Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
RobotA robot is a machine—especially one programmable by a computer—capable of carrying out a complex series of actions automatically. A robot can be guided by an external control device, or the control may be embedded within. Robots may be constructed to evoke human form, but most robots are task-performing machines, designed with an emphasis on stark functionality, rather than expressive aesthetics.
Swarm roboticsSwarm robotics is an approach to the coordination of multiple robots as a system which consist of large numbers of mostly simple physical robots. ′′In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act.′′ It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment.
CommunicationCommunication is usually defined as the transmission of information. The term can also refer to the message itself, or the field of inquiry studying these transmissions, also known as communication studies. The precise definition of communication is disputed. Controversial issues are whether unintentional or failed transmissions are included and whether communication does not just transmit meaning but also create it. Models of communication aim to provide a simplified overview of its main components and their interaction.
Distributed artificial intelligenceDistributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems. Multi-agent systems and distributed problem solving are the two main DAI approaches. There are numerous applications and tools. Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems.
RoboticsRobotics is an interdisciplinary branch of electronics and communication, computer science and engineering. Robotics involves the design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics engineering, electronics, biomedical engineering, computer engineering, control systems engineering, software engineering, mathematics, etc.
Models of communicationModels of communication are simplified representations of the process of communication. Most models try to describe both verbal and non-verbal communication and often understand it as an exchange of messages. Their function is to give a compact overview of the complex process of communication. This helps researchers formulate hypotheses, apply communication-related concepts to real-world cases, and test predictions. Despite their usefulness, many models are criticized based on the claim that they are too simple because they leave out essential aspects.
Interpersonal communicationInterpersonal communication is an exchange of information between two or more people. It is also an area of research that seeks to understand how humans use verbal and nonverbal cues to accomplish a number of personal and relational goals. Interpersonal communication research addresses at least six categories of inquiry: 1) how humans adjust and adapt their verbal communication and nonverbal communication during face-to-face communication; 2) how messages are produced; 3) how uncertainty influences behavior and information-management strategies; 4) deceptive communication; 5) relational dialectics; and 6) social interactions that are mediated by technology.
Human communicationHuman communication, or anthroposemiotics, is a field of study dedicated to understanding how humans communicate. Humans' ability to communicate with one another would not be possible without an understanding of what we are referencing or thinking about. Because humans are unable to fully understand one another's perspective, there needs to be a creation of commonality through a shared mindset or viewpoint. The field of communication is very diverse, as there are multiple layers of what communication is and how we use its different features as human beings.
Bio-inspired roboticsBio-inspired robotic locomotion is a fairly new subcategory of bio-inspired design. It is about learning concepts from nature and applying them to the design of real-world engineered systems. More specifically, this field is about making robots that are inspired by biological systems, including Biomimicry. Biomimicry is copying from nature while bio-inspired design is learning from nature and making a mechanism that is simpler and more effective than the system observed in nature.
Nonverbal communicationNonverbal communication (NVC) is the transmission of messages or signals through a nonverbal platform such as eye contact, facial expressions, gestures, posture, use of objects and body language. It includes the use of social cues, kinesics, distance (proxemics) and physical environments/appearance, of voice (paralanguage) and of touch (haptics). A signal has three different parts to it, including the basic signal, what the signal is trying to convey, and how it is interpreted.
Artificial intelligenceArtificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of human beings or animals. AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go).
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Organizational communicationWithin the realm of communication studies, organizational communication is a field of study surrounding all areas of communication and information flow that contribute to the functioning of an organization . Organizational communication is constantly evolving and as a result, the scope of organizations included in this field of research have also shifted over time. Now both traditionally profitable companies, as well as NGO's and non-profit organizations, are points of interest for scholars focused on the field of organizational communication.
Q-learningQ-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.
Artificial immune systemIn artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving. The field of artificial immune systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology.
Swarm intelligenceSwarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems.
Distributed cognitionDistributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s. From cognitive ethnography, Hutchins argues that mental representations, which classical cognitive science held that are within the individual brain, are actually distributed in sociocultural systems that constitute the tools to think and perceive the world.
Robot locomotionRobot locomotion is the collective name for the various methods that robots use to transport themselves from place to place. Wheeled robots are typically quite energy efficient and simple to control. However, other forms of locomotion may be more appropriate for a number of reasons, for example traversing rough terrain, as well as moving and interacting in human environments. Furthermore, studying bipedal and insect-like robots may beneficially impact on biomechanics.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
