The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation

Mobile Intelligent Autonomous Systems

Mobile intelligent autonomous systems (MIAS) is a fast emerging research area. Although it can be regarded as a general R&D area, it is mainly directed towards robotics. Several important subtopics within MIAS research are:(i) perception and reasoning, (ii) mobility and navigation,(iii) haptics and teleoperation, (iv) image fusion/computervision, (v) modelling of manipulators, (vi) hardware/software architectures for planning and behaviour learning leadingto robotic architecture, (vii) vehicle-robot path and motionplanning/control, (viii) human-machine interfaces for interaction between humans and robots, and (ix) application of artificial neural networks (ANNs), fuzzy logic/systems (FLS),probabilistic/approximate reasoning (PAR), Bayesian networks(BN) and genetic algorithms (GA) to the above-mentioned problems. Also, multi-sensor data fusion (MSDF) playsvery crucial role at many levels of the data fusion process:(i) kinematic fusion (position/bearing tracking), (ii) imagefusion (for scene recognition), (iii) information fusion (forbuilding world models), and (iv) decision fusion (for tracking,control actions). The MIAS as a technology is useful for automation of complex tasks, surveillance in a hazardousand hostile environment, human-assistance in very difficultmanual works, medical robotics, hospital systems, autodiagnosticsystems, and many other related civil and military systems. Also, other important research areas for MIAScomprise sensor/actuator modelling, failure management/reconfiguration, scene understanding, knowledge representation, learning and decision-making. Examples ofdynamic systems considered within the MIAS would be:autonomous systems (unmanned ground vehicles, unmannedaerial vehicles, micro/mini air vehicles, and autonomousunder water vehicles), mobile/fixed robotic systems, dexterousmanipulator robots, mining robots, surveillance systems,and networked/multi-robot systems, to name a few.Defence Science Journal, 2010, 60(1), pp.3-4, DOI:http://dx.doi.org/10.14429/dsj.60.9

Commercial software tools for intelligent autonomous systems

This article identifies some of the commercial software tools that can potentially be examined, or relied upon for their techniques, within new EPSRC projects entitled "Reconfigurable Autonomy" and "Distributed Sensing and Control.." awarded and to be undertaken between Liverpool, Southampton and Surrey Universities in the next 4 years. Although such projects strive to produce new techniques of various kinds, the software reviewed here could also influence, shape and help to integrate the algorithmic outcome of all 16 projects awarded within the EPSRC Autonomous and Intelligent Systems programme early 2012. To avoid mis-representation of technololgies provided by the software producer companies listed, most of this review is based on using quotes from original product descriptions

Autonomous power system intelligent diagnosis and control

The Autonomous Power System (APS) project at NASA Lewis Research Center is designed to demonstrate the abilities of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution hardware. Knowledge-based software provides a robust method of control for highly complex space-based power systems that conventional methods do not allow. The project consists of three elements: the Autonomous Power Expert System (APEX) for fault diagnosis and control, the Autonomous Intelligent Power Scheduler (AIPS) to determine system configuration, and power hardware (Brassboard) to simulate a space based power system. The operation of the Autonomous Power System as a whole is described and the responsibilities of the three elements - APEX, AIPS, and Brassboard - are characterized. A discussion of the methodologies used in each element is provided. Future plans are discussed for the growth of the Autonomous Power System

Methodological Flaws in Cognitive Animat Research

In the field of convergence between research in autonomous machine construction and biological systems understanding it is usually argued that building robots for research on auton- omy by replicating extant animals is a valuable strategy for engineering autonomous intelligent systems. In this paper we will address the very issue of animat construction, the ratio- nale behind this, their current implementations and the value they are producing. It will be shown that current activity, as it is done today, is deeply flawed and useless as research in the science and engineering of autonomy

Advanced training systems

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training

Rice-obot 1: An intelligent autonomous mobile robot

The Rice-obot I is the first in a series of Intelligent Autonomous Mobile Robots (IAMRs) being developed at Rice University's Cooperative Intelligent Mobile Robots (CIMR) lab. The Rice-obot I is mainly designed to be a testbed for various robotic and AI techniques, and a platform for developing intelligent control systems for exploratory robots. Researchers present the need for a generalized environment capable of combining all of the control, sensory and knowledge systems of an IAMR. They introduce Lisp-Nodes as such a system, and develop the basic concepts of nodes, messages and classes. Furthermore, they show how the control system of the Rice-obot I is implemented as sub-systems in Lisp-Nodes