Search results for "Machine learning."
showing 10 items of 1455 documents
Structural Knowledge Extraction from Mobility Data
2016
Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …
Modeling and 'smart' prototyping human-in-the-loop interactions for AmI environments
2021
[EN] Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term. Human-system integration introduces an important number of challenges and problems that have to be solved. On the one hand, humans should interact with systems even in those situations where their attentional, cognitive, and physical resources are limited in order to perform the interaction.…
Stochastic model predicts evolving preferences in the Iowa gambling task
2014
Learning under uncertainty is a common task that people face in their daily life. This process relies on the cognitive ability to adjust behavior to environmental demands. Although the biological underpinnings of those cognitive processes have been extensively studied, there has been little work in formal models seeking to capture the fundamental dynamic of learning under uncertainty. In the present work, we aimed to understand the basic cognitive mechanisms of outcome processing involved in decisions under uncertainty and to evaluate the relevance of previous experiences in enhancing learning processes within such uncertain context. We propose a formal model that emulates the behavior of p…
Learning Behavioral Rules from Multi-Agent Simulations for Optimizing Hospital Processes
2021
Hospital processes are getting more and more complex, starting from the creation of therapy plans over intra-hospital transportation up to the coordination of patients and staff members. In this paper, multi-agent simulations will be used to optimize the coordination of different kinds of individuals (like patients and doctors) in a hospital process. But instead of providing results in form of optimized schedules, here, behavioral rules for the different individuals will be learned from the simulations, that can be exploited by the individuals to optimize the overall process. As a proof-of-concept, the approach will be demonstrated in different variants of a hospital optimization scenario, …
The Art of Bootstrapping
2020
Language workbenches are used to define languages using appropriate meta-languages. Meta-languages are also just languages and can, therefore, be defined using themselves. The process is called bootstrapping and is often difficult to achieve.
The Influence of Context-Based Complexity on Decision Processes
2011
In this chapter, we present an empirical study which investigates the influence of context-based complexity on decision processes.1 To determine context-based complexity accurately, we measure each subject’s preferences individually with two advanced techniques from marketing research: choice-based conjoint analysis (CBC, Haaijer and Wedel 2007) and pairwise-comparison-based preference measurement (PCPM, Scholz et al. 2010), rather than relying on less precise estimates of preferences. Furthermore, we use eye tracking to trace the process of information acquisition precisely. Our results show that low context-based complexity leads to less information acquisition and more alternative-wise s…
Selection Task and Computer-Based Feedback to Improve the Searching Process in Task-Oriented Reading Situations
2015
Adaptive feedback has showed to be effective to enhance strategic reading behaviors and performance in task-oriented reading situations, but it is difficult to be implemented in classroom environments. Computer-based systems allow overcoming these challenges. We conducted an experiment in which secondary-school students read two texts, answered comprehension questions and selected relevant text information while receiving automatic feedback about selection accuracy and performance. Two experimental conditions were designed to assess the effects of feedback and selection attempts. Then, students perform a transfer task without any of these elements. We found that one-attempt and two-attempt …
MAVIE-Lab Sports: a mHealth for Injury Prevention and Risk Management in Sport
2018
International audience; Smart-phones technology and the development of mHealth (Mobile Health) applications offer an opportunity to design intervention tools to influence health behavior changes. The MAVIE-Lab is a mHealth application including a DSS (Desicion Support System) to assist in the personalized evaluation of HLIs (Home, Leisure and Sport Injuries) risk and to promote the adoption of prevention measures. MAVIE-Lab Sports will be the first module of the mobile application. The purpose of this PhD project is to improve a particular module of MAVIE-Lab, devoted to sports (MAVIE-Lab Sports), in different aspects: statistical modeling, design and ergonomics. It also aims to evaluate sy…
The extensive and intensive margins of Spanish trade
2011
Recent empirical research highlights that differences in trade flows across countries, products and years are governed by two margins: the intensive margin and the extensive margin. The analysis of the relative contribution of each margin is very important to determine which policies can be more efficient to foster trade at the aggregate, geographic, product or firm level. We use the whole universe of firm level transaction data to analyse the relative contribution of these margins to changes in Spanish trade flows during the 1997–2007 period. We first apply the methodology proposed by Bernard et al. (2009) to decompose trade variation over time into three components: net entry of firms, pr…
An adaptive approach to learning the preferences of users in a social network using weak estimators
2012
Published version of an article in the journal: Journal of Information Processing Systems. Also available from the publisher at: http://dx.doi.org/10.3745/JIPS.2012.8.2.191 - Open Access Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked i…