The document discusses how robots may need to be self-aware to be trusted, especially in unpredictable environments. It argues that safety cannot be achieved without self-awareness when a robot's environment is unknown. An internal model allows a robot to simulate possible future actions and outcomes without committing to them. This can provide a minimal level of functional self-awareness for safety. A generic internal modeling architecture is proposed where an internal model evaluates consequences of actions to moderate action selection for safety. Examples of robots using internal models for functions like planning, learning control, and distributed coordination are also provided.
Computer vision is a prominent subset of artificial intelligence that can analyse and make sense of image and video data. Dr Tian Jing, Senior Lecturer & Consultant, Artificial Intelligence Practice will expand on recent advanced computer vision developments and key use cases in the new normal, such as social distancing in surveillance, hand hygiene monitoring in healthcare and more. This talk will also demonstrate examples of practice module projects of Intelligent Sensing Systems Graduate Certificate, offered by NUS-ISS in the past semesters.
UMLassure: An approach to model software securitymanishthaper
This document presents a UML profile called UMLassure that can be used to model security requirements in software systems. UMLassure extends UML with stereotypes and tagged values to represent security concepts like cross-site scripting and SQL injection. The profile helps automate the translation of non-functional security requirements from design artifacts into code. An example application demonstrates how UMLassure can be used to model threats and avoid vulnerabilities in a case study.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
SBQS 2013 Keynote: Cooperative Testing and AnalysisTao Xie
SBQS 2013 Keynote: Cooperative Testing and Analysis: Human-Tool, Tool-Tool, and Human-Human Cooperations to Get Work Done http://sbqs.dcc.ufba.br/view/palestrantes.php
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
This document discusses engineering autonomic ensembles through model-based development. It describes modeling autonomic systems using Agamemnon and implementing components using Poem. Reinforcement learning is used to find good completions for partial programs that maximize reward. The Service Component Ensemble Language (SCEL) provides an abstract framework for ensemble programming. A case study of a robot ensemble is used to illustrate modeling the domain and requirements, selecting adaptation patterns, modeling behavior, and analyzing requirements through simulation and sensitivity analysis.
This document discusses self-awareness in the context of hypermusic nodes. It defines self-awareness and self-expression for nodes. It then describes a solojam application where hypermusic nodes coordinate solos through auctions based on a utility function. Future work is outlined to improve the auction mechanism, user interaction, and test with more nodes including humans and AI.
Computer vision is a prominent subset of artificial intelligence that can analyse and make sense of image and video data. Dr Tian Jing, Senior Lecturer & Consultant, Artificial Intelligence Practice will expand on recent advanced computer vision developments and key use cases in the new normal, such as social distancing in surveillance, hand hygiene monitoring in healthcare and more. This talk will also demonstrate examples of practice module projects of Intelligent Sensing Systems Graduate Certificate, offered by NUS-ISS in the past semesters.
UMLassure: An approach to model software securitymanishthaper
This document presents a UML profile called UMLassure that can be used to model security requirements in software systems. UMLassure extends UML with stereotypes and tagged values to represent security concepts like cross-site scripting and SQL injection. The profile helps automate the translation of non-functional security requirements from design artifacts into code. An example application demonstrates how UMLassure can be used to model threats and avoid vulnerabilities in a case study.
DEEPFAKE DETECTION TECHNIQUES: A REVIEWvivatechijri
Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
SBQS 2013 Keynote: Cooperative Testing and AnalysisTao Xie
SBQS 2013 Keynote: Cooperative Testing and Analysis: Human-Tool, Tool-Tool, and Human-Human Cooperations to Get Work Done http://sbqs.dcc.ufba.br/view/palestrantes.php
AI Security : Machine Learning, Deep Learning and Computer Vision SecurityCihan Özhan
This document discusses technologies related to machine learning, deep learning, computer vision, and artificial intelligence. It covers topics such as ML/DL algorithms, applications, data objects, cloud computing services, distributed systems, security issues, model lifecycles, publishing ML projects, and adversarial attacks against various AI systems including image, speech, NLP, remote sensing, autonomous vehicles, and industrial applications. It also provides links to the founder's online profiles and contact information.
This document discusses engineering autonomic ensembles through model-based development. It describes modeling autonomic systems using Agamemnon and implementing components using Poem. Reinforcement learning is used to find good completions for partial programs that maximize reward. The Service Component Ensemble Language (SCEL) provides an abstract framework for ensemble programming. A case study of a robot ensemble is used to illustrate modeling the domain and requirements, selecting adaptation patterns, modeling behavior, and analyzing requirements through simulation and sensitivity analysis.
