Artificial Intelligence

1603 Submissions

[2] viXra:1603.0378 [pdf] submitted on 2016-03-27 16:51:16

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Authors: Hussein A. Abbass, George Leu, Kathryn Merrick
Comments: 32 Pages.

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.
Category: Artificial Intelligence

[1] viXra:1603.0335 [pdf] submitted on 2016-03-23 10:04:45

Conditional Deng Entropy, Joint Deng Entropy and Generalized Mutual Information

Authors: Haoyang Zheng, Yong Deng
Comments: 16 Pages.

Shannon entropy, conditional entropy, joint entropy and mutual information, can estimate the chaotic level of information. However, these methods could only handle certain situations. Based on Deng entropy, this paper introduces multiple new entropy to estimate entropy under multiple interactive uncertain information: conditional Deng entropy is used to calculate entropy under conditional basic belief assignment; joint Deng entropy could calculate entropy by applying joint basic belief assignment distribution; generalized mutual information is applied to estimate the uncertainty of information under knowing another information. Numerical examples are used for illustrating the function of new entropy in the end.
Category: Artificial Intelligence