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.

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.

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.

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.

Edge Computing: Processing data near its source rather than relying on a central server, reducing latency and bandwidth use.

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.

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.

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.

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.

Key Performance Indicators (KPIs): Metrics used to evaluate the success of a simulation or AI system in achieving objectives.

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.

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.

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.

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.

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.

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.

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.

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.

Urban Simulation: The use of modeling techniques to analyze and predict urban growth, planning, and development.

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.

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.

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.

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.

Zero-Shot Learning: A machine learning technique where models make predictions about unseen classes without prior training on them.