This document discusses self-awareness in the context of hypermusic nodes. It defines self-awareness and self-expression for nodes. It then describes a solojam application where hypermusic nodes coordinate solos through auctions based on a utility function. Future work is outlined to improve the auction mechanism, user interaction, and test with more nodes including humans and AI.
This document discusses self-awareness in autonomous systems and provides examples. It defines autonomic systems as self-governing systems that can operate without external direction in complex environments. Examples discussed include robot swarms, science clouds, and cooperative electric vehicles. The motivation for self-awareness in information and communication technology systems is that as systems become more distributed and complex, they require mechanisms to manage and organize themselves. Existing self-aware systems in nature that provide inspiration include flocking behavior in animals and ant foraging behavior through decentralized coordination.
Presentation by Aikaterini Bourzeri, Jeremy Pitt, Pablo Almajano, Inmaculada Rodriguez and Maite Lopez-Sanchez at the 2nd Awareness Workshop on Challenges for Achieving Self-awareness in Autonomic Systems @ SASO 2012, Lyon, France
Simulation tools can help understand natural systems and develop self-aware systems. Existing simulators like Repast and The ONE have advantages but lack certain features. The CoSMoS method structures simulation development through domain, platform, and results models to help ensure simulations accurately represent domains. Simulations aid controller design for systems like underwater robots, though the "reality gap" between simulation and reality requires attention.
This document discusses using opportunistic networks to provide crowd steering services at a music festival. It outlines requirements like scalability and adaptability given the large, unpredictable crowds. The SAPERE project aims to develop a decentralized framework for self-aware, adaptive services on mobile devices. A proposed app would use devices to share information about events, points of interest, taxi locations and more to guide the crowd. Design considerations include how information spreads, routing algorithms, technologies like Bluetooth and challenges of collisions, overload and simulations.
Ichiro Satoh presented his experiences with context-aware services from experiments conducted in real world settings like museums. The experiments tested different methods of providing audio and text annotations to visitors based on their location and past exhibits viewed. Evaluation of over 200 participants found that traditional paper-based posters achieved the highest learning scores, while methods relying on devices distracted visitors. Key lessons included informing users about available services, supporting legacy spaces with constraints, and managing heterogeneous systems in public settings.
This document proposes a conceptual map and classification system for ensembles of autonomic components based on their awareness and organization. It outlines trees of concepts related to the single component level (communication, internal architecture, awareness capabilities) and the ensemble level (organization, architecture, team features). The goal is to enhance reuse and adaptation of components by defining their relationships and required features for different coordination patterns. Future work includes formalizing the concept of distances between patterns and implementing test scenarios to validate the classifications.
The document provides an outline for a presentation on self-awareness in autonomic systems. It discusses introductory examples of robot swarms, science clouds, and cooperative electric vehicles. It then motivates the need for awareness in complex distributed systems like communication and power networks. Existing research projects exploring self-awareness concepts are summarized, including ASCENS, CoCoRo, EPiCS, RECOGNITION, SAPERE, and SYMBRION. Nature-inspired examples of self-aware behaviors in flocking, ant foraging, quorum sensing, chemotaxis, morphogenesis, and gossiping are presented. Finally, awareness properties in biological systems like the immune system are discussed.
The document discusses conflicting goals in power systems management between government, consumers, and generators. It proposes a solution using self-organizing institutions inspired by Elinor Ostrom's work on common pool resource management. Key elements include specifying norm-governed multi-agent systems, roles and protocols, and an awareness loop where agents identify and react to the state of their population over time to balance resource availability and avoid excess or crisis.
The document discusses self-organizing pervasive service ecosystems. It introduces various self-organizing mechanisms inspired by biology, including spreading, evaporation, aggregation, repulsion, gossip, gradients, digital pheromone, ant foraging, and morphogenesis. These mechanisms are described as design patterns that can provide low-level services to applications in a pervasive computing environment. The patterns are organized into three levels and can serve as building blocks for the development of self-aware services and applications.
This document provides an overview of classifying human motion for active music systems. It discusses using motion classification as a first step to map sensed human motion to music. Two algorithms for motion classification are described: Dynamic Time Warping and Hidden Markov Models. Dynamic Time Warping allows comparison of signals of different lengths by finding the optimal alignment between them. Hidden Markov Models are statistical generative models that can determine the probability a time-varying signal was generated by a given model. The document outlines challenges in motion classification and demonstrates classification of motion data from a mobile device's accelerometer.
