This is the start of a simple glossary of short definitions for AI
terminology.  The purpose is not to present the gorey details, but
give ageneral idea.

   A*:
	A search algorithm to find the shortest path through a search
	space to a goal state using a heuristic.  See '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
        overestimates 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]

   ai:
        A three-toed sloth of genus Bradypus. This forest-dwelling
        animal eats the leaves of the trumpet-tree and sounds a
        high-pitched squeal when disturbed. (Based on the Random House
        dictionary definition.)

   Alpha-Beta Pruning: 
        A method of limiting search in the MiniMax algorithm.  The
        coolest thing you learn in an undergraduate course.  If done
        optimally, it reduces the branching factor from B to the
        square root of B.

   Animat Approach:
        The design and study of simulated animals or adaptive real robots
        inspired by animals.  (From www-poleia.lip6.fr/ANIMATLAB - click on
        "English page")

   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.

   Computational Linguistics:
	The branch of AI that deals with understanding human language.  Also
	called natural language processing.

   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 uninformed search algorithm, where the deepest non-terminal
	node is expanded first.

   Embodiment:
        An approach to Artificial Intelligence that maintains that the
        only way to create general intelligence is to use programs
        with 'bodies' in the real world (i.e. robots).  It is an
        extreme form of Situatedness, first and most strongly put
        forth by Rod Brooks at MIT.

   Evaluation Function:
	A function applied to a game state to generate a guess as to
	who is winning.  Used by Minimax 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.

   Heuristic:
        The dictionary defines it as a method that serves as an aid to
        problem solving.  It is sometimes defined as any 'rule of
        thumb'.  Technically, a heuristic is a function that takes a
        state as input and outputs a value for that state- often as a
        guess of how far away that state is from the goal state.  See
        also: Admissibility, Search.

   Information Extraction:
	Getting computer-understanable information from human-readable
	(ie natural language) documents.
	
   Iterative Deepening:
	An uninformed search that combines good properties of
	Depth-fisrt and Breadth-first search.

   Iterative Deepening A*:
	The ideas of iterative deepening applied to A*.

   Language Acquisition:
	A relatively new sub-branch of AI; traditionally computational
	linguists tried to make computers understand human language by
	giving the computer grammar rules.  Language acquisition is a
	technique for the computer to generate the grammar rules itself.

   Machine Learning:
	A field of AI concerned with programs that learn.  It includes
	Reinforcement Learning and Neural Networks among many other
	fields. 

   MiniMax:
	An algorithm for game playing in games with perfect
	information.  See 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.

   Natural Language (NL):
	Evolved languages that humans use to communicate with one another.

   Natural Language Queries:
	Using human language to get information from a database.

   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 constuct sequences
	of actions to acheive 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
	'Admissibility', 'Problem Space', and 'Heuristic'.

   Situatedness:
        The property of an AI program being located in an environment
        that it senses.  Via its actions, the program can select its
        sensation input, as well as change its environment.
        Situatedness is often considered necessary in the Animat
        approach.  Some researchers claim that situatedness is key to
        understanding general intelligence.  (see Embodiment)

   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.