Abstract This chapter considers the application of artificial intelligence to policy-making in lifelong learning, based on Enliven project’s development a prototype Intelligent Decision Support System (IDSS) focused young people Not Employment, Education or Training (NEETs). Providing repository information about existing projects (particularly aimed at benefitting groups disadvantaged terms ge...

Many decision-making problems can be formulated in the framework of a partially observable Markov decision process (POMDP) [5]. The optimality of decisions relies on the accuracy of the POMDP model as well as the policy found for the model. In many applications the model is unknown and learned empirically based on experience, and building a model is just as difficult as finding the associated p...

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on laten...

background despite the significant role “values” play in decision-making no definition or attributes regarding the concept have been provided in health policy-making. this study aimed to clarify the defining attributes of a concept of value and its irrelevant structures in health policy-making. we anticipate our findings will help reduce the semantic ambiguities associated with the use of “valu...

integrated urban management means adopting a holistic approach to urban management and designing appropriate mechanisms for this approach’s requirements in practice. the integration of the urban activities brings together organizational , structural and institutional activities and action in which the urban development is supported. in order to achieve integration, it is essential to identify f...

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...