The document discusses using swarms of underwater robots to perform search and rescue tasks. It describes the CoCoRo project which uses collective cognition and swarm intelligence to coordinate groups of simple robots. This allows them to display complex emergent behaviors. Specific challenges of operating underwater like communication and localization are addressed. The document proposes using a relay chain to connect an exploratory swarm of robots to a base station. It provides resources to start simulating and developing algorithms for the swarm and relay chain behaviors.
This document discusses coordination models and technologies for self-organizing systems. It provides background on tuple-based models for complex systems coordination, which originated from Linda and allow for distributed coordination through shared tuple spaces. Key features of tuple-based models include knowledge-based coordination through associative access to tuples, distribution of coordination media, and programmability of coordination rules. These features help enable coordination in complex, distributed systems.
This document provides an introduction to modeling and analyzing autonomic systems. It discusses modeling autonomic systems using the SOTA/GEM framework for requirements specification and the SCEL modeling language. It then presents a case study of modeling a swarm of garbage collecting robots. Key steps include modeling goals and requirements, selecting adaptation patterns, modeling the robot behavior and interactions in SCEL, and validating requirements through quantitative analysis using techniques like CTMC and ODE models. The document outlines the iterative design time and runtime engineering process for autonomic systems using these techniques.
Symbrion aims to develop self-replicating robots through two grand challenges: 1) Creating 100 robots that operate for 100 days, and 2) Evolving self-replicating organisms. The document outlines an evolution of organisms from basic bodies and functionalities, to organisms that can perform user-defined tasks. It discusses concepts like self-awareness, where organisms can autonomously select mates or commit suicide, know when they are injured and self-repair, and evolve mismatched bodies and minds that must learn to control their new form.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
This document summarizes several research projects related to autonomic and self-aware systems. It discusses proprioceptive systems like EPiCS which aim to develop self-aware and self-expressive computing systems. It also discusses swarm robotics projects like SYMBRION that develop robotic swarms capable of self-organization. Data management projects like SAPERE and RECOGNITION seek to develop self-aware techniques for acquiring and managing large amounts of data and content.
- The document proposes a cognitive heuristic model for local community recognition based on principles of probabilistic reasoning and learning from human cognition.
- It describes a tripartite model of unconscious knowledge, reasoning processes, and learning evaluation to represent cognitive heuristics for tasks like community detection and definition.
- A simple cognitive algorithm is presented that uses memory vectors and inference rules inspired by cognitive heuristics to recognize communities in a network through local interactions alone.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
This document discusses self-awareness in autonomous systems and provides examples. It defines autonomic systems as self-governing systems that can operate without external direction in complex environments. Examples discussed include robot swarms, science clouds, and cooperative electric vehicles. The motivation for self-awareness in information and communication technology systems is that as systems become more distributed and complex, they require mechanisms to manage and organize themselves. Existing self-aware systems in nature that provide inspiration include flocking behavior in animals and ant foraging behavior through decentralized coordination.
Presentation by Aikaterini Bourzeri, Jeremy Pitt, Pablo Almajano, Inmaculada Rodriguez and Maite Lopez-Sanchez at the 2nd Awareness Workshop on Challenges for Achieving Self-awareness in Autonomic Systems @ SASO 2012, Lyon, France
Simulation tools can help understand natural systems and develop self-aware systems. Existing simulators like Repast and The ONE have advantages but lack certain features. The CoSMoS method structures simulation development through domain, platform, and results models to help ensure simulations accurately represent domains. Simulations aid controller design for systems like underwater robots, though the "reality gap" between simulation and reality requires attention.
This document discusses using opportunistic networks to provide crowd steering services at a music festival. It outlines requirements like scalability and adaptability given the large, unpredictable crowds. The SAPERE project aims to develop a decentralized framework for self-aware, adaptive services on mobile devices. A proposed app would use devices to share information about events, points of interest, taxi locations and more to guide the crowd. Design considerations include how information spreads, routing algorithms, technologies like Bluetooth and challenges of collisions, overload and simulations.
Ichiro Satoh presented his experiences with context-aware services from experiments conducted in real world settings like museums. The experiments tested different methods of providing audio and text annotations to visitors based on their location and past exhibits viewed. Evaluation of over 200 participants found that traditional paper-based posters achieved the highest learning scores, while methods relying on devices distracted visitors. Key lessons included informing users about available services, supporting legacy spaces with constraints, and managing heterogeneous systems in public settings.
This document proposes a conceptual map and classification system for ensembles of autonomic components based on their awareness and organization. It outlines trees of concepts related to the single component level (communication, internal architecture, awareness capabilities) and the ensemble level (organization, architecture, team features). The goal is to enhance reuse and adaptation of components by defining their relationships and required features for different coordination patterns. Future work includes formalizing the concept of distances between patterns and implementing test scenarios to validate the classifications.
The document provides an outline for a presentation on self-awareness in autonomic systems. It discusses introductory examples of robot swarms, science clouds, and cooperative electric vehicles. It then motivates the need for awareness in complex distributed systems like communication and power networks. Existing research projects exploring self-awareness concepts are summarized, including ASCENS, CoCoRo, EPiCS, RECOGNITION, SAPERE, and SYMBRION. Nature-inspired examples of self-aware behaviors in flocking, ant foraging, quorum sensing, chemotaxis, morphogenesis, and gossiping are presented. Finally, awareness properties in biological systems like the immune system are discussed.
The document discusses conflicting goals in power systems management between government, consumers, and generators. It proposes a solution using self-organizing institutions inspired by Elinor Ostrom's work on common pool resource management. Key elements include specifying norm-governed multi-agent systems, roles and protocols, and an awareness loop where agents identify and react to the state of their population over time to balance resource availability and avoid excess or crisis.
The document discusses self-organizing pervasive service ecosystems. It introduces various self-organizing mechanisms inspired by biology, including spreading, evaporation, aggregation, repulsion, gossip, gradients, digital pheromone, ant foraging, and morphogenesis. These mechanisms are described as design patterns that can provide low-level services to applications in a pervasive computing environment. The patterns are organized into three levels and can serve as building blocks for the development of self-aware services and applications.
This document provides an overview of classifying human motion for active music systems. It discusses using motion classification as a first step to map sensed human motion to music. Two algorithms for motion classification are described: Dynamic Time Warping and Hidden Markov Models. Dynamic Time Warping allows comparison of signals of different lengths by finding the optimal alignment between them. Hidden Markov Models are statistical generative models that can determine the probability a time-varying signal was generated by a given model. The document outlines challenges in motion classification and demonstrates classification of motion data from a mobile device's accelerometer.
The document discusses using swarms of underwater robots to perform search and rescue tasks. It describes the CoCoRo project which uses collective cognition and swarm intelligence to coordinate groups of simple robots. This allows them to display complex emergent behaviors. Specific challenges of operating underwater like communication and localization are addressed. The document proposes using a relay chain to connect an exploratory swarm of robots to a base station. It provides resources to start simulating and developing algorithms for the swarm and relay chain behaviors.
This document discusses coordination models and technologies for self-organizing systems. It provides background on tuple-based models for complex systems coordination, which originated from Linda and allow for distributed coordination through shared tuple spaces. Key features of tuple-based models include knowledge-based coordination through associative access to tuples, distribution of coordination media, and programmability of coordination rules. These features help enable coordination in complex, distributed systems.
This document provides an introduction to modeling and analyzing autonomic systems. It discusses modeling autonomic systems using the SOTA/GEM framework for requirements specification and the SCEL modeling language. It then presents a case study of modeling a swarm of garbage collecting robots. Key steps include modeling goals and requirements, selecting adaptation patterns, modeling the robot behavior and interactions in SCEL, and validating requirements through quantitative analysis using techniques like CTMC and ODE models. The document outlines the iterative design time and runtime engineering process for autonomic systems using these techniques.
Symbrion aims to develop self-replicating robots through two grand challenges: 1) Creating 100 robots that operate for 100 days, and 2) Evolving self-replicating organisms. The document outlines an evolution of organisms from basic bodies and functionalities, to organisms that can perform user-defined tasks. It discusses concepts like self-awareness, where organisms can autonomously select mates or commit suicide, know when they are injured and self-repair, and evolve mismatched bodies and minds that must learn to control their new form.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
This document summarizes several research projects related to autonomic and self-aware systems. It discusses proprioceptive systems like EPiCS which aim to develop self-aware and self-expressive computing systems. It also discusses swarm robotics projects like SYMBRION that develop robotic swarms capable of self-organization. Data management projects like SAPERE and RECOGNITION seek to develop self-aware techniques for acquiring and managing large amounts of data and content.
- The document proposes a cognitive heuristic model for local community recognition based on principles of probabilistic reasoning and learning from human cognition.
- It describes a tripartite model of unconscious knowledge, reasoning processes, and learning evaluation to represent cognitive heuristics for tasks like community detection and definition.
- A simple cognitive algorithm is presented that uses memory vectors and inference rules inspired by cognitive heuristics to recognize communities in a network through local interactions alone.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
This document summarizes a research paper that describes creating a custom 7 degree of freedom robotic arm in the CoppeliaSim robot simulator. The paper outlines how to design the robotic arm by first defining the DH parameters and then following CoppeliaSim's procedures to build the arm without any code. Sensors and actuators can then be added to the arm and code can be attached using Lua scripting to control the arm via TCP/IP communication. The custom robotic arm created in CoppeliaSim allows researchers to test forward and inverse kinematics, statics, dynamics and other algorithms before implementing on a real robot.
Modular robotics allow for robots made of identical, interchangeable parts that can reconfigure to perform different tasks. Researchers have created several types of modular and self-reconfiguring robots, including those that can change shapes autonomously, mimic snakes or geckos, and even deconstruct and reconstruct themselves. Many projects aim to develop robots capable of decentralized control through simple local rules rather than centralized algorithms, allowing for robust and adaptable emergent behaviors.
[Skolkovo Robotics V] Collaborative Robots: Research, Technologies and Applic...Skolkovo Robotics Center
Collaborative robots allow humans and robots to work together in a shared workspace. They are designed to be safe through features like lightweight materials, sensitive sensors to detect contact, and safety-rated control schemes. Regulations like the ISO 10218 and ISO/TS 15066 standards define requirements for speed and separation monitoring as well as power and force limiting to ensure collaborative robots can safely interact with humans. Current applications are mostly in traditional factory settings for tasks like machine tending, assembly, and quality inspection, but research continues in areas like full-body compliance, grasping, and human-aware planning to expand collaborative robot capabilities.
This document introduces a robotics course that will teach computer programming, embedded systems engineering, design, and mathematics concepts using robotics projects. Students will develop 21st century skills like time management, teamwork, and problem-solving. The course will cover the Three Laws of Robotics, robot hardware and software, programming syntax, sensor integration, and synchronized motor control. Safety practices will be emphasized. Students will design, build, test, and evaluate robots to complete challenges. The instructor has 18 years experience and is excited to teach robotics concepts.
This document introduces a robotics course at the American Nicaraguan School. The course will cover topics like computer programming, embedded systems engineering, and mathematics using robotics as a teaching tool. Students will learn 21st century skills including time management, teamwork, and problem solving. The class will explore Isaac Asimov's Three Laws of Robotics and discuss how true artificial general intelligence may be developed within a decade. Students will build and program Lego Mindstorms robots using the ROBOTC software to complete engineering challenges and competitions throughout the course. Safety practices and procedures will be emphasized.
1. A robot is a machine that can perform complex human tasks through electrical and mechanical units and by being programmed by a computer.
2. The word "robot" was introduced in a 1920 play and the word "robotics" was coined accidentally by science fiction writer Isaac Asimov in 1942. Asimov also introduced the Three Laws of Robotics.
3. Robots consist of sensors to detect their environment, effectors to interact with it, actuators to move parts of the robot, controllers to operate it, and often arms and artificial intelligence capabilities. They are used for tasks that are dangerous, repetitive, or difficult for humans.
This document provides information about robotics and machine vision systems courses. The objectives are to study robot components, derive kinematics and dynamics equations, manipulate trajectories, and learn machine vision. Key topics covered include robot history, components, configurations like Cartesian and cylindrical, applications in material handling, processing, assembly, and inspection. Benefits of robots are also discussed.
This document provides information about robotics and machine vision systems. It discusses the objectives of studying these topics, which include understanding the components of industrial robots, deriving kinematics and dynamics equations, programming robots for applications, and learning machine vision systems. Key events in the history of robotics are outlined from the 1940s to present day. The basic components and functions of an industrial robot are described. Reasons for using robots include handling hazardous materials, improving consistency and productivity.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
The Grand Unified Theory of Autonomous Systems, Humans and SimulationAndy Fawkes
Presented at the NATO SCI Verification and Validation of Autonomous Systems Workshop on 25 June 14 at Imperial College London - The theme, a Valid and Verified Autonomous System must Include Human(s) and Simulation has a Key Role in Developing, Testing and Training both the Autonomous System and Human(s), Separately and Together
London Futurists - The Future of AI & SustainabilityAlex Housley
Artificial intelligence (AI) is powering the fourth industrial revolution. Intelligent machines are tackling new cognitive tasks at scale, leading to enormous economic efficiency gains and disruption across the labour market. But what will be the net impact of AI on society and the ecological environment?
In this talk, Alex Housley, founder and CEO of open-source machine learning platform Seldon, explains how the collaborative approach to AI development helps transform industries and provides the macro-scale opportunities for AI to make the world a better and more sustainable place.
The event was chaired by David Wood. The camera was operated by Kiran Manam.
For more details about this event, see https://www.meetup.com/London-Futuris....
For more information about Seldon, see https://www.seldon.io/.
To apply to join the closed beta mentioned in the talk, visit bit.ly/deploy-beta.
This document provides class notes for an introduction to robotics course. It includes information on the definition of a robot, types of robots, robot applications, robot configurations, and the history and issues of industrial robot usage. The class schedule and grading breakdown are also outlined. The document aims to give students an overview of key robotics concepts to prepare them for the course.
Realtime Face mask Detector using YoloV4IRJET Journal
This document presents a real-time face mask detector using YOLOv4. The system was able to detect faces with 94.75% accuracy and a maximum frame rate of 38 FPS. It used a dataset of images with bounding box annotations that was split into training and validation sets. The YOLOv4 model was trained on the darknet framework using this dataset. Testing showed it could accurately detect single people with or without masks and multiple people in various scenarios. The authors conclude the model provides fast and accurate mask detection in real-time suitable for applications like monitoring mask compliance.
The document describes a project that aims to develop an automated system for coordinating robots working in a swarm environment. The system uses image processing to identify nearby robots by scanning QR codes on each robot and adjusting schedules to avoid collisions without human intervention. It discusses modeling swarm robotics and compares it to other multi-agent systems. The goals are to enhance efficiency of swarms through automatic coordination and reduce human errors. Applications include defence operations, sensitive tasks requiring coordination, and medical fields.
Join an expert panel put together by the Design World editorial team to examine the latest developments and challenges in the ever-changing field of robotics. We’ll learn about Clearpath Robotics’ unmanned vehicles, used for research and development, and what design challenges they faced in developing their products. Panelists will discuss what some of the best practices are for engineers involved in the design of robotics. We’ll also talk about safety issues in robotics and why ease of use of industrial robots is becoming more important. And we’ll examine what’s driving robotics technology today, as well as where the field is going in the coming years.
Testing Is How You Avoid Looking StupidSteve Branam
Presented at With The Best IOT online conference, Oct 14 2017: As IOT products become more pervasive, they have an increasing ability to adversely affect the lives of their users and those around them. Testing is the due diligence that closes the engineering loop to verify proper behavior. This presents an introductory overview to testing for IOT products, covering the IOT triad: embedded IOT devices, backend servers, and frontend apps. I talk about the consequences of inadequate testing for companies and individual contributors, and levels and types of testing.
Similar to Why Robots may need to be self-‐aware, before we can really trust them - Alan Winfield. (20)
Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.
This document discusses design patterns for autonomic systems. It begins by explaining what design patterns are and how they allow common solutions to recurring problems to be reused, saving time. It then discusses how patterns are described and can be composed to solve different problems. The document outlines several bio-inspired design patterns for autonomic computing systems, including spreading, aggregation, evaporation, and repulsion. It concludes by discussing a taxonomy for classifying patterns according to the component and ensemble levels in an autonomic system.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
The document discusses autonomic multi-agent systems and self-awareness. It covers:
1) The objectives of understanding fundamental properties of autonomic systems and how agents can use environmental awareness for self-organization.
2) An overview of multi-agent systems, autonomic systems, and representative approaches like dynamic norm-governed systems.
3) How awareness can enable self-healing through maintaining congruence between rules and system state.
This document discusses common features of complex systems and networks. It notes that complex systems generally have a large number of elements that follow individual behavior rules and interact locally. The systems exhibit node and link diversity and dynamics. They can display hierarchy across different levels and heterogeneity. Complex networks form the backbone of complex systems. Network structure influences function and vice versa. Three key metrics to characterize networks are described - average path length, degree distribution, and clustering coefficient. Different types of networks, including random, regular, small-world and scale-free are also discussed.
This document discusses self-awareness in psychology and proposes a framework for computational self-awareness. It defines different types of self-awareness, such as implicit/explicit and private/public. It also outlines levels of self-awareness ranging from stimulus awareness to meta-self-awareness. Finally, it proposes applying these concepts to computing by defining private and public computational self-awareness and levels that could emerge from interactions between components.
The document discusses awareness in autonomous systems. It covers general properties of self-awareness like perception and collectivity. It also discusses the short-term impacts of self-awareness like safety and sustainability and long-term open issues. Key aspects of self-awareness are levels ranging from ecological to conceptual awareness. Distributed emergence of self-awareness is possible through collective systems though parts exhibit less awareness. Internal models are important for self-aware systems to represent themselves and environments to test possibilities.
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The document describes MICE (Monitoring and modelIng the Context Evolu4on), a tool that supports moving context awareness managers (AMs) from design time to run time. MICE is a composite, distributed system with three main components: a Monitor that collects heterogeneous contextual data sensed by the application, an Analyzer that updates the AMs based on the monitored data, and a Predictor that performs predictive analysis based on the updated AMs. MICE aims to enable validation and refinement of context models at run time to support predictive quality of service analysis and proactive context evolution awareness.
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Why Robots may need to be self-‐aware, before we can really trust them - Alan Winfield.
1. Why
Robots
may
need
to
be
self-‐aware,
before
we
can
really
trust
them
Alan
FT
Winfield
Bristol
Robo=cs
Laboratory
Awareness
Summer
School,
Lucca
26
June
2013
2. Outline
• The
safety
problem
• The
central
proposi=on
of
this
talk
• Introducing
Internal
Models
in
robo=cs
• A
generic
Internal
Modelling
architecture,
for
safety
– worked
example:
a
scenario
with
safety
hazards
• Towards
an
ethical
robot
– worked
example:
a
hazardous
scenario
with
a
human
and
a
robot
• The
major
challenges
• How
self-‐aware
would
the
robot
be?
• A
hint
of
neuroscien=fic
plausibility
3. The
safety
problem
• For
any
engineered
system
to
be
trusted,
it
must
be
safe
– We
already
have
many
examples
of
complex
engineered
systems
that
are
trusted;
passenger
airliners,
for
instance
– These
systems
are
trusted
because
they
are
designed,
built,
verified
and
operated
to
very
stringent
design
and
safety
standards
– The
same
will
need
to
apply
to
autonomous
systems
4. The
safety
problem
• The
problem
of
safe
autonomous
systems
in
unstructured
or
unpredictable
environments,
i.e.
– robots
designed
to
share
human
workspaces
and
physically
interact
with
humans
must
be
safe,
– yet
guaranteeing
safe
behaviour
is
extremely
difficult
because
the
robot’s
human-‐centred
working
environment
is,
by
defini5on,
unpredictable
– it
becomes
even
more
difficult
if
the
robot
is
also
capable
of
learning
or
adapta5on
5. The
proposi=on
In
unknown
or
unpredictable
environments,
safety
cannot
be
achieved
without
self-‐awareness
6. What
is
an
internal
model?
• It
is
an
internal
mechanism
for
represen=ng
both
the
system
itself
and
its
environment
– example:
a
robot
with
a
simula5on
of
itself
and
its
currently
perceived
environment,
inside
itself
• The
mechanism
might
be
centralized,
distributed,
or
emergent
“..an
internal
model
allows
a
system
to
look
ahead
to
the
future
consequences
of
current
ac=ons,
without
actually
commiYng
itself
to
those
ac=ons”
John
Holland
(1992),
Complex
Adap=ve
Systems,
Daedalus.
7. Using
internal
models
• Internal
models
can
provide
a
minimal
level
of
func5onal
self-‐awareness
– sufficient
to
allow
complex
systems
to
ask
what-‐if
ques=ons
about
the
consequences
of
their
next
possible
ac=ons,
for
safety
• Following
Dennea
an
internal
model
can
generate
and
test
what-‐if
hypotheses:
– what if I carry out action x..?!
– of several possible next actions xi, which
should I choose?!
8. Dennea’s
Tower
of
Generate
and
Test
Darwinian
Creatures
Skinnerian
Creatures
Popperian
Creatures
Dennea,
D.
(1995).
Darwin’s
Dangerous
Idea,
London,
Penguin.
Natural
Selec=on
Individual
(Reinforcement)
Learning
Internal
Modelling
9. Examples
1
• A
robot
using
self-‐
simula=on
to
plan
a
safe
route
with
incomplete
knowledge
Vaughan,
R.
T.
and
Zuluaga,
M.
(2006).
Use
your
illusion:
Sensorimotor
self-‐
simula=on
allows
complex
agents
to
plan
with
incomplete
self-‐knowledge,
in
Proceedings
of
the
Interna=onal
Conference
on
Simula=on
of
Adap=ve
Behaviour
(SAB),
pp.
298–309.
10. Examples
2
• A
robot
with
an
internal
model
that
can
learn
how
to
control
itself
Bongard,
J.,
Zykov,
V.,
Lipson,
H.
(2006)
Resilient
machines
through
con=nuous
self-‐
modeling.
Science,
314:
1118-‐1121.
11. Examples
3
• ECCE-‐Robot
– A
robot
with
a
complex
body
uses
an
internal
model
as
a
‘func=onal
imagina=on’
Marques,
H.
and
Holland,
O.
(2009).
