ترکیب انسان و ماشین برای آینده
Abstract
1. Introduction
2. Background and related work
3. Methods
4. Results
5. Conclusion, limitations, and future research directions
Appendix A. Supplementary data
References
Abstract
This paper proposes a method to quantify interest. In common terminology, when we engage with an object, e.g. Online Games, Social Networking Websites, Mobile Apps, etc., there is a degree of interest between us and the object. But, owing to the lack of a procedure that can quantify interest, we are unable to tell by how ‘much’ of a factor are we interested in the object. In other words, can we find a number for someone’s interest? In this article, we propose a method that uses the principle of Bayesian Inference to tackle this issue. We formulate the “interest estimation problem” as a state estimation problem to deduce interest (in any object) indirectly from user activity. Activity caused by interest is computed through a subjective objective weighted approach, then using indirect inference rules, we provide numerical estimates of interest. To do that, we model the dynamics of interest through the Ornstein-Uhlenbeck process. To further enhance the base performance, we draw inspiration from Stochastic Volatility models from Finance. Subsequently, drawing upon a self-adapting transfer function, we provide an avant-garde statistical procedure to model the transformation of interest into activity. The individual contributions are then combined and a solution is provided via Particle filters. Validation of the method is done in two ways. 1) Experimentation is performed on real datasets. Through numerical investigation we have found that the method shows good performance. 2) We implement the framework as a Web application and deploy it on an Enterprise Service Bus. The framework has been successfully hosted on a Cloud based Virtualized testbed consisting of several Virtual Machines constructed over XENServer as the underlying hypervisor. Through this experimental setup, we show the efficacy of the proposed algorithm in estimating interest, at much the same time, we demonstrate the viability of the method in practical cloud based deployment scenarios.
Introduction
The last few years have witnessed a tremendous growth in the field of artificial intelligence. In this area, work has been trying to elevate a machine from a mere mechanical device to a fullfledged system capable of behaving intelligently like a human. This vision is indeed one of the most fascinating instances of researchers trying to induce human like intelligence in lifeless entities. Compelled by this foresight, there is a huge body of work dedicated to the study of stimulating human like factors in an artificial environment [1]. Moreover, the state-of-the-art developments in Data Analytics, Human Computer Interaction, and Cloud Computing have laid a foundation for these visions to become a reality, e.g. there are studies that have tried to analyze Human Relationship Dynamics [2], Dynamics of Betrayal [3], and so on. In this paper, we follow this particular line of research and focus our attention on estimating one of the variables closely linked to the human psyche. In particular, we address the issue of a machine automatically estimating the property of human interest. Interest is an intangible mental variable that has attracted substantial research (First paper on interest was published in ۱۸۰۶/۱۹۶۵ [۴]). According to [5], interest is an every day term that specifies a person’s characteristic or perhaps an innate preference towards an entity, subject, or topic in the real world. It has further been specified that interest is a representative of the actions taken by an individual and is an outcome of the desire to engage with an object of one’s interest [6]. Because of its relationship between the psychological and the physical being, interest has become one of most attractive topics of scientific investigation. Though, the initial days witnessed significant efforts in the discipline of Psychology, it grew from a mere mental variable to a concept of particular curiosity in Artificial Intelligence (AI).