scholarly journals BEHAVIOR PREDICTION FOR DECISION AND CONTROL IN COGNITIVE AUTONOMOUS SYSTEMS

2013 ◽  
Vol 09 (03) ◽  
pp. 263-271 ◽  
Author(s):  
ASOK RAY ◽  
SHASHI PHOHA ◽  
SOUMIK SARKAR
Keyword(s):  
Domain Knowledge ◽  
Autonomous Systems ◽  
Quantum Probability ◽  
Machine Intelligence ◽  
Human Cognition ◽  
Mathematical Framework ◽  
Behavior Prediction ◽  
Human Operators ◽  
And Control ◽  
Innovative Concept

This paper presents an innovative concept of behavior prediction for decision and control in cognitive autonomous systems. The objective is to coordinate human–machine collaboration such that human operators can assess and enable autonomous systems to utilize their experiential and unmodeled domain knowledge and perception for mission execution. The concept of quantum probability is proposed to construct a unified mathematical framework for interfacing between models of human cognition and machine intelligence.

Blind Brains and Moral Machines: Neuroscience and Autonomous Weapon Systems

2021 ◽  
pp. 203-216
Author(s):  
Nicholas G. Evans
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Human Cognition ◽  
Post Traumatic Stress ◽  
Moral Machines ◽  
Neuroscience Research ◽  
Primary Attraction ◽  
Open Question ◽  
Second Category ◽  
And Control

While the majority of neuroscience research promises novel therapies for treating dementia and post-traumatic stress disorder, among others, a lesser-known branch of neuroscientific research informs the construction of artificial intelligence inspired by human neurophysiology. For those concerned with the normative implications of autonomous weapons systems (AWS), however, a tension arises between the primary attraction of AWS, their theoretic capacity to make better decisions in armed conflict, and the relatively low-hanging fruit of modeling machine intelligence on the very thing that causes humans to make (relatively) bad decisions—the human brain. This chapter examines human cognition as a model for machine intelligence, and some of its implications for AWS development. It first outlines recent neuroscience developments as drivers for advances in artificial intelligence. This chapter then expands on a key distinction for the ethics of AWS: poor normative decisions that are a function of poor judgments given a certain set of inputs, and poor normative decisions that are a function of poor sets of inputs. It argues that given that there are cases in the second category of decisions in which we judge humans to have acted wrongly, we should likewise judge AWS platforms. Further, while an AWS may in principle outperform humans in the former, it is an open question of design whether they can outperform humans in the latter. Finally, this chapter then discusses what this means for the design and control of, and ultimately liability for AWS behavior, and sources of inspiration for the alternate design of AWS platforms.

Design and Control of Drones

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mark W. Mueller ◽  
Seung Jae Lee ◽  
Raffaello D’Andrea
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Recent Progress ◽  
Scaling Laws ◽  
Autonomous Systems ◽  
Annual Review ◽  
Publication Date ◽  
Trade Offs ◽  
Active Research ◽  
And Control

The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Mobile Laboratory Model for Next-Generation Heterogeneous Wireless Systems

Robotics ◽  
2013 ◽  
pp. 1644-1661
Author(s):  
Ibrahima Ngom ◽  
Hamadou Saliah-Hassane ◽  
Claude Lishou
Keyword(s):  
Mobile Devices ◽  
Wireless Systems ◽  
Integrated System ◽  
Laboratory Model ◽  
Mobile Laboratory ◽  
Next Generation ◽  
Distributed Communication ◽  
Human Operators ◽  
Heterogeneous Wireless Systems ◽  
And Control

Failure to integrate heterogeneous wireless systems generally makes it difficult, if not impossible, for the continuation of remote working or remote experiments when human operators and equipment coexist through networks in a collaborative environment. Mobile laboratories using ubiquitous mobile communication for next-generation heterogeneous wireless systems have prospects for increasing the operation of distributed communication and mobile ubiquitous systems. All “technology assessors” concur that tomorrow's society will have access to smart objects (mobile devices or apparatuses, mobile equipment, e.g. robots) that contain “programs” that will assist with communication in everyday life. However one of the tomorrow’s challenges will consist of programming those objects to cooperate with and control telecommunications technologies. For a Mobile Laboratory to ensure consistent mobility in an environment, it must combine various wireless networks as a single integrated system. In this chapter we propose a Mobile Laboratory Model with mobile devices that take advantage of multiple mobile gateways by using Internet Protocol (IP) as the interconnection protocol to achieve the objective stated above.

