removal restrictions. We also explain why Congressâs anti-removal power has advantages over statutory removal restrictions, including a surer constitutional footing and enhanced accountability: Both the President and Congress face political consequences for how they exercise their removal and anti-removal powers. Finally, we offer Congress a path forward to restore some agency independence and limit judicial challenges to agency structures
Handbook Transdisciplinary Learning
What is transdisciplinarity - and what are its methods? How does a living lab work? What is the purpose of citizen science, student-organized teaching and cooperative education? This handbook unpacks key terms and concepts to describe the range of transdisciplinary learning in the context of academic education. Transdisciplinary learning turns out to be a comprehensive innovation process in response to the major global challenges such as climate change, urbanization or migration. A reference work for students, lecturers, scientists, and anyone wanting to understand the profound changes in higher education
Borhan: A Novel System for Prioritized Default Logic
Prioritized Default Logic presents an optimal solution for addressing
real-world problems characterized by incomplete information and the need to
establish preferences among diverse scenarios. Although it has reached great
success in the theoretical aspect, its practical implementation has received
less attention. In this article, we introduce Borhan, a system designed and
created for prioritized default logic reasoning. To create an effective system,
we have refined existing default logic definitions, including the extension
concept, and introduced novel concepts. In addition to its theoretical merits,
Borhan proves its practical utility by efficiently addressing a range of
prioritized default logic problems. In addition, one of the advantages of our
system is its ability to both store and report the explanation path for any
inferred triple, enhancing transparency and interpretability. Borhan is offered
as an open-source system, implemented in Python, and even offers a simplified
Java version as a plugin for the Protege ontology editor. Borhan thus
represents a significant step forward in bridging the gap between the
theoretical foundations of default logic and its real-world applications
A Behavioural Decision-Making Framework For Agent-Based Models
In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making
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
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A Mazzinian Inspired Moral Form of Partiality: <i>MfP</i> Patriotism
The Brexit vote in the UK revealed two tribal views that conceive partiality, change, identity and social trust differently. These are not new and represent two world views of morality, one global, the Anywheres, the other local, the Somewheres. My project seeks to bridge the divide between them by developing a moral form of partiality, best described as a moderate form of patriotism, which I call MfP. Avoiding the standard philosophical approach to justify MfP, I make two interdependent claims. First, my patria and polity, the object of MfP, is a Korsgaardian conditional value that is objectively good when the condition of its objective goodness is satisfied. Secondly, the strongly normative practical identity of a MfP-patriot is the best identity to act to satisfy the condition of objective goodness. MfP is about satisfying the desire for human flourishing by acting from the universal principle to reject indifference and neglect in the workable, cooperative, social and political system of my patria and polity. The MfP-patriot is focused on acting to foster and sustain the social fabric, secure the common good and protect the environment. MfP is inspired by the patriotic thoughts of Giuseppe Mazzini who was a nineteenth century Italian revolutionary and prolific writer. It helps make our minds bigger so that we extend our circle of concern to others. Being a MfP-patriot is about sharing in the labour for the good of humanity. MfP is underpinned by a disposition I call âconnected, poetically present and compassionateâ conservatism; cppc-conservatism. MfP is justified by our rational nature. It is a moral form of partiality that marks it out as worthy of choice and makes the world a better place
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Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group
This is the Proceedings of the 33rd Annual Workshop of the Psychology of Programming Interest Group (PPIG). This was the first PPIG to be held physically since 2019, following the two online-only PPIGs in 2020 and 2021, both during the Covid pandemic. It was also the first PPIG conference to be designed specifically for hybrid attendance. Reflecting the theme, it was hosted by Music Computing Lab at the Open University in Milton Keynes
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