Multi-agent systemA multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. Despite considerable overlap, a multi-agent system is not always the same as an agent-based model (ABM).
Learning spaceLearning space or learning setting refers to a physical setting for a learning environment, a place in which teaching and learning occur. The term is commonly used as a more definitive alternative to "classroom," but it may also refer to an indoor or outdoor location, either actual or virtual. Learning spaces are highly diverse in use, configuration, location, and educational institution. They support a variety of pedagogies, including quiet study, passive or active learning, kinesthetic or physical learning, vocational learning, experiential learning, and others.
Learning theory (education)Learning theory describes how students receive, process, and retain knowledge during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a worldview, is acquired or changed and knowledge and skills retained. Behaviorists look at learning as an aspect of conditioning and advocate a system of rewards and targets in education.
Agent-based modelAn agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models.
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.
Experiential learningExperiential learning (ExL) is the process of learning through experience, and is more narrowly defined as "learning through reflection on doing". Hands-on learning can be a form of experiential learning, but does not necessarily involve students reflecting on their product. Experiential learning is distinct from rote or didactic learning, in which the learner plays a comparatively passive role. It is related to, but not synonymous with, other forms of active learning such as action learning, adventure learning, free-choice learning, cooperative learning, service-learning, and situated learning.
Crowd simulationCrowd simulation is the process of simulating the movement (or ) of a large number of entities or characters. It is commonly used to create virtual scenes for visual media like films and video games, and is also used in crisis training, architecture and urban planning, and evacuation simulation. Crowd simulation may focus on aspects that target different applications. For realistic and fast rendering of a crowd for visual media or virtual cinematography, reduction of the complexity of the 3D scene and are used, while variations (changes) in appearance help present a realistic population.
Immersion (virtual reality)Immersion into virtual reality (VR) is a perception of being physically present in a non-physical world. The perception is created by surrounding the user of the VR system in images, sound or other stimuli that provide an engrossing total environment. The name is a metaphoric use of the experience of submersion applied to representation, fiction or simulation.
Virtual fixtureA virtual fixture is an overlay of augmented sensory information upon a user's perception of a real environment in order to improve human performance in both direct and remotely manipulated tasks. Developed in the early 1990s by Louis Rosenberg at the U.S. Air Force Research Laboratory (AFRL), Virtual Fixtures was a pioneering platform in virtual reality and augmented reality technologies. Virtual Fixtures was first developed by Louis Rosenberg in 1992 at the USAF Armstrong Labs, resulting in the first immersive augmented reality system ever built.
ReinforcementIn reinforcement theory, it is argued that human behavior is a result of "contingent consequences" to human actions The publication pushes forward the idea that "you get what you reinforce" This means that behavior when given the right types of reinforcers can change employee behavior for the better and negative behavior can be weeded out. The model of self-regulation has three main aspects of human behavior, which are self-awareness, self-reflection, and self-regulation. Reinforcements traditionally align with self-regulation.
