Agent-Based Modeling (ABM):A simulation method where individual entities (agents) act and interact according to predefined rules.
Algorithm: A set of rules or instructions for solving a problem or performing a task, often used in programming and AI.
Artificial Intelligence (AI): The simulation of human intelligence in machines that can learn, reason, and make decisions.
B
Bayesian Inference: A statistical method that updates the probability of a hypothesis as more evidence becomes available.
Bias (in AI): Systematic errors in AI systems resulting from imbalanced data or flawed algorithms.
Big Data: Extremely large datasets that require advanced methods for storage, analysis, and visualization.
C
Causal Inference:The process of determining cause-and-effect relationships from data and models.
Cloud Computing: Delivery of computing services over the internet, enabling scalable resources for M&S and AI.
Clustering: A machine learning technique that groups similar data points together based on specific features.
D
Data Mining: The process of discovering patterns and knowledge from large amounts of data.
Deep Learning:A subset of machine learning that uses neural networks with many layers to analyze complex patterns in data.
Discrete Event Simulation (DES): A simulation technique that models systems as a sequence of events occurring at specific times.
E
Edge Computing: Processing data near its source rather than relying on a central server, reducing latency and bandwidth use.
F
Federated Learning: A machine learning technique where multiple devices collaboratively train a model while keeping data localized.
Framework: A structured environment for developing software, such as TensorFlow for AI or OpenSim for simulations.
G
Generative AI:AI systems that can create content, such as text, images, or music, based on input data.
GPU (Graphics Processing Unit): A hardware component optimized for parallel processing, essential for AI and simulation tasks.
H
Hybrid Simulation: Combining multiple simulation methods, such as agent-based and system dynamics modeling, in one framework.
Heuristic: A problem-solving approach that uses practical methods or rules of thumb for decision-making.
I
Interoperability: The ability of different systems or tools to work together seamlessly, critical in simulation and AI integration.
Interactive Simulation: A simulation that allows user interaction during its execution, often used for training or decision support.
K
Key Performance Indicators (KPIs): Metrics used to evaluate the success of a simulation or AI system in achieving objectives.
L
Linear Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables.
Loss Function: A mathematical function used to measure the difference between predicted and actual outcomes in machine learning.
M
Machine Learning (ML):A subset of AI where machines learn patterns from data to make predictions or decisions without being explicitly programmed.
Monte Carlo Simulation: A probabilistic method that uses random sampling to model uncertainty and predict outcomes.
N
Natural Language Processing (NLP): A branch of AI focused on enabling machines to understand, interpret, and generate human language.
Neural Network: A computing system inspired by the human brain, used in deep learning to recognize patterns in data.
O
Open-Source Software: Software with publicly available source code, allowing users to modify, distribute, and improve it.
Optimization: The process of making a system as effective or efficient as possible, often used in AI and simulation.
P
Probabilistic Modeling: A mathematical approach to account for uncertainty and variability in simulations and predictions.
Python: A popular programming language widely used in AI, machine learning, and simulation development.
R
Real-Time Simulation: A simulation that operates at the same pace as the real-world processes it represents.
Reinforcement Learning: A machine learning technique where agents learn by interacting with an environment to maximize rewards.
S
Scalability: The ability of a system to handle increased workload or scale up without compromising performance.
System Dynamics Modeling: A simulation method that represents systems as stocks, flows, and feedback loops.
T
TensorFlow: An open-source framework for developing machine learning and AI applications.
Training Data: A dataset used to teach machine learning models how to make predictions or decisions.
U
Urban Simulation: The use of modeling techniques to analyze and predict urban growth, planning, and development.
V
Validation (in Simulation): The process of ensuring that a simulation accurately represents the real-world system it is intended to model.
Virtual Reality (VR): A simulated environment that immerses users in a 3D world, often used for training and visualization in M&S.
W
Web API: An API that can be accessed over the web using HTTP protocols, enabling interaction with web services.
Workflow: A sequence of tasks or processes to accomplish a specific goal, often used in AI model development or simulation projects.
X
Explainable AI (XAI): AI systems designed to provide clear and understandable insights into how decisions are made.
XML (eXtensible Markup Language): A markup language that defines rules for encoding documents in a format readable by both humans and machines, often used in data interchange between APIs.
Y
YAML (YAML Ain't Markup Language): A human-readable data serialization standard often used for configuration.
Yield Analysis: Assessing the effectiveness or success rate of a system, often used in manufacturing simulations.
Z
Zero-Shot Learning: A machine learning technique where models make predictions about unseen classes without prior training on them.