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Probabilistic Quantitative temporal Constraints:Representing,reasoning,and Query Answering

Journal of Electronic Science and Technology 2018年第1期16卷 1-10页

作者： Paolo Terenziani Antonella Andolina Dipartimento di Scienze e Innovazione Tecnologica University of Eastern PiedmontAlessandria 15121 Istituto Tecnico Commerciale Sommeiller Torino 10129

In many applications and domains,temporal constraints between actions, and their probabilities play an important role. We propose the first approach in the literature coping with probabilistic quantitative constraints. To achieve such a challenging goal, we extend the widely used simple temporal problem(STP) framework to consider ***,we propose i) a formal representation of probabilistic quantitative constraints, ii) an algorithm,based on the operations of intersection and composition,for the propagation of such temporal constraints, and iii) facilities to support query answering on a set of such constraints. As a result, we provide users with the first homogeneous method supporting the treatment(representing,reasoning,and querying) of probabilistic quantitative constraints, as required by many applications and domains.

关键词： Artificial intelligence(AI) probabilities query answering quantitative temporal constraints temporal reasoning

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Using AI and Precision Nutrition to Support Brain Health during Aging

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Advances in Aging Research 2024年第5期13卷 85-106页

作者： Sabira Arefin Gideon Kipkoech Global Healthcare Leadership Program Harvard University Boston USA Independent Researcher University of Embu Nairobi Kenya

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they le

关键词： Artificial Intelligence (AI) Precision Nutrition Brain Health Aging Research Gerontology Cognitive Functions temporal reasoning Medication Adherence Electronic Health Records (EHRs) Machine Learning (ML) Healthcare Technology

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