Trusted Autonomy and Cognitive Cyber Symbiosis: Open Challenges

Abstract

This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human-machine teams are viewed as a network with each node comprising humans (as computational machines) or computers. CoCyS focuses on the architecture and interface of a Trusted Autonomous System. This paper examines these two concepts and seeks to remove ambiguity by introducing formal definitions for these concepts. It then discusses open challenges for TA and CoCyS, that is, whether a team made of humans and machines can work in fluid, seamless harmony.

Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems

“…The design of automation in man-machine systems is mainly focused on cognitive tasks. Taking inspiration from previous work on human factors (Rasmussen, 1986) (Rasmussen, Pejtersen, & Goodstein, 1994), these studies analyse the decision-making process as a sequence of stages, such as situation analysis, value judgement, planning and execution (Abbass, Petraki, Merrick, Harvey, & Barlow, 2016) (Sheridan & Verplank, 1978), which can be engineered with different levels of automation in order to obtain the desired performances, in different contexts and situations.…”

“…It is therefore important to characterize human activities with reference to the main phases that compose this process. Leveraging on the literature on the decision ladder (Lintern, 2010) and task analysis for determining automation levels (Abbass et al, 2016), the several steps of these activities have been summarized in three main phases corresponding to (DI) situation awareness, (DII) analysis and decision making, (DIII) execution. Furthermore, all the activities of reporting, often disregarded by the analysis of decision-making, have been included in a dedicated phase (DIV).…”

Computational Motivation, Autonomy and Trustworthiness: Can We Have It All?

“…The fitness function itself is then constructed by summing intensity functions for motivating sensor data as follows: A synthetic fitness landscape generated with Eq. 16.13 using 9 motivating points at positions (4, 4), (4,18), (4,19), (4,22), (12,4), (12,12), (20,4), (20,12) and (21,22) …”

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

“…Trust is a multi-dimensional concept. Research has established many facets and influential factors [12], [11] for trust dynamics [13]. We will discuss these facets and factors incrementally as each factor requires the list of factors listed before it to have a meaningful evolution of a trusting relationship.…”

“…It is seen to be owned by fields such as psychology [1], [2], social sciences [3], [2], [4], and organization sciences [5], [6], [7], [8]. Technologists and computational scientists have been modelling trust for a few decades [9], [10], [11], [12]. TAS brings this large spectrum of research together in the face of a new challenge: the creation of mutual trust between a human and a machine.…”