This is the start of a simple glossary of short definitions for AI terminology. The purpose is not to present the gory details, but to give a general idea.
A*:
A search algorithm to find the shortest path through a search space to a goal state using a heuristic. See also 'search', 'problem space', 'Admissibility', and 'heuristic'.
Admissibility:
An admissible search algorithm is one that is guaranteed to find an optimal path from the start node to a goal node, if one exists. In A* search, an admissible heuristic is one that never over estimates the distance remaining from the current node to the goal.
Agent:
"Anything that can can be viewed a perceiving its environment through sensors and acting upon that environment through effectors." [Russel, Norvig 1995]
Alpha-Beta Pruning:
A method of limiting search in the MiniMax algorithm. The coolest thing you learn in an undergraduate course.
Backward Chaining:
In a logic system, reasoning from a query to the data. See Forward chaining.
Belief Network (also Bayesian Network):
A mechanism for representing probabilistic knowledge. Inference algorithms in belief networks use the structure of the network to generate inferences effeciently (compared to joint probability distributions over all the variables).
Breadth-first Search:
An uninformed search algorithm where the shallowest node in the search tree is expanded first.
Case-based Reasoning:
Technique whereby "cases" similar to the current problem are retrieved and their "solutions" modified to work on the current problem.
Closed World Assumption:
The assumption that if a system has no knowledge about a query, it is false.
Data Mining:
Also known as Knowledge Discovery in Databases (KDD) was been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" in Frawley and
Piatetsky-Shapiro's overview. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to humans.
Depth-first Search:
An un-informed search algorithm where the deepest non-terminal node is expanded first.
Evaluation Function:
A function applied to a game state to generate a guess as to who is winning. Used by Minmax when the game tree is too large to be searched exhaustively.
Forward Chaining:
In a logic system, reasoning from facts to conclusions. See Backward Chaining.
Fuzzy Logic:
In Fuzzy Logic, truth values are real values in the closed interval [0..1]. The definitions of the boolean operators are extended to fit this continuous domain. By avoiding discrete truth-values, Fuzzy Logic avoids some of the problems inherent in either-or judgments and yields natural interpretations of utterances like "very hot". Fuzzy Logic has applications in control theory.
Iterative Deepening:
An uninformed search that combines good properties of both Depth-first and Breadth-first search.
Iterative Deepening A*:
The ideas of iterative deepening applied to A*.
Machine Learning:
A field of AI concerned with programs that learn. It includes Reinforcement Learning and Neural Networks among many other fields.
MinMax:
An algorithm for game playing in games with perfect information. See also alpha-beta pruning.
Modus Ponens:
An inference rule that says: if you know x and you know that 'If x is true then y is true' then you can conclude y.
Nonlinear Planning:
A planning paradigm which does not enforce a total (linear) ordering on the components of a plan.
Partial Order Planner:
A planner that only orders steps that need to be ordered, and leaves unordered any steps that can be done in any order.
Planning:
A field of AI concerned with systems that construct sequences of actions to achieve goals in real-world-like environments.
Problem Space (also State Space):
The formulation of an AI problem into states and operators. There is usually a start state and a goal state. The problem space is searched to find a solution.
Search:
The finding of a path from a start state to a goal state. See also 'Admissibility', 'Problem Space', and 'Heuristic'.
Strong AI:
Claim that computers can be made to actually think, just like human beings do. More precisely, the claim that there exists a class of computer programs, such that any implementation of such a program is really thinking.
Unification:
The process of finding a substitution (an assignment of constants and variables to variables) that makes two logical statements look the same.
Validation:
The process of confirming that one's model uses measureable inputs and produces output that can be used to make decisions about the real world.
Verification:
The process of confirming that an implemented model works as intended.
Weak AI:
Claim that computers are important tools in the modeling and simulation of human activity.
Department of Computer Science
University of California at Davis
Davis, CA 95616-8562