Architectures
for
func=onal
imagina=on,
Neurocompu=ng
72,
4-‐6,
pp.
743–759.
Diamond,
A.,
Knight,
R.,
Devereux,
D.
and
Holland,
O.
(2012).
Anthropomime=c
robots:
Concept,
construc=on
and
modelling,
Interna=onal
Journal
of
Advanced
Robo=c
Systems
9,
pp.
1–14.
12. Examples
4
• A
distributed
system
in
which
each
robot
has
an
internal
model
of
itself
and
the
whole
system
– Robot
controllers
and
the
internal
simulator
are
co-‐
evolved
O’Dowd
P,
Winfield
A
and
Studley
M
(2011),
The
Distributed
Co-‐Evolu=on
of
an
Embodied
Simulator
and
Controller
for
Swarm
Robot
Behaviours,
in
Proc
IEEE/RSJ
Interna=onal
Conference
on
Intelligent
Robots
and
Systems
(IROS
2011),
San
Francisco,
September
2011.
25. Challenges
and
open
ques=ons
• Fidelity:
to
model
both
the
system
and
its
environment
with
sufficient
fidelity;
• To
connect
the
IM
with
the
system’s
real
sensors
and
actuators
(or
equivalent);
• Timing
and
data
flows:
to
synchronize
the
internal
model
with
both
changing
perceptual
data,
and
efferent
actuator
data;
• Valida5on,
i.e.
of
the
consequence
rules.
26. Major
challenges:
performance
• Example
–
imagine
placing
this
Webots
simula=on
inside
each
NAO
robot:
Note
the
simulated
robot’s
eye
view
of
it’s
world
27. A
science
of
simula=on:
the
CoSMoS
approach
The
Complex
Systems
Modelling
and
Simula=on
(CoSMoS)
process,
from
Susan
Stepney,
et
al,
Engineering
Simula=ons
as
Scien=fic
Instruments
—
a
paaern
language,
Springer,
in
prepara=on.
The
CoSMoS
Process
Version
0.1:
A
Process
for
the
Modelling
and
Simula=on
of
Complex
Systems,
Paul
S.
Andrews,
et
al,
Dept
of
Computer
Science,
University
of
York,
Number
YCS-‐2010-‐453
28. Major
challenges:
=ming
• When
and
how
oqen
do
we
need
to
ini=ate
the
generate-‐and-‐test-‐loop
(IM
cycle)?
– Maybe
when
the
object
tracker
senses
a
nearby
object
star=ng
to
move..?
• How
far
ahead
should
the
IM
simulate
– Let
us
call
this
=me
ts.
if
ts
is
too
short
the
IM
will
not
encounter
the
hazard;
too
long
will
slow
down
the
robot.
– Ideally
ts
and
its
upper
limit
should
be
adap=ve.
29. How
self-‐aware
would
this
robot
be?
• The
robot
would
not
pass
the
mirror
test
– Haikkonen
(2007),
Reflec=ons
of
consciousness
• However,
I
argue
this
robot
would
be
minimally
but
sufficiently
self-‐aware
to
merit
the
label
– But
this
would
have
to
be
demonstrated
by
the
robot
behaving
in
interes5ng
ways,
that
were
not
pre-‐programmed,
in
response
to
novel
situa5ons
– Valida=ng
any
claims
to
self-‐awareness
would
be
very
challenging
30. Some
neuroscien=fic
plausibility?
• Libet’s
famous
experimental
result
showed
that
ini=a=on
of
ac=on
occurs
before
the
conscious
decision
to
make
take
that
ac=on
– Libet,
B
(1985),
Unconscious
cerebral
Ini=a=ve
and
the
role
of
conscious
will
in
voluntary
ac=on,
Behavioral
and
Brain
Science,
8,
529-‐539.
• Although
controversial
there
appears
to
be
a
growing
body
of
opinion
toward
consciousness
as
a
mechanism
for
vetoing
ac=ons
– Libet
coined
the
term:
free
won’t
31. In
conclusion
• I
strongly
suspect
that
self-‐awareness
via
internal
models
might
prove
to
be
the
only
way
to
guarantee
safety
in
robots,
and
by
extension
autonomous
systems,
in
unknown
and
unpredictable
environments
– and
just
maybe
provide
ethical
behaviours
too
Thank
you!
Reference
for
the
work
of
this
talk:
Winfield
AFT,
Robots
with
Internal
Models:
A
Route
to
Self-‐Aware
and
Hence
Safer
Robots,
accepted
for
The
Computer
AJer
Me,
eds.
Jeremy
Pia
and
Julia
Schaumeier,
Imperial
College
Press,
2013.