Recently added AI concepts

Fresh entries and updates across the QwickAI glossary.

• A* search — A* search is a pathfinding algorithm that finds the shortest path between two points in a graph.
• A/B testing — A statistical method comparing two versions (A and B) of something to see which performs better.
• Abductive logic programming (ALP) — Abductive logic programming (ALP) is a framework for solving problems by finding the simplest explanations for observations.
• Abductive reasoning — Abductive reasoning infers the most likely explanation for a set of observations, unlike deductive reasoning which proves.
• Ablation — Ablation involves removing a component of an AI system to study its impact on overall performance.
• Ablation Study — An experiment that removes one component of a system at a time to measure its individual contribution.
• Abstract data type — An abstract data type (ADT) defines data behavior by specifying possible values and operations, independent of implementation.
• Abstraction — Abstraction is the process of simplifying complex systems by focusing on essential features and ignoring details.
• Accelerating change — Accelerating change refers to the perceived historical trend of technology advancing at an ever-increasing rate.
• Accelerator — Any specialized hardware designed to run AI workloads faster or more efficiently than a general CPU.
• Accelerator chip — Specialized hardware that speeds up computationally intensive AI tasks like deep learning.
• Accuracy — The fraction of predictions a model gets correct on a labeled dataset.
• Act — The stage in an agent's loop where it executes a chosen action, like sending an API request.
• Action — The mechanism by which an agent in reinforcement learning changes the state of its environment.
• Action language — An action language formally describes how actions affect the state of a system over time, used in AI and robotics.
• Action model learning — Action model learning focuses on agents learning about the effects and prerequisites of actions in their environment.
• Action selection — Action selection is the fundamental problem of deciding "what to do next" for intelligent systems.
• Action space — The set of all possible actions an agent can take to interact with its environment.
• Activation Function — A non-linear function applied to a neuron's output, enabling the network to learn complex, non-linear relationships.
• Activation Patching — An interpretability technique that swaps activations from one forward pass into another to causally localize where a behavior lives in a model.
• Active Learning — An ML approach where the model selects which examples it most wants labeled next.
• AdaGrad — A gradient descent optimization algorithm that adapts the learning rate for each parameter individually.
• Adaptation — A synonym for tuning or fine-tuning a pre-trained AI model on new data.
• Adaptive Agent Context — Context that reshapes itself at runtime based on the task phase, difficulty, and what the agent has already learned.
• Adaptive algorithm — An adaptive algorithm modifies its behavior during execution based on predefined criteria or rewards.
• Adaptive Learning Systems — Educational technologies powered by AI that personalize learning paths and content based on an individual's progress and needs.
• Adaptive neuro fuzzy inference system (ANFIS) — ANFIS integrates neural networks and fuzzy logic to approximate nonlinear functions and learn from data.
• Admissible heuristic — An admissible heuristic never overestimates the cost to reach a goal, ensuring optimal pathfinding.
• Adversarial Attack — An input crafted to fool a model into making a wrong prediction.
• Adversarial Examples — Inputs crafted by an attacker to intentionally cause a machine learning model to make an incorrect prediction.
• Affective computing — Affective computing develops systems that can recognize, interpret, and simulate human emotions.
• Affective Robots — Robots designed with the ability to perceive, express, and potentially experience emotions, enabling more natural human interaction.
• Agent Action Evidence — Verifiable records proving what an agent did, under whose authority, with what inputs and result.
• Agent architecture — An agent architecture is a blueprint detailing the components and arrangement of a software or intelligent agent.
• Agent Assist — AI tools that support human customer service agents with real-time information and suggestions.
• Agent Audit Trail — The chronological, tamper-resistant record of an agent's decisions, tool calls, and context sources.
• Agent Authorization — Deciding and enforcing what an authenticated agent is allowed to do, at what scale, and under which conditions.
• Agent Authorization Protocol (AAP) — A protocol that determines which actions an authenticated agent is permitted to perform, on whose behalf, and within what limits.
• Agent Capabilities — The concrete set of actions an agent can perform — the tools, APIs, and side effects available to it.
• Agent Capability Vocabulary — A shared, standardized naming scheme for the actions agents can offer and request.
• Agent Credential Delegation — Passing a scoped, verifiable credential from a principal to an agent — or from agent to sub-agent — so it can act on their behalf.
• Agent Credential Exchange — The handshake in which agents present, verify, and accept each other's credentials before doing business.
• Agent Delegation — Handing part of a task, and the authority needed to complete it, from a human to an agent or from one agent to another.
• Agent Framework — A software library or toolkit that simplifies the development of AI agents capable of planning, executing, and adapting tasks.
• Agent Identity — The verifiable answer to 'which agent is this?' — a stable, attestable identifier distinct from the human or service behind it.
• Agent Identity Protocol (AIP) — A protocol for establishing and proving an AI agent's identity to other agents, services, and users.
• Agent Memory — Information an agent retains beyond the current context window — episodic history, learned facts, preferences, and task state.
• Agent orchestration — Agent orchestration is the management of multiple AI agents or LLM calls to efficiently handle complex tasks.
• Agent Passport System (APS) — A scheme that issues verifiable, portable identity documents to AI agents so other systems can recognise and trust them across domains.
• Agent Protocol Dimensions — The distinct axes any agent-to-agent protocol must cover: identity, authorization, transport, semantics, evidence, and lifecycle.
• Agent Protocol Semantics — The shared meaning layer of an agent protocol — what messages, verbs, and capability names actually denote.
• Agent Revocation — Withdrawing an agent's identity, credentials, or capabilities so it can no longer act — immediately and verifiably.
• Agent Sandbox — A controlled, isolated environment used to safely develop, test, and evaluate the behavior of AI agents without impacting external systems.
• Agent Transparency — Making an agent's identity, authority, reasoning, and actions legible to the humans and systems it interacts with.
• Agent Trust — The calibrated confidence a system or person places in an agent, grounded in verified identity, bounded authority, and observed behaviour.
• Agent-Based Model — A computational model simulating the actions and interactions of autonomous agents to observe system-level properties.
• Agent-to-Agent Intent — A structured statement of purpose one agent sends another so the request can be understood, authorized, and negotiated.
• Agentic — Agentic describes qualities associated with agents, such as autonomy and the ability to act independently.
• Agentic AI — AI systems designed to independently plan, execute, and adapt actions to achieve a given goal, often involving multiple steps and external tools.
• Agentic Commerce — Commerce driven by AI agents that compare, negotiate, and purchase on behalf of buyers or sellers.