Operationality - Operationality is the suitability of a concept description for effective use by a particular performance system.
Operations research - Operations research is the use of mathematical models and analytical methods to support better decisions about complex systems.
Opportunistic reasoning - Opportunistic reasoning is a flexible approach to problem solving in which new information or intermediate results can change which useful reasoning step is...
Opportunity cost - Opportunity cost is the value of the best available alternative that is given up when a choice uses a scarce resource.
Optimal brain damage - Optimal Brain Damage is a neural-network pruning method that estimates how much a trained network's objective would increase if particular parameters were...
Optimal controllers - Optimal controllers select control actions that achieve the best value of a specified objective for a modeled system, subject to its constraints.
Optimal solution - An optimal solution is a feasible solution that achieves the best value of the objective defined for a problem.
Optimism under uncertainty - Optimism under uncertainty is an exploration principle that gives insufficiently understood actions the benefit of plausible favorable outcomes.
Optimizer's curse - The optimizer's curse is the tendency for an alternative selected because it has the highest noisy value estimate to perform worse, on average, than that...
Ordering constraints - Ordering constraints specify that one action or event must occur before another.
Ordinal utility function - An ordinal utility function assigns numbers to alternatives so that their ordering represents a decision-maker's preferences.
Orientation - Orientation describes the direction in which something is aligned relative to a chosen reference.
Origin function - An origin function, in the BLOG probabilistic modeling language, maps a generated object to an object that participated in its generation.
Out-of-bag error - Out-of-bag error is an estimate of predictive error obtained by evaluating each training example using only ensemble members whose bootstrap training...
Out of vocabulary - Out of vocabulary (OOV) describes an input item that is absent from a system's specified vocabulary.
PAC learning - PAC learning, short for probably approximately correct learning, is a mathematical framework for studying when an algorithm can learn a predictor from...
Parameter independence - Parameter independence is an assumption that specified groups of uncertain model parameters are probabilistically independent.
Parameter learning - Parameter learning is the process of estimating a model's numerical quantities from data while working within a specified model structure.
Parametric model - A parametric model is a model family described by a fixed finite set of parameters, for a specified model structure.
Parent node - A parent node is a node with a direct directed edge leading into another node. If the graph contains A → B, A is a parent of B and B is a child of A.
Pareto dominated - An option is Pareto dominated when another feasible option is at least as good on every specified objective and strictly better on at least one.
Pareto optimal - A feasible solution is Pareto optimal if no other feasible solution improves at least one specified objective without worsening any other.
Partial assignment - A partial assignment gives values to some of the variables in a problem while leaving others unassigned.
Partial information - Partial information means that an agent's available evidence does not determine every relevant aspect of the situation.
Partial-order planning - Partial-order planning is an approach to automated planning that specifies only the action orderings needed to support a valid plan.
Partial program - A partial program is a program specification that deliberately leaves some choices unresolved.
Partial solution - A partial solution is an intermediate candidate that specifies some, but not all, of what is needed to solve a problem.
Particle MCMC - Particle MCMC is a family of Monte Carlo methods that combines particle-based sequential simulation with Markov chain Monte Carlo to sample from difficult...
Passive learning - Passive learning, in the reinforcement-learning sense used here, means learning about the value or consequences of following a fixed policy.
Path - A path is an ordered sequence of connected locations, nodes or states. In AI search, it describes successive states linked by available actions.
Path planning - Path planning is the process of finding a feasible route from a starting configuration to a goal configuration while respecting obstacles and relevant...
Pattern matching - Pattern matching is the process of checking whether data fit a specified form and, where supported, extracting values from matching parts.
Payoff function - A payoff function assigns a numerical value to an outcome or combination of players' actions, representing how desirable it is to a particular player.
PD controller - A PD controller is a feedback controller that combines a proportional response to the current error with a derivative response to how that error is changing.
PDDL - PDDL stands for Planning Domain Definition Language.
PEAS - PEAS is a framework for describing an intelligent agent's task environment.
Peeking - Peeking, in machine-learning evaluation, means using information from a test set to influence model development or selection and then treating performance...
Percept - A percept is the input an agent receives from its sensors at a particular observation step.
Percept sequence - A percept sequence is the ordered history of observations an agent has received up to a particular point.
Perception - Perception is the process by which an AI system interprets sensory observations to form useful representations of objects, events or conditions.
Perceptual computing - Perceptual computing, in the computing-with-words tradition, is an approach to supporting subjective judgments by representing linguistic assessments...
Perfect Graph - A perfect graph is a finite simple undirected graph in which every induced subgraph can be colored using exactly as many colors as the size of its largest...
Perfect hash function - A perfect hash function maps every distinct key in a specified set to a different hash value, with no collisions within that set.
Perfect information - Perfect information is a property of a sequential game in which the player making a decision knows the history of play up to that point.
Perfect rationality - Perfect rationality is the ideal of always selecting an action that maximizes expected performance, given the information available to the agent and its...
Performance element - The performance element is the part of a learning agent that selects actions in response to its available observations and internal state.
Performance measure - A performance measure is the criterion used to evaluate how well an agent achieves the intended task.
Perpetual punishment - Perpetual punishment is a repeated-game strategy feature in which a detected deviation triggers a punishment phase that continues for every remaining round.