We describe a formulation of multi-agents operating within a Cyber-Physical System, resulting in collaborative or adversarial games. We show that the non-determinism inherent in the communication medium between agents and the underlying physical environment gives rise to environment evolution that is a probabilistic function of agents' strategies. We name these emergent properties Cyber Physical Games and study its properties. We present an algorithmic model that determines the most likely system evolution, approximating Cyber Physical Games through Probabilistic Finite State Automata, and evaluate it on collaborative and adversarial versions of the Iterated Boolean Game, comparing theoretical results with simulated ones. Results support the validity of the proposed model, and suggest several required research directions to continue evolving our understanding of Cyber Physical System, as well as how to best design agents that must operate within such environments.

Model Predictive Controllers (MPC) are widely used for controlling cyber-physical systems. It is an iterative process of optimizing the prediction of the future states of a robot over a fixed time horizon. MPCs are effective in practice, but because they are computationally expensive and slow, they are not well suited for use in real-time applications. Overcoming the flaw can be accomplished by approximating an MPC's functionality. Neural networks are very good function approximators and are faster compared to an MPC. It can be challenging to apply neural networks to control-based applications since the data does not match the i.i.d assumption. This study investigates various imitation learning methods for using a neural network in a control-based environment and evaluates their benefits and shortcomings.

Cyber-physical systems (CPSs) are smart systems that depend on the synergy of cyber and physical components. They link the physical world (e.g. through sensors, actuators, robotics, and embedded systems) with the virtual world of information processing. Applications of CPS have the tremendous potential of improving convenience, comfort, and safety in our daily life. This paper provides a brief introduction to CPSs and their applications. The term "cyber-physical system" (CPS) was coined in 2006 by Helen Gill of the US National Science Foundation (Henshaw, 2016).

Cyber-physical systems (CPSs) are smart systems that depend on the synergy of cyber and physical components. They link the physical world (e.g. through sensors, actuators, robotics, and embedded systems) with the virtual world of information processing. Applications of CPS have the tremendous potential of improving convenience, comfort, and safety in our daily life. This paper provides a brief introduction to CPSs and their applications. The term "cyber-physical system" (CPS) was coined in 2006 by Helen Gill of the US National Science Foundation (Henshaw, 2016).

Indraprastha Institute of Information Technology (IIIT) Delhi has introduced two new specializations – machine learning and cyber-physical systems – for the students seeking admission in the MTech (ECE) department. Newly launched MTech (ECE) programme on cyber-physical systems (CPS) at IIIT-Delhi aims to train and equip students with industry-ready skillset on wireless networks and control systems. Upon completion of the programme, student will be able to join the industrial workforce and contribute to the wireless industry, IIIT Delhi said. The MTech (ECE) program on Machine Learning (ML) seeks to equip students with the foundations of Machine Learning and signal processing theory and methods. The coursework is based on foundational and cutting-edge methods in these areas and is taught by faculty conducting pioneering research in the field.

The challenges encountered by manufacturing companies when it comes to handling data are well reported, but what can they do to ensure that data is an asset rather than a problem? Data has long been treated in the manufacturing industry as the orphan nephew living in the cupboard under the stairs. While operational and service industries have leapt on the benefits of data as the catalyst of business growth and efficiency gains, the manufacturing sector has been slow to adopt the culture of becoming a data-driven business. According to Accenture, only 13 per cent of manufacturing companies have seen through a digital transformation of their processes. "In many ways the core approach to manufacturing has remained unchanged for the past 50 years despite the industry experimenting with offshoring and integrated manufacturing in mega factories," Tim Hall, VP products, InfluxData, says.

In his latest Mann ki Baat, Prime Minister Narendra Modi touted Artificial Intelligence (AI) as a technology that can be "harnessed to better the lives of the underprivileged, the marginalised and the needy." In this year's budget the government doubled the allocation for the Digital India initiative in part to support the cyber physical systems mission. Ashutosh Sharma, secretary, department of science and technology, spoke to Malavika Vyawahare to give his take on what an AI revolution means for India. AI is the future of mankind. Machine learning has two aspects; analysis and decision making.

This post is an adaptation of a talk I recently gave for the Global AI Mind Web Series hosted and organized by Wetogether.co and GirlsinTech Taiwan. Whether you realize it or not, you are living in a world that has been dramatically altered due to artificial intelligence and automation. Advances in technology have been improving the efficiency of everyday processes and recreating our workforce in incremental ways. It's easy to adapt to these small changes as they are introduced, since they provide convenience and eliminate tedious tasks but as small changes add up they quickly create a new version of reality. When is the last time you had to filter spam from your email inbox?

An interdisciplinary workshop on self-organization and swarm intelligence in cyber physical systems was held at Lakeside Labs this week. Experts presented their work and discussed open issues in this exciting field. "Our crazyswarm is the largest indoor drone swarm that I'm aware of," Nora Ayanian states. The assistant professor from the Viterbi School of Engineering at the University of Southern California (USC) in Los Angeles was recently described by MIT Technology Review to be one of "35 innovators under 35." She came to Klagenfurt to expound her latest results on multirobot coordination.

Developing intelligent systems requires combining results from both industry and academia. In this report you find an overview of relevant research fields and industrially applicable technologies for building very large scale cyber physical systems. A concept architecture is used to illustrate how existing pieces may fit together, and the maturity of the subsystems is estimated. The goal is to structure the developments and the challenge of machine intelligence for Consumer and Industrial Internet technologists, cyber physical systems researchers and people interested in the convergence of data & Internet of Things. It can be used for planning developments of intelligent systems.