Mobile Laboratory Model for Next-Generation Heterogeneous Wireless Systems

2012 ◽  
pp. 511-528 ◽  
Author(s):  
Ibrahima Ngom ◽  
Hamadou Saliah-Hassane ◽  
Claude Lishou
Keyword(s):  
Mobile Devices ◽  
Wireless Systems ◽  
Integrated System ◽  
Laboratory Model ◽  
Mobile Laboratory ◽  
Next Generation ◽  
Distributed Communication ◽  
Human Operators ◽  
Heterogeneous Wireless Systems ◽  
And Control

Failure to integrate heterogeneous wireless systems generally makes it difficult, if not impossible, for the continuation of remote working or remote experiments when human operators and equipment coexist through networks in a collaborative environment. Mobile laboratories using ubiquitous mobile communication for next-generation heterogeneous wireless systems have prospects for increasing the operation of distributed communication and mobile ubiquitous systems. All “technology assessors” concur that tomorrow’s society will have access to smart objects (mobile devices or apparatuses, mobile equipment, e.g. robots) that contain “programs” that will assist with communication in everyday life. However one of the tomorrow’s challenges will consist of programming those objects to cooperate with and control telecommunications technologies. For a Mobile Laboratory to ensure consistent mobility in an environment, it must combine various wireless networks as a single integrated system. In this chapter we propose a Mobile Laboratory Model with mobile devices that take advantage of multiple mobile gateways by using Internet Protocol (IP) as the interconnection protocol to achieve the objective stated above.

scholarly journals Creating explorable extended reality environments with semantic annotations

2020 ◽  
Author(s):  
Jakub Flotyński
Keyword(s):  
Domain Knowledge ◽  
Description Logics ◽  
Main Element ◽  
Arbitrary Domain ◽  
Aspect Oriented Programming ◽  
Domain Experts ◽  
Semantic Annotations ◽  
Knowledge Based ◽  
And Control ◽  
3D Content

Abstract The main element of extended reality (XR) environments is behavior-rich 3D content consisting of objects that act and interact with one another as well as with users. Such actions and interactions constitute the evolution of the content over time. Multiple application domains of XR, e.g., education, training, marketing, merchandising, and design, could benefit from the analysis of 3D content changes based on general or domain knowledge comprehensible to average users or domain experts. Such analysis can be intended, in particular, to monitor, comprehend, examine, and control XR environments as well as users’ skills, experience, interests and preferences, and XR objects’ features. However, it is difficult to achieve as long as XR environments are developed with methods and tools that focus on programming and 3D modeling rather than expressing domain knowledge accompanying content users and objects, and their behavior. The main contribution of this paper is an approach to creating explorable knowledge-based XR environments with semantic annotations. The approach combines description logics with aspect-oriented programming, which enables knowledge representation in an arbitrary domain as well as transformation of available environments with minimal users’ effort. We have implemented the approach using well-established development tools and exemplify it with an explorable immersive car showroom. The approach enables efficient creation of explorable XR environments and knowledge acquisition from XR.

Programming Language Support for Implementing Machine Learning Algorithms

2020 ◽  
pp. 402-421
Author(s):  
Anitha Elavarasi S. ◽  
Jayanthi J.
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Mathematical Optimization ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
Machine Learning Applications ◽  
And Control

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.

scholarly journals Model Reduction Methods for Complex Network Systems

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
X. Cheng ◽  
J.M.A. Scherpen
Keyword(s):  
Autonomous Systems ◽  
High Dimensional ◽  
Annual Review ◽  
Publication Date ◽  
Network Systems ◽  
Modeling And Control ◽  
Reduced Order ◽  
Reduction Methods ◽  
The Stability ◽  
And Control

Network systems consist of subsystems and their interconnections and provide a powerful framework for the analysis, modeling, and control of complex systems. However, subsystems may have high-dimensional dynamics and a large number of complex interconnections, and it is therefore relevant to study reduction methods for network systems. Here, we provide an overview of reduction methods for both the topological (interconnection) structure of a network and the dynamics of the nodes while preserving structural properties of the network. We first review topological complexity reduction methods based on graph clustering and aggregation, producing a reduced-order network model. Next, we consider reduction of the nodal dynamics using extensions of classical methods while preserving the stability and synchronization properties. Finally, we present a structure-preserving generalized balancing method for simultaneously simplifying the topological structure and the order of the nodal dynamics. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Quantum-Like Interdependence Theory Advances Autonomous Human–Machine Teams (A-HMTs)

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1227
Author(s):  
William F. Lawless
Keyword(s):  
Autonomous Systems ◽  
Quantum Decoherence ◽  
Multi Agent Systems ◽  
Scale Free ◽  
Weapon Systems ◽  
Interdependence Theory ◽  
Human Autonomy ◽  
Degree Of Certainty ◽  
Human Operators ◽  
High Degree

As humanity grapples with the concept of autonomy for human–machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.

scholarly journals A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Lin Li ◽  
Austin J. Brockmeier ◽  
John S. Choi ◽  
Joseph T. Francis ◽  
Justin C. Sanchez ◽  
...  
Keyword(s):  
Tensor Product ◽  
Neural Activity ◽  
Spike Timing ◽  
The Body ◽  
Data Type ◽  
Mathematical Framework ◽  
Neural Decoding ◽  
Electrical Microstimulation ◽  
Product Kernel ◽  
And Control

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain’s motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.

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