16 research outputs found
Binary credal classification under sparsity constraints.
Binary classification is a well known problem in statistics. Besides classical methods, several techniques such as the naive credal classifier (for categorical data) and imprecise logistic regression (for continuous data) have been proposed to handle sparse data. However, a convincing approach to the classification problem in high dimensional problems (i.e., when the number of attributes is larger than the number of observations) is yet to be explored in the context of imprecise probability. In this article, we propose a sensitivity analysis based on penalised logistic regression scheme that works as binary classifier for high dimensional cases. We use an approach based on a set of likelihood functions (i.e. an imprecise likelihood, if you like), that assigns a set of weights to the attributes, to ensure a robust selection of the important attributes, whilst training the model at the same time, all in one fell swoop. We do a sensitivity analysis on the weights of the penalty term resulting in a set of sparse constraints which helps to identify imprecision in the dataset
On the Perception of Difficulty: Differences between Humans and AI
With the increased adoption of artificial intelligence (AI) in industry and
society, effective human-AI interaction systems are becoming increasingly
important. A central challenge in the interaction of humans with AI is the
estimation of difficulty for human and AI agents for single task
instances.These estimations are crucial to evaluate each agent's capabilities
and, thus, required to facilitate effective collaboration. So far, research in
the field of human-AI interaction estimates the perceived difficulty of humans
and AI independently from each other. However, the effective interaction of
human and AI agents depends on metrics that accurately reflect each agent's
perceived difficulty in achieving valuable outcomes. Research to date has not
yet adequately examined the differences in the perceived difficulty of humans
and AI. Thus, this work reviews recent research on the perceived difficulty in
human-AI interaction and contributing factors to consistently compare each
agent's perceived difficulty, e.g., creating the same prerequisites.
Furthermore, we present an experimental design to thoroughly examine the
perceived difficulty of both agents and contribute to a better understanding of
the design of such systems
On the Perception of Difficulty: Differences between Humans and AI
With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances. These estimations are crucial to evaluate each agent\u27s capabilities and, thus, required to facilitate effective collaboration. So far, research in the field of human-AI interaction estimates the perceived difficulty of humans and AI independently from each other. However, the effective interaction of human and AI agents depends on metrics that accurately reflect each agent\u27s perceived difficulty in achieving valuable outcomes. Research to date has not yet adequately examined the differences in the perceived difficulty of humans and AI. Thus, this work reviews recent research on the perceived difficulty in human-AI interaction and contributing factors to consistently compare each agent\u27s perceived difficulty, e.g., creating the same prerequisites. Furthermore, we present an experimental design to thoroughly examine the perceived difficulty of both agents and contribute to a better understanding of the design of such systems
Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education
In the rapidly evolving landscape of education, digital technologies have
repeatedly disrupted traditional pedagogical methods. This paper explores the
latest of these disruptions: the potential integration of large language models
(LLMs) and chatbots into graduate engineering education. We begin by tracing
historical and technological disruptions to provide context and then introduce
key terms such as machine learning and deep learning and the underlying
mechanisms of recent advancements, namely attention/transformer models and
graphics processing units. The heart of our investigation lies in the
application of an LLM-based chatbot in a graduate fluid mechanics course. We
developed a question bank from the course material and assessed the chatbot's
ability to provide accurate, insightful responses. The results are encouraging,
demonstrating not only the bot's ability to effectively answer complex
questions but also the potential advantages of chatbot usage in the classroom,
such as the promotion of self-paced learning, the provision of instantaneous
feedback, and the reduction of instructors' workload. The study also examines
the transformative effect of intelligent prompting on enhancing the chatbot's
performance. Furthermore, we demonstrate how powerful plugins like Wolfram
Alpha for mathematical problem-solving and code interpretation can
significantly extend the chatbot's capabilities, transforming it into a
comprehensive educational tool. While acknowledging the challenges and ethical
implications surrounding the use of such AI models in education, we advocate
for a balanced approach. The use of LLMs and chatbots in graduate education can
be greatly beneficial but requires ongoing evaluation and adaptation to ensure
ethical and efficient use.Comment: 44 pages, 16 figures, preprint for PLOS ON
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations
The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains