Smart Home and Artificial Intelligence as Environment for the Implementation of New Technologies

The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits.The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering