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\section{These are all the glosary terms from my AI class Csce876 from UNL}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

$A^*$ Search  &amp;  The most widely-known form of the best-first
search, $A^*$ evaluates nodes using the evaluation function: \[ f(n)
= g(n) + h(n) \] $A^*$ has several nice properties. Using the
Tree-Search Algorithm and an {\em admissible} heuristic function we
have: \begin{itemize} \item Optimality \item Complete \item
Optimally efficient \end{itemize} If the heuristic function is
consistent, then we have: \begin{itemize} \item Optimality \item
Complete \end{itemize} \\ \hline

$C^*$  &amp;  This is the actual cost of the optimal solution path. \\
\hline

$f(n)$ (Evaluation Function)  &amp;  The evaluation function is "the"
problem-specific knowledge beyond the definition of the problem
itself that is used to determine which node is chosen for expansion
in the search process. \\ \hline

$g(n)$ (Cost to node function)  &amp;  This function gives the actual
cost to reach node $n$ on the current path. \\ \hline

$h(n)$ (Heuristic function)  &amp;  The estimated cost of the cheape4st
path from $n$ to a (the) goal. \\ \hline

$k-$consistency  &amp;  A CSP is $k-$consistent if, for any set of $k-1$
variables and for any consistent assignment to those variables, a
consistent value can always be assigned to any $k^{th}$ variable.
CITATION(aitextbook) (p 147) \\ \hline

$n$-queens problem  &amp;  The the goal of the $n-queens$ problem is to
place $n$ queens on an $n \times n$ chess board where no queen is in
a position to attack another queen. \\ \hline

Abstraction (definition, goal, example)  &amp;  The process of removing
detail from a representation is called abstraction. We say the
abstraction is valid if we can expand any abstract solution into a
solution in the more detailed world. \\ \hline

Admissible Heuristic  &amp;  $h(n)$ is an admissible heuristic provided
that $h(n)$ never overestimates the cost to reach the goal. The
direct consequence of this is that $f(n)$ for $A^*$ never
overestimates the true cost of a solution through $n$. \\ \hline

Agent  &amp;  An agent is just something that acts CITATION(aitextbook) \\
\hline

Alldiff constraint  &amp;  A constraint that indicate that all involved
variables must be assigned a different value. CITATION(aitextbook)
(p 140) \\ \hline

Alliances  &amp;  Alliances occur in many games involving more than two
players. Alliances involve a group of players that are cooperating
in a competitive environment. CITATION(aitextbook) (p 166) \\ \hline

Alpha-beta pruning  &amp;  This procedure is based on a DFS. Consider a
node $n$ somewhere in the tree such that Player has a choice of
moving to that node. If Player has a better choice $m$ either at the
parent node of $n$ or at any choice point further up, then $n$ {\em
will never be reached in actual play}. So once we have found out
enough about $n$ (by examining its descendants in a DFS) to reach
this conclusion, we can prune it. Alpha-beta pruning gets its name
from the two parameters: \begin{description} \item[$\alpha =$] The
value of the best choice we have found so far at any choice point
along the path for $\max$. \item[$\beta =$] The value of the best
choice we have found so far at any choice point along the path for
$\min$. \end{description} CITATION(aitextbook) (p~168-169) \\ \hline

And-elimination  &amp;  $\frac{A \wedge B}{A}$ CITATION(aitextbook) (p
211)
\\ \hline

Anytime algorithm  &amp;  An anytime algorithm is an algorithm whose
output quality improves gradually over time, so that it has a
reasonable decision ready whenever it is interrupted.
CITATION(aitextbook) (p 971). \\ \hline

\end{tabular}

\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Arc consistency  &amp;  An arc from $X$ to $Y$ is consistent if, for
every value $x$ of $X$ there is some value $y$ of $Y$ that is
consistent with $x$. CITATION(aitextbook) (p 145) \\ \hline

Arity (of a relation or a function)  &amp;  Arity is the number of
arguments CITATION(aitextbook) (p 246) \\ \hline

Assertion  &amp;  A FOL logic sentence added to the KB are called an
assertion. CITATION(aitextbook) (p 253) \\ \hline

Atmost constraint  &amp;  The atmost constraint has a number $P$ and
variables $P_1,...,P_k$ that are assigned numbers such that
$\sum_{i=1}^{k} P_i \leq P$. CITATION(aitextbook) (p 148) \\ \hline

Atomic sentences  &amp;  Consists of a single proposition symbol.
CITATION(aitextbook) (p 204) \\ \hline

Autonomy (autonomous agent)  &amp;  Not relying on prior knowledge of
its designer, but rather on its own precepts. CITATION(aitextbook) \\
\hline

Axiom  &amp;  Axioms provide the basic factual information form which
conclusions can be derived. CITATION(aitextbook) (p 255) \\ \hline

Background knowledge  &amp;  This is the sentences of information that a
knowledge base is initialized with. CITATION(aitextbook) (p 196) \\
\hline

Backjumping  &amp;  The backjumping method backtracks to the most recent
variable in the conflict set. CITATION(aitextbook) (p 149) \\ \hline

Backtracking Search  &amp;  A variant of the depth-first search it uses
still less memory. In backtracking, only on successor is generated
at a time rather than all successors; each partially expanded node
remembers which successor to generate next. Thus the memory
requirement is only $O(m)$ instead of $O(bm)$. \\ \hline

Backtracking search  &amp;  The backtracking search is a depth-first
search that chooses values for one variable at a time and backtracks
when a variable has no legal values left to assign.
CITATION(aitextbook) (p 141) \\ \hline

Backward chaining  &amp;  Backward chaining starts with the query for
the fact $q$ and finds those implications in the knowledge base that
conclude $q$. If all of the premises of one of these implications
can be proved true (through backward chaining), then $q$ is true.
CITATION(aitextbook) (p 220) \\ \hline

Belief State  &amp;  When in a sensorless problem, the agent may only
know what states it can possibly start in. By applying the successor
function to each of these states it can determine a set of states
that could be reached by a specific action. The set of states that
the agent could be in at any one time is the {\em belief state}. \\
\hline

Best-first Search  &amp;  Best-first search is an instance of the
general Tree-Search or Graph-Search algorithm in which a node is
selected for expansion based on an evaluation function, $f(n)$. \\
\hline

Biconditional  &amp;  $A \Leftrightarrow B$ is a biconditional that
states that $A$ is true if and only if $B$ is true or alternately if
$A \Rightarrow B$ and $B \Rightarrow A$. CITATION(aitextbook) (p
205)
\\ \hline

Bidirectional search  &amp;  The strategy is to run two simultaneous
searches, one from the initial state forward and one from the goal
state backwards. This is not always possible, and requires that the
successor function be invertible. This should cut the search depth
by a factor of two so that the storage requirements are $O(b^{d/2})
&lt;&lt; b^d$. \\ \hline

Binary constraint  &amp;  A binary constraint relates two variables.
CITATION(aitextbook) (p 140) \\ \hline

Binding list  &amp;  When posing a query $\exists x~Predicate(x)$,
answering yes is not very helpful. The standard form of an answer to
such a query includes a set of values that satisfy the query. This
set is called a substitution or binding list. CITATION(aitextbook)
(p 254) \\ \hline

Blind Search  &amp;  See Uninformed search \\ \hline

Body (of a Horn clause)  &amp;  The negative literals of a horn clause
form the body. CITATION(aitextbook) (p 218) \\ \hline

Boolean CSPs  &amp;  Boolean CSPs are CSPs where the domains of the
variables are either true or false. CITATION(aitextbook) (p 139) \\
\hline

Bounded differences (constraint of)  &amp;  A bounded difference
constraint has the form of $const_1 ~~ \theta ~~ x-y ~~ \theta ~~
const_2$ where $\theta \in {&lt;,&gt;,\leq,\geq}$. CITATION(ainotes) \\
\hline

Bounds Propagation  &amp;  We say that a CSP is bounds-consistent if for
every variable $X$, and for both the lower bound and upper bound
values of $X$, there exists some value of $Y$ that satisfies the
constraint between $X$ and $Y$, for every variable $Y$. This kind of
constraint propagation is called {\em bounds propagation}.
CITATION(aitextbook) (p 148) \\ \hline

Branching Factor  &amp;  The {\em branching factor} is the maximum
number of successors of any node. \\ \hline

Breadth-first Search  &amp;  The BFS expands nodes from a FIFO queue.
That is, as each node is discovered it is put into a FIFO queue.
This results in all nodes being expanded at each level before a new
level is expanded. The storage requirement is $O(b^{d+1})$. This
requirement is prohibitive. \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Chinese room experiment  &amp;  The system consists of a human, who
understands only English (CPU), equipped with a rule book (PROGRAM),
written in English, and various stacks of paper, some blank, some
with indecipherable inscriptions (memory). The system is inside a
room with a small opening to the outside. Through the opening appear
slips of paper with indecipherable symbols. The human finds matching
symbols in the rule book, and follows the instructions. The
instructions may include writing symbols on new slips of paper,
finding symbols in the stacks, rearranging the stacks and so on.
Eventually, the instructions will cause one or more symbols to be
transcribed onto a piece of paper that is passed back to the outside
world with the correct response. Searle argues against the Turing
test in that just running the right program does not generate
understanding. CITATION(aitextbook) \\ \hline

Chronological backtracking  &amp;  The process of a backtrack search
where we backup to the preceding variable and try a different value
is called chronological backtracking, because the most recent
decision point is revisited. CITATION(aitextbook) (p 148) \\ \hline

Coercion  &amp;  Coercion is the ability of an agent to reach a goal
state even in a sensorless problem. The agent knows that it starts
in any of the states but can reach a goal by reasoning about sets of
states. See the Vacuum cleaner agent on p84 for a good example. \\
\hline

Cognitive science  &amp;  The interdisciplinary field of cognitive
science brings together computer models from AI and experimental
techniques from psychology to try to construct precise and testable
theories of the workings of the human mind. CITATION(aitextbook) \\
\hline

Competitive Agent  &amp;  In a multiagent system, when one's performance
depends upon the performance of another in an inverse relationship,
then the two agents are in competition and may be called competitive
agents. \\ \hline

Complementary literals  &amp;  Literals such that one is a negation of
the other. CITATION(aitextbook) (p 214) \\ \hline

Complete-state formulation  &amp;  A {\em complete-state formulation}
starts with a complete proposed solution and then changes to it to
find a solution that satisfies the goal test. \\ \hline

Completeness  &amp;  If the exists a solution, then the algorithm is
guaranteed to find a solution. \\ \hline

Completeness (of inference)  &amp;  An inference $i$ is complete if
whenever KB $\models \alpha$, it is also true that KB $\vdash_i
\alpha$. That is, the procedure will answer any question whose
answer follows from what is know by the KB. CITATION(ainotes)
(10.23)
\\ \hline

Compositional (languages)  &amp;  In a compositional language, the
meaning of a sentence is a function of the meaning of its parts.
CITATION(aitextbook) (p 241) \\ \hline

Compositionality  &amp;  In a compostional language, the meaning of a
sentence is a function of the meaning of its parts.
CITATION(aitextbook) (p 241) \\ \hline

Condition-action rule  &amp;  A condition action rule is a tuple
(Condition, Action). When the condition is matched by the sensor
input, the Simple Reflex agent will perform the "Action". \\ \hline

Conflict set  &amp;  A more intelligent approach to backtracking than
chronological backtracking is to go all the way back to one of the
set of variables that {\em caused the failure}. This set is called
the {\bf conflict set}. CITATION(aitextbook) (p 148) \\ \hline

Conflict-directed backjumping  &amp;  The backtracking method that backs
up to the source of a conflict that prohibits the finding of a
consistent assignment to the remaining variables.
CITATION(ainotes,aitextbook) (p 149) \\ \hline

Conjunctive normal form  &amp;  A sentence expressed as a conjunction of
disjunctions of literals is said to be in conjunctive normal form.
CITATION(aitextbook) (p 215) \\ \hline

Connectionism  &amp;  Connectionism models mental or behavioral
phenomena as the emergent processes of interconnected networks of
simple units. There are many different forms of connectionism, but
the most common forms utilize neural network models.
CITATION(Wikipedia:Connectionism) \\ \hline

Consistency (as a property of the $h$ function)  &amp;  A heuristic
$h(n)$ is consistent if, for every node $n$ and every successor $n'$
of $n$ generated by any action $a$, the estimated cost of reaching
the goal from $n$ is no greater than the step cost of getting to
$n'$ plust the estimated cost or reaching the goal from $n'$: \\
\hline

Consistent assignment  &amp;  An assignment that does not violate any
constraints is called a consistent or legal assignment.
CITATION(aitextbook) (p 137) \\ \hline

Constant symbol  &amp;  A symbol in FOL that stand for objects.
CITATION(aitextbook) (p 246) \\ \hline

Constraint graph  &amp;  A graph where nodes correspond to the variables
of the problem and arcs correspond to the constraints.
CITATION(aitextbook) (p 138) \\ \hline

Constraint hypergraph  &amp;  A constraint hypergraph contains two types
of nodes: variables and constraints. arcs from constraint nodes to
variable nodes indicate that the variable is involved in the
constraint. This is used for representing constraints with an arity
greater than 2. CITATION(ainotes) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Constraint propagation  &amp;  This is the general term for propagation
the implications of a constraint on one variable onto other
variables. CITATION(aitextbook) (p 145) \\ \hline

Constraint scope  &amp;  The scope of a constraint is the set of
variables that are involved in the constraint CITATION(ainotes). \\
\hline

Constructive Search  &amp;  This is the standard search process were we
assign a value to a variable at each node starting from the first
variable to the last. CITATION(ainotes). \\ \hline

Contingency problem  &amp;  If the environment is partially observable
or if actions are uncertain, then the agent's percepts provide new
information after each action. Each possible percept defines a {\em
contingency} that must be planned for. A contingency problem is {\em
adversarial} if the uncertainty is caused by the actions of other
agents. \\ \hline

Continuous domains  &amp;  Where the variables require continuous values
such as the real numbers we say we are using continuous domains.
Problems that require precise values often require continuous
domains. CITATION(aitextbook) (p 139) \\ \hline

Contours  &amp;  The fact that $f$-costs are nondecreasing along any
path also means that we can draw contours in the state space, just
like the contours in a topographic map. \\ \hline

Crossover  &amp;  In genetic algorithms when you cross two states you
choose a point to split the states and "cross" the two states. State
1's first half with state 2's second half, and similarly for the
second state. The split point is called the "crossover" point. \\
\hline

Current State  &amp;  Local search algorithms operate using a single
"current state" and generally move only to neighboring states. The
current state is then a complete description of a solution that may
not meet all of the constraints or may not be optimal. (Of course it
could be optimal too, but then it would be a solution) \\ \hline

Cutset conditioning  &amp;  Cutset conditioning is the process of
finding the smallest cycle cutset. CITATION(aitextbook) (p 154) \\
\hline

Cycle cutset  &amp;  A subset $S$ called the cycle cutset is a set of
variables of a CSP such that the constraint graph becomes a tree
after the removal of $S$ CITATION(aitextbook) (p 153). \\ \hline

Data driven  &amp;  Data driven reasoning is reasoning in which the
focus of attention starts with the known data. CITATION(aitextbook)
(p 219) \\ \hline

Data Mining  &amp;  Data mining, also known as knowledge-discovery in
databases (KDD), is the practice of automatically searching large
stores of data for patterns. CITATION(Wikipedia:Datamining) \\
\hline

Decision problem  &amp;   Is any problem that must be answered yes or
no. \\ \hline

Deduction  &amp;  Logical inference. That is to use the rules of logic
to infer a conclusion. We say a conclusion is reached by deduction
if it is logically inferred by the premise(s). \\ \hline

Deduction theorem  &amp;  For any sentences $\alpha$ and $\beta$,
$\alpha \models \beta$ if and only if the sentence $(\alpha
\Rightarrow \beta)$ is valid. CITATION(aitextbook) (p 210) \\ \hline

Definite clause  &amp;  Horn clauses with exactly one positive literal
are called definite clauses. CITATION(aitextbook) (p 218) \\ \hline

Degree (ordering heuristic)  &amp;  Attempts to reduce the branching
factor by selecting the variable that is involved in the largest
number of constraints. CITATION(aitextbook) (p~144) \\ \hline

Depth-First Search  &amp;  This strategy expands nodes from a LIFO
queue, also known as a stack. This results in nodes being expanded
as deep as they can before exploring other nodes at the same level.
Thus it is called the DFS. The space requirements for DFS are
$O(bm)$. There is no guarantee that this will ever find a goal state
because it may get caught in a loop. \\ \hline

Depth-Limited Search  &amp;  This is just a DFS search where we limit
the depth that the search may descend to. It is also not guaranteed
to find a solution in general, but we may guarantee a goal state is
found if we know a goal exists within a certain depth. This has the
same requirements as DFS \\ \hline

Diameter (of a graph)  &amp;  Given a graph $G=(V,E)$, the distance
between two vertices $u$ and $v$ in $G$, denoted $d(u,v)$, is the
length of the shortest $u-v$ path in $G$. The {\em diameter} of $G$,
denoted diam$(G)$, is the maximum distance between any two vertices
in $G$. CITATION(GraphTheoryThulasiramanSwamy) \\ \hline

Domain (of a model)  &amp;  The domain of a model is the set of objects
that it contains. CITATION(ainotes) (p 245) \\ \hline

Domain (of a variable)  &amp;  Given a CSP the domain of a variable are
the values that the variable may be assigned
CITATION(ainotes,aitextbook) (p 137) \\ \hline

Domain/degree heuristics (not in textbook)  &amp;  The degree heuristic
chooses the variable to assign that is involved in the largest
number of constraints on other unassigned variables.
CITATION(aitextbook) (p 144 - it is in the book) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Domination (of two functions)  &amp;  We say a heuristic function
$h_1(n)$ dominates another heuristic function $h_2(n)$ if \[ h_1(n)
\geq h_2(n) \] \\ \hline

Effective branching factor CITATION(ainotes)  &amp;  One way to
characterize the quality of a heuristic is using the {\em effective
branching factor}. In words this is the the branching factor that a
uniform tree of depth $d$ would have in order to contain $N+1$
nodes. Where $d$ is the depth of the search tree and $N$ is the
number of nodes generated. That is at each depth starting at $0$ we
have: \[ N = 1 + b^* + (b^*)^2 + ... + (b^*)^d =
\frac{(b^*)^{d+1}-1}{b^* - 1} \] \\ \hline

Entailment  &amp;  Let KB be a knowledge base of sentences and let
$\alpha$ be a sentence. $\alpha$ is entailed by a KB if the models
of the KB are {\em all} models of $\alpha$. CITATION(ainotes) (11.5)
For my own notes: Entailment is similar to implication except the
domain of the variables are sentences (or propositions). KB $\models
\alpha$ iff all models of KB are models of $\alpha$. ($i.e., M(KB)
\subseteq M(\alpha)$) CITATION(ainotes) (11.5) \\ \hline

Environment: Deterministic vs. Stochastic  &amp;  If the next state of
the environment is completely determined by the current state and
the action executed by the agent, then we say the environment is
deterministic, otherwise we say it is stochastic. \\ \hline

Environment: Discrete vs. Continuous  &amp;  The discrete/continuous
distinction can be applied (1) to the state of the environment - is
it constantly changing?, (2) to the way {\em time} is handled - is
it continuous in the application or is it handled as discrete
points?, and (3) to the {\em percepts} and {\em actions} of the
agent - How can the agent perceive the environment, and how does it
act (continuously or discretely)?. \\ \hline

Environment: Episodic vs. Sequential  &amp;  If the agents experience is
broken up into atomic episodes that do not affect actions in
previous episodes, we call it episodic. If on the other hand current
actions depend upon the sequence of actions taken previously, then
we call the task environment sequential. \\ \hline

Environment: Observable: Fully vs Partially  &amp;  A task environment
is effectively fully observable if the sensors detect all aspects
that are {\em relevant} to the choice of action. Otherwise we say
the task environment is partially observable. \\ \hline

Environment: Single agent vs. Multiagent  &amp;  The key distinction is
whether an agent's behavior is best described as maximizing a
performance measure whose value depends on other agents' behavior.
\\ \hline

Environment: Static vs. Dynamic  &amp;  If the environment can change
while the agent is deliberating, we say the environment is
\textbf{dynamic}. If the environment does not change while the agent
is deliberating, we call the environment \textbf{static}. If the
score changes (decreases) as the agent deliberates in a static
environment, we call the environment \textbf{semidynamic}. \\ \hline

Environment: Strategic  &amp;  If the environment is deterministic
except for the actions of other agents, we say that the environment
is strategic. (This was under Deterministic vs Stochastic in the
book.) \\ \hline

Epistemological level  &amp;  Abstract description of what the agent
knows about the world. CITATION(ainotes) (10.4) \\ \hline

Exploration Problem  &amp;  When the states and actions of the
environment are unknown (e.g. you are dropped somewhere where you
don't speak the language) the agent must act to discover them. This
type of problem can be viewed as an extreme case of the contingency
problem. \\ \hline

Extended interpretation  &amp;  An extended interpretation specifies a
domain element in addition to the mapping given by the
interpretation. CITATION(aitextbook) (p 249). \\ \hline

Extensively defined constraint (not in textbook)  &amp;  Extensionally
defined constraints: all allowed tuples are listed. This is
practical for defining arbitrary constraints. CITATION(ainotes) \\
\hline

Factoring  &amp;  Factoring is the last step in resolution and involves
removing multiple copies of literals from the expression.
CITATION(aitextbook) (p 214) \\ \hline

Finite domains  &amp;  Variables with finite (or discrete) domains are
the simplest kind of CSPs. CITATION(aitextbook) (p 139) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

First-Order Logic  &amp;  First-Order logic adds to propositional
\begin{itemize} \item symbols: Objects, properties, functions and
relations \item connectives: quantifiers $\exists$ and $\forall$
\end{itemize} CITATION(ainotes) (12.4,5) \\ \hline

Fitness function  &amp;   In genetic algorithms each state produced in
the next generation is evaluated or rated using a fitness function.
This determines how "good" the produced state is. \\ \hline

Fixed point  &amp;  The point at which applying inference rules produces
no new facts. CITATION(aitextbook) (p 218) \\ \hline

Forward chaining  &amp;  If all the premises of an implication are
known, then its conclusion is added to the list of known facts. The
process continues until the desired fact is known. This process of
chaining horn clauses together is called forward chaining.
CITATION(aitextbook) (p 218) \\ \hline

Forward Checking  &amp;  Whenever a variable $X$ is assigned, the
forward checking process looks at each unassigned variable $Y$ that
is connected to $X$ by a constraint and deletes from $Y$'s domain
any value that is inconsistent with the value chosen for $X$.
CITATION(aitextbook) (p 144) \\ \hline

Fringe  &amp;  The collection of nodes that have been generated but not
yet expanded is called the fringe (p 70). \\ \hline

Function  &amp;  In terms of FOL, a function is a relation in which
there is only one "value" for a given "input." CITATION(aitextbook)
(p 242) \\ \hline

Function (mathematical definition)  &amp;  A relation $f:A \rightarrow
B$ is said to be a function if and only if $f$ maps each $a \in A$
to only one value in $B$. (Dr. Anderson) \\ \hline

Genetic algorithm  &amp;  A genetic algorithm is a variant of stochastic
beam search in which successor states are generated by combining two
parent states. \\ \hline

Global constraint  &amp;  Involves all the variables in a CSP.
CITATION(ainotes) \\ \hline

Global optimum (maximum or minimum)  &amp;  If elevation in a state
space landscape corresponds to cost, then the aim is to find the
lowest valley (minimum). If the landscape corresponds to an
objective function, then the aim is to find the highest peak. \\
\hline

Goal Test  &amp;  The goal test determines whether a given state is a
goal state. \\ \hline

Goal-directed reasoning  &amp;  Goal directed reasoning is useful for
answering specific questions such as "What shall I do now?" and
"Where are my keys?" Backward chaining is an example of goal
directed reasoning. CITATION(aitextbook) (p 220) \\ \hline

Gradient ascent  &amp;  The gradient defines the direction of the
steepest ascent (in this case) or descent. (From calculus) \\ \hline

Greedy Algorithm  &amp;  A greedy algorithm always makes the choice that
looks best at the moment. CITATION(IntroToAlgorithms) The Best-first
search, also called the "greedy search" or "greedy best-first
search", tries to expand the node that is closest to the goal, on
the grounds that this is likely to lead to a solution quickly. Thus
it evaluates nodes by using just the heuristic function \[ f(n) =
h(n) \] \\ \hline

Greedy local search  &amp;  Hill climbing is sometimes called a greedy
local search because it grabs a good neighbor state without thinking
ahead about where to go next. \\ \hline

Ground term  &amp;  A term with no variables is called a ground term.
CITATION(aitextbook) (p 249) \\ \hline

Grounding  &amp;  Grounding is the process of determining if the logical
reasoning processes and the real environment in which the agent
exists are consistent. The question is, "how do we know that KB is
true in the real world?" Of course if you worried about this, you
would never have time to worry about anything else.
CITATION(aitextbook) (p 204) \\ \hline

Hanhatten distance &amp;  This is the sum distances to the object in
each dimension. Sometimes called the city block distance because you
can't cut through the buildings. \\ \hline

Head (of a Horn clause)  &amp;  The positive literal of a horn clause is
called the head. CITATION(aitextbook) (p 205) \\ \hline

Heuristic Function  &amp;  See $h(n)$. \\ \hline

Heuristic Search  &amp;  See "Informed search" \\ \hline

Higher-Order Logic  &amp;  Higher-Order logic views the relations and
functions in first-order logic as objects in themselves.
CITATION(aitextbook) (p 244) \\ \hline

Hill climbing: first choice  &amp;  Implements stochastic hill climbing
by generating successor randomly until one is generated that is
better than the current state. \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Hill climbing: random restart  &amp;  Because hill climbing will find it
harder and harder to find a better state as time goes on, we may
stop the search process and start it from a new random location.
This is called random restart. \\ \hline

Hill climbing: stochastic  &amp;  Stochastic hill climbing chooses at
random from among the uphill moves where the probability of
selection can vary with the steepness of the uphill move. \\ \hline

Horn clauses  &amp;  A horn clause is a disjunction of literals of which
at most one is positive. CITATION(aitextbook) (p 204) \\ \hline

Incremental formulation  &amp;  An incremental formulation involves
operations that augment or {\em build} the state description. The
goal is to build a state that satisfies the goal test and at any
point in the process, if we find we can not satisfy the goal state
using the last augmentation, we may back track.\\ \hline

Induction  &amp;  General rules aquired by exposure to repeated
associations between their elements. CITATION(aitextbook) \\ \hline

Inference  &amp;  Deriving new sentences from old. In the case of
logical agents, inference must obey the fundamental requirement that
when one asks a question of the knowledge base, the answer should
follow from what has been told the knowledge base previously.
CITATION(aitextbook) (p 195) \\ \hline

Infinite domains  &amp;  Domains where the size of the set is infinite.
This can include discrete domains such as the integers.
CITATION(aitextbook) (p 139) \\ \hline

Informed Search  &amp;  Strategies that know whether one non-goal state
is more promising than another non-goal state are called informed or
heuristic searches. \\ \hline

Initial State  &amp;  The state that the agent starts in. \\ \hline

Instantiated variable  &amp;  A variable is instantiated if it has been
assigned a value from its domain. CITATION(ainotes) \\ \hline

Intended interpretation  &amp;  The intended interpretation is the one
possible interpretation that we specify. CITATION(aitextbook) (p
246)
\\ \hline

Intensively defined constraint (not in textbook)  &amp;  Intensionally
defined constraints are used when it is not practical or even
possible to list all tuples. This is the form we normally see using
variables and relationship predicates. CITATION(ainotes) \\ \hline

Interpretation  &amp;  The semantics of FOL must relate sentences to
models in order to determine truth. An interpretation specifies
exactly which objects, relations and functions are referred to by
the constant, predicate and function symbols.CITATION(aitextbook) (p
246) An {\em interpretation} for a set $X$ of formulas {\bf is a
domain} $D$ together with a rule that \begin{itemize} \item assigns
to each $n$-place predicate symbol (that occurs in a formula) of $X$
an $n$-place predicate in $D$; \item assigns to each $n$-place
operation symbol of $X$ an $n$-place operation in $D$; \item assigns
to each constant symbol of $X$ an element of $D$; \item assigns to
$=$ the identity predicate $=$ in $D$, defined by: $a=b$ is true if
and only if $a$ and $b$ are the same. CITATION(FOML) \end{itemize} \\
\hline

Intractability  &amp;  We say an algorithms is intractable if the time
to run the algorithm increases exponentially as a function of the
size of the problem. (From 310 or 823?) \\ \hline

Intractability  &amp;  A problem is called intractable if the time
required to solve instance of the problme grows exponentially with
the size of the instances. (p 8) \\ \hline

Iterative-Deepening Search  &amp;  Also called the {\em Iterative
deepening depth-first search}, this is just a DFS performed to a
depth $d$ where $d$ iteratively increases from $0$ to some limiting
value $l$ until a goal is found. this will occur when $d$ reaches
the shallowest goal state. Space complexity will be the same as the
DFS $O(bd)$. Unlike DFS it is complete and finds the optimal goal
when the path cost is a non-decreasing function of the depth of the
node. {\em In general, iterative deepening is the preferred
uninformed search method when there is a large search space and the
depth of the solution is not known}. \\ \hline

Iterative-Lengthening Search  &amp;  Identical to the
Iterative-Deepening Search except that the search uses an increasing
path-cost limit instead of an increasing depth limit. This
guarantees optimality while avoiding expensive memory requirements.
\\ \hline

k-CNF  &amp;  A sentence expressed as a conjunction of disjunctions of
literals is said to be in conjunctive normal form. A sentence is
$k-$CNF if it has exactly (or at most) $k$ literals per clause.
CITATION(aitextbook) (p 215) \\ \hline

\end{tabular}

\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Knowledge acquisition  &amp;  Knowledge acquisition is the process of
acquiring knowledge. This can be done by exploration or by (p261)
applying to an outside expert to retrieve the knowledge required. \\
\hline

Knowledge base  &amp;  A set (conjunction) of sentences that represent
facts about the world. CITATION(aitextbook) (p 195) \\ \hline

Knowledge representation  &amp;  One of the six disciplines of AI
dedicated to the "representation" of knowledge that an entity knows
or senses. CITATION(aitextbook) \\ \hline

Knowledge representation language  &amp;  A formal language used to
represent sentences of information in the knowledge base.
CITATION(aitextbook) (p 195) \\ \hline

Leaf Node  &amp;  A leaf node is an element of the fringe, that is, a
node with no successors in the tree. \\ \hline

Limited rationality  &amp;  It is not always possible to do precisely
the right thing in complicated environments do to high computational
demands. CITATION(aitextbook) \\ \hline

Linear constraints  &amp;  Constraints in which each variable appears
only in linear form. CITATION(ainotes) \\ \hline

Linear Programming  &amp;  Linear programming involves CSPs where
constraints must be linear inequalities forming a convex region.
CITATION(aitextbook) p 140 \\ \hline

Literal  &amp;  A literal is single symbol or its negation.
CITATION(aitextbook) (p 204) \\ \hline

Local Optimum  &amp;  This corresponds to a peak or a valley in the
state space landscape that is not the best solution. This is were a
search may get stuck and need a random restart. \\ \hline

Local Search  &amp;   Local searches do not care about the path too the
solution, and so they don't keep track of where they have been. The
use very little memory and they can often find solutions in large or
infinite (continuous) state spaces for which systematic algorithms
are unavailable. \\ \hline

Logical connectives  &amp;  Not, conjunction, disjunction implication,
biconditional which are respectively: $\neg \wedge, \vee,
\Rightarrow, \Leftrightarrow$. CITATION(aitextbook) (p 204-205) \\
\hline

Logical equivalence  &amp;  Two sentences $A$ and $B$ are logically
equivalent if they are true in the same set of models. This is
written: $A \equiv B$. CITATION(aitextbook) (p 210) \\ \hline

Min-conflict heuristic  &amp;  The min-conflicts heuristic chooses a
value for a variable that causes the fewest conflicts with other
variables. CITATION(ainotes) \\ \hline

Minimax algorithm  &amp;  The minimax algorithm computes the minimax
decision form the current state. It uses a simple recursive
computation of the minimax values of each successor state, directly
implementing the defining equations. The recursion proceeds all the
way down to the leaves of the tree, and then the minimax values are
backed up through the tree as the recursions unwinds.
CITATION(aitextbook) (p 165) \\ \hline

Minimax decision  &amp;  When the Minimax value has been computed for
each child node in the tree for the current node, the Minimax
decision is the largest value of the child nodes. That is we choose
the action that is the optimal choice (max value) because it
guarantees the highest minimum value. CITATION(aitextbook) (p 165) \\
\hline

Minimax value &amp;
\begin{equation}
Minimax\text{-}Value(n)=\left\{
\begin{array}[c]{ll}
Utility\left(n\right)                    &amp; \text{if }n\text{ is a terminal state}\\
\max_{s\in Succ(n)}Minimax\text{-}Val(s) &amp; \text{if }n\text{ is a }\max\text{ node}\\
\min_{s\in Succ(n)}Minimax\text{-}Val(s) &amp; \text{if }n\text{ is a
}\min\text{ node.}
\end{array}
\right.
\end{equation}
\\ \hline

Minimum remaining values (a.k.a. least domain variable)  &amp;  A
heuristic that chooses the variable with the fewest legal
values.CITATION(aitextbook) (p 143) \\ \hline

Missionaries and cannibals problem  &amp;  Three missionaries and three
cannibals are on one side of a river, along with a both that can
hold one or two people.. Find a way to get everyone to the other
size without ever leaving a group of missionaries in one place
outnumbered by the cannibals in that place (p 90). \\ \hline

Model (in general)  &amp;  A model is a world in which a sentence is
true under a particular interpretation. CITATION(ainotes) (11.4) \\
\hline

Model checking  &amp;  Given a knowledge base KB and a sentence
$\alpha$, model checking enumerates all possible models to check
that a sentence $\alpha$ is true in all models in which KB is true.
OR, model checking ensures that $\alpha$ is true in each model
contained in KB. CITATION(aitextbook) (p 202) \\ \hline

Model in propositional logic  &amp;  A model is a mapping from
proposition symbols directly to truth or falsehood. The models of a
sentence are the mappings that make the sentence true.
CITATION(ainotes) \\ \hline

Modus ponens  &amp;  $\frac{\alpha \Rightarrow \beta,~~ \alpha}{\beta}$
CITATION(aitextbook) (p 211) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Monotonicity (as a property of the $h$ function  &amp;  A heuristic
function $h(n)$ is said to be monotone if it satisfies: \[ h(n) \leq
c(n,n') + h(n') \forall n,n' | n' \in SCS(n) \] where $SCS(n)$ gives
the successors of $n$. \\ \hline

Monotonicity (of inference)  &amp;  Monotonicity says that the set of
entailed sentences can only increase as information is added to the
knowledge base. CITATION(aitextbook) (p 213) \\ \hline

Mutation  &amp;   In genetic algorithms after a crossover between two
states has been completed we will sometimes randomly change the
state by randomly changing one element of the state. This is called
a mutation. \\ \hline

Natural Language Processing  &amp;  The ability of the computer to
process English or some other natural language to communicate
successfully. CITATION(aitextbook) \\ \hline

Node consistency  &amp;  A term meaning that a CSP is 1-consistent, or
that any node by itself is consistent. CITATION(aitextbook) (p 147) \\
\hline

Node expansion (in search)  &amp;  After we examine a state (node) $x$
and determine that it is not a goal state, we determine the
successor states (nodes) using the successor function. These set of
states (nodes) are the expansion of $x$ (p 69). \\ \hline

NP-completeness  &amp;  The theory of NP-completeness, provides a method
to reconize an intractable problem. Any problem class to which the
class of NP-complete problems can be reduced is likely to be
intractable. Cook and Karp showed the existence of large classes of
canonical combinatorial search and resoning problems that are
NP-complete. CITATION(aitextbook) \\ \hline

Objective function  &amp;  Objective functions are part of the
description of an optimization problem that measures states to
determine the best state. (from Glossary 6) \\ \hline

Objective function (in optimization problem)  &amp;  Objective functions
are part of the description of an optimization problem that measures
states to determine the best state. \\ \hline

Occam's (or Ockham's) razor  &amp;  is a principle attributed to the
14th-century English logician and Franciscan friar, William of
Ockham. It forms the basis of methodological reductionism, also
called the principle of parsimony or law of economy. In its simplest
form, Occam's Razor states that one should make no more assumptions
than needed. Put into everyday language, it says: Given two equally
predictive theories, choose the simpler.
CITATION(Wikipedia:OccamsRazor) \\ \hline

Omniscience  &amp;  Literally "all knowing". In the AI sense it means
knowing all the information necessary to make the best choice that
optimizes the performance measure. \\ \hline

Open-loop  &amp;  When given a {\bf static}, {\bf observable}, {\bf
discrete}, {\bf deterministic} environment, solutions can be
executed without pay attention to the percepts. Thus an agent
carries out its plan with its eyes closed. Control theorists call
this an {\bf open-loop} because it breaks the loop between agent and
environment. \\ \hline

Operator  &amp;  Successor functions gives a description of the possible
actions available to the agent. An alternate formulation uses a set
of operators that can be applied to a state to generate successors.
(p 62) \\ \hline

Optimal solution  &amp;  According to AIAMA: The optimal solution has
the lowest path cost among all solutions. \\ \hline

Optimally efficient  &amp;  If an algorithm is optimally efficient it
means that no other optimal algorithm is guaranteed to expand fewer
nodes except possibly through tie-breaking among nodes with
$f(n)=C^*$. {\em We note that $A^*$ is optimally efficient {\bf and}
that it is also exponential in practice.} \\ \hline

Optimization Problem  &amp;  Optimization problem define as a goal to
find the best state according to an objective function. \\ \hline

Path  &amp;  A {\em path} in the state space is a sequence of states
connected by a sequence of actions. \\ \hline

Path consistency  &amp;  Any pair of adjacent variables can always be
extended to a third neighboring variable - also called
$3-$consistency. CITATION(aitextbook) (p 147) \\ \hline

Path Cost  &amp;  A {\em path cost} function assigns a numeric cost to
each path from the root node to any node in the tree. \\ \hline

Pathmax Equation CITATION(astar)  &amp;  The PathMax function is an
optimization routine that ensures monotonicity: \[
\hat{f}(m)=\max\{f(n),f(m)\} \] for all $m \in \{succ(n)\}$. \\
\hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Payoff function  &amp;  Also called the {\bf objective function} or {\bf
utility function}, it gives the numeric value for the terminal
states. (Objective and utility functions generally give a value to
each state). CITATION(aitextbook) (p 163) \\ \hline

Percept Sequence  &amp;  An agent's percept sequence is the complete
history of everything the agent has ever perceived. \\ \hline

Performance Measure  &amp;  A performance measure embodies the criterion
for success of an agent's behavior. \\ \hline

Planning  &amp;  Planning is the process of finding a sequence that
achieves a desired effect given a suitable constructive inference
algorithm (p 330). \\ \hline

Plateau  &amp;  An area in the state space landscape where the
evaluation function is flat. I could be a flat local maximum or a
shoulder. \\ \hline

Ply  &amp;  This is essentially a turn in a turn taking game. One move
is made up of two different players in a two player game each taking
a turn. Each turn is called a half move or ply. CITATION(aitextbook)
(p 163) \\ \hline

Predecessors  &amp;  Predecessors of a state $x$ are all those states
that have $x$ as a successor. \\ \hline

Predicate symbol  &amp;  Stand for relations in FOL.
CITATION(aitextbook) (p 246) \\ \hline

Premise  &amp;  The premise or antecedent is the left hand side of an
implication ($\Rightarrow$). CITATION(aitextbook) (p 205) \\ \hline

Problem Formulation  &amp;  Problem formulation is the process of
deciding what actions and states to consider, given a goal. A
problem can be defined formally by four components:
\begin{enumerate} \setlength{\topsep}{0in} \setlength{\parsep}{0in}
\setlength{\parskip}{0in} \setlength{\itemsep}{0in} \item initial
state \item successor function \item goal test \item path cost
\end{enumerate} \\ \hline

Proof  &amp;  A sequence of applications of inference rules is called a
proof. CITATION(aitextbook) (p 212) \\ \hline

Property  &amp;  Unary relations CITATION(aitextbook) (p 242) \\ \hline

Proposition symbol  &amp;  A symbol that can be assigned true or false
which represents a proposition (or statement). CITATION(aitextbook)
(p 204) \\ \hline

Propositional logic  &amp;  Propositional logic is a very simple
language consisting of proposition symbols and logical connectives.
It can handle propositions that are known true, known false, or
completely unknown. CITATION(aitextbook) (p 233) Propositional logic
is made up of a set of symbols and boolean connectives $\wedge,
\vee, \neg, \Rightarrow, \Leftrightarrow$ formed into sentences
using the formal grammar form: Backus-Naur Form. The semantics are
defined by the operation of the connectives as either True or False.
CITATION(ainotes) (11.3, 11.7) \\ \hline

Pruning  &amp;  When a node $n'$ is explored and placed in the fringe as
part of a node expansion of $n$ and $h(n') &gt; h(n)$ then $A^*$ will
not never expand this node, because $f(n') &gt; C^*$. When this happens
we say that $n'$ is {\em pruned}. \\ \hline

Pruning (in general)  &amp;  Pruning is the process of eliminating
possibilities from consideration without having to examine them. In
a tree structure we often say that we prune a subtree or branch by
eliminating it from consideration. CITATION(aitextbook) (p 100) \\
\hline

Rationality  &amp;  Rationality (restricted rationality) is the ideal
concept of intelligence (restrcited rationality is listed above) It
simply means to do the right thingCITATION(aitextbook) \\ \hline

Reduction ad absurdum  &amp;  Proving $\beta$ from $\alpha$ by checking
the unsatisfiability of $(\alpha \wedge \neg \beta)$ corresponds
exactly to the standard mathematical proof technique of {\em
reductio ad aburdum}. This is also called refutation: $\alpha
\models \beta$ if and only if the sentence $(\alpha \wedge \neg
\beta)$ is unsatisifiable. CITATION(aitextbook) (p 211) \\ \hline

Refutation completeness  &amp;  Any complete search algorithm, applying
only the resolution rule, can derive any conclusion entailed by any
knowledge base in propositional logic. There is a caveat: resolution
is complete in a specialized sense. Given that $A$ is true, we can
not use the resolution to automatically generate the consequence $A
\vee B$ is true. This is called {\bf refutation completeness},
meaning that resolution can always be used to either confirm or
refute a sentence, but it cannot be used to enumerate true
sentences. CITATION(aitextbook) (p 214) \\ \hline

Relation  &amp;  A relation between $A$ and $B$ is defined as $R_{A,B}
\subseteq A \times B$ or  $R_{A,B} \subseteq \{ (x,y) | x \in A, y
\in B\}$ CITATION(ainotes) \\ \hline

Relation (mathematical definition)  &amp;  A relation between $A$ and
$B$ is defined as $R_{A,B} \subseteq A \times B$ or  $R_{A,B}
\subseteq \{ (x,y) | x \in A, y \in B\}$ CITATION(ainotes) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Relaxed problem  &amp;  A problem with fewer restrictions on the actions
is called a {\em relaxed problem}. The cost of an optimal solution
to a relaxed problem is an admissible heuristic for the original
problem. \\ \hline

Resolution  &amp;  If $l_i$ and $m_j$ are complementary literals, then
we have resolution defined as: \begin{equation} \frac{l_1 \vee ...
\vee l_k, ~~~~ m_1 \vee ... \vee m_n} {l_1 \vee ... \vee l_{i-1}
\vee l_{i+1} \vee ... \vee l_k \vee m_1 \vee ... \vee m_{j-1} \vee
m_{j+1} \vee ... \vee m_n} \end{equation} CITATION(aitextbook) (p
214)
\\ \hline

Resolution closure  &amp;  Given a set of clauses $S$, resolution
closure is the set of all derivable clauses. CITATION(aitextbook) (p
217) \\ \hline

Route-finding problem &amp;  The Route-finding problem is defined in
terms of locations and transitions along links between them. \\
\hline

Satisfiability  &amp;  A sentence is satisfiable if it is true in some
model. CITATION(aitextbook) (p 211) \\ \hline

Search  &amp;  The search process consists of finding a path from the
initial state to a goal state (p 60). The essence of a search is to
check a state (node) to determine if it satisfies the goal state and
expanding it to find other options to examine later if it does not.
(p 69) \\ \hline

Search cost (vs. total cost)  &amp;  Search cost typically depends on
the time complexity , but can also include a term for memory usage.
Total cost combines the search cost and the path cost of the
solution found (p 72). \\ \hline

Search node  &amp;  The search node corresponds to the node representing
the initial state. (p69) \\ \hline

Search Strategy  &amp;  The {\em search strategy} determines which which
state to expand next. \\ \hline

Search Tree  &amp;  A tree structure generated from the initial state
and children found using the successor function defines the search
tree. \\ \hline

Semantics (of a logic)  &amp;  The semantics of the language defines the
truth of each sentence with respect to each possible world.
CITATION(aitextbook) (p 207) \\ \hline

Sensorless problem  &amp;  (Also called conformant problems) If an agent
has no sensors at all, then it could be in one of several possible
initial states, and each action might lead to one of several
possible successor states. To know that a goal state has been
reached, all states in the successor set must be goal states. \\
\hline

Sentence  &amp;  A sentence is a representation of a fact in the world
in a {\em formal language}. CITATION(ainotes) (10.3) \\ \hline

Sideway move  &amp;  When reaching a shoulder, we can not find a state
to move to that is better than the state we are in. To fix this we
allow moving to states that are equal in hopes of finding a way off
a shoulder. This is called a sideways move. \\ \hline

Simulated annealing  &amp;  This combines hill climbing with random walk
in such a way that the amount of randomness slowly decreases (cools)
over time. \\ \hline

Size of a CSP  &amp;  The size of a CSP is characterized by the number
of constraints. CITATION(ainotes) \\ \hline

Solution quality  &amp;  Solution quality is measured by the path cost
function. \\ \hline

Soundness (of inference)  &amp;  An inference algorithm that derives
only entailed sentences is called sound. CITATION(aitextbook) (p
203)
\\ \hline

State space  &amp;  Together, the initial state and successor function
implicitly define the state space which is the set of all states
reachable from the initial state. \\ \hline

Step Cost  &amp;  The step cost is the cost of taking an action $a$ to
go from state $x$ to state $y$ is denoted by $c(x,a,y)$. \\ \hline

Straight-line distance  &amp;  The shortest distance between two points.
This is an especially interesting heuristic, because it will always
be optimistic no mater how we define points and measures we use for
distance in our problem since the shortest distance between two
points is a straight line. \\ \hline

Strong $k-$consistency  &amp;  A graph is strongly $k-$consistent if it
is $i-$consistent for $i \in {1,..,k}$. CITATION(aitextbook) (p 147) \\
\hline

Substitution  &amp;  When posing a query $\exists x~Predicate(x)$,
answering yes is not very helpful. The standard form of an answer to
such a query includes a set of values that satisfy the query. This
set is called a substitution or binding list. CITATION(aitextbook)
(p 254) \\ \hline

Successor Function (in search) &amp;  The successor function is a
function $S:x \in {s_i} \rightarrow S(s_i):\{&lt;A_j , S_{i+1}^j&gt;^*\}$
where $s_i$ is a state, $A_j$ are actions and $S_{i+1}^j$ is a
state. [notes] \\ \hline

Syllogism  &amp;  A Syllogism is an inference in which one proposition
(conclusion) follows necessarily from two other premises. It is
irrefutable reasoning processes (or patterns) that consequently
always yield correct conclusions. CITATION(aitextbook) \\ \hline

\end{tabular}

\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Syntax (of a logic)  &amp;  Defines the sentences in a language
(grammar) CITATION(ainotes) (10.16) \\ \hline

Task Environment$^*$  &amp;  PEAS: Performance, Environment, Actuators,
Sensors. \\ \hline

Tautology  &amp;  A sentence that is valid. CITATION(aitextbook) (p 210) \\
\hline

Term  &amp;  A term is a logical expression that referes to an object.
CITATION(aitextbook) (p 248) \\ \hline

Terminal states  &amp;  States where the game has ended are called
terminal states. CITATION(aitextbook) (p 163) \\ \hline

Terminal test  &amp;  A terminal test determines when the game is over.
CITATION(aitextbook) (p 163) \\ \hline

Ternary constraint  &amp;  A constraint involving 3 variables.
CITATION(ainotes) \\ \hline

Theorem  &amp;  Theorems are sentences entailed by axioms.
CITATION(aitextbook) (p 255) \\ \hline

Total Turing test  &amp;  A human judge engages in a natural language
conversation with two other parties, one a human and the other a
machine; if the judge cannot reliably tell which is which, then the
machine is said to pass the test. To this "Turing test" we add a
video signal and the opportunity to pass physical object "through
the hatch" to get a total Turing test. \cite{Wikipedia:TuringTest,
aitextbook} \\ \hline

Touring Problem  &amp;  Visit every city (vertice) given at least once
starting and ending at a specific city. Or more precisely given a
graph $G=(V,E)$ and an initial vertex $v_0$ give a sequence of edges
such that each vertex is visited at least once (p 67). \\ \hline

Toy Problems  &amp;  A toy problem is intended to illustrate or exercise
various problem-solving methods. It can be given a concise, exact,
description and is easily used by different researchers to compare
the performance of algorithms. \\ \hline

Transpositions  &amp;  In games, repeated states occur frequently
because of transpositions which are different permutations of the
move sequence that end up in t6he same position (or state).
CITATION(aitextbook) (p 170) \\ \hline

Traveling Salesperson Problem (TSP - give a {\em formal} definition)
CITATION(NPCompleteness) &amp;  INSTANCE: A finite set
$C={c_1,c_2,...,c_m}$ of cities, a distance $d(c_i,c)j) \in Z^+$ for
each pair of cities $c_i,c_j \in C$, a bound $B \in Z^+$. QUESTION:
Is there a "tour" of all the cities in $C$ having total length no
more than $B$, that is, an ordering
$&lt;c_{\pi(1)},c|{\pi(2)},...,c_{\pi(m)}&gt;$ of $C$ such that
\begin{equation}
\left(\sum_{i=1}^{m-1}d(c_{(i)},c_{(i+1)})\right)+d(c_{\pi(m)},c_{\pi(1)}=B?
\end{equation} \\ \hline

Tree decomposition (of a CSP)  &amp;  The process of decomposing a
constraint graph into a tree of connected components. A tree
decomposition must satisfy the following three requirements: (1)
Every variable in the original problem appears in at least one of
the subproblems. (2) If two variables are connected by a constraint
in the original problem, they must appear together (along with the
constraint) in at least one of the subproblems. (3) If a variable
appears in two subproblems in the tree, it must appear in every
subproblem along the path connecting the those subproblems.
CITATION(aitextbook) (p 154) \begin{definition} A
\emph{tree-decomposition} of a graph $G$ is a tree $T$ whose nodes,
called bags, are subsets of $V\left( G\right) $ such that:
\begin{enumerate} \item $\cup _{x\in V\left( T\right) }X=V\left(
G\right) ;$ \item $\forall \left\{ u,v\right\} \in E\left( G\right)
$, $\exists X\in   V\left( T\right) $ such that $u,v\in X;$ and
\item $\forall X,Y,Z\in V\left( T\right) $, if $Y$ is on the path
from $X$   to $Z$ in $T$ then $X\cap Z\subseteq Y$. From Dourisboure
and Gavoille CITATION(doorisboure03) \end{enumerate}
\end{definition}
\\ \hline

Tree width of a graph (check a book on graph theory)  &amp;  Let $T$ be
a tree-decomposition fo a graph $G$. The width of $T$ is
\begin{equation} width(T) =\max_{X\in T}\{ \left\vert X\right\vert
-1\} \end{equation} Let $D$ be the set of all decompositions of the
tree $T$. Then the tree width is $\min_{T\in D}width\left( T\right)
$. CITATION(doorisboure03) \\ \hline

Triangle inequality  &amp;  In AI we say \[ h(n) \leq c(n, a, n') +
h(n') \] is the triangle inequality for paths from one node to
another in the search tree. \\ \hline

Truth table  &amp;  A truth table specifies the truth value of a complex
sentence for each possible assignment of truth values.
CITATION(aitextbook) (p 206) \\ \hline

\end{tabular}


\noindent \begin{tabular}[c]{|p{2in}|p{4.75in}|}\hline

Turing machine  &amp;  A Turing machine is a 7-tuple, $\left(  Q,\Sigma
,\Gamma,\delta,q_{0},q_{accept},q_{reject}\right)  $, where $Q$ is
the set of start states, $\Sigma$ is the input alphabet not
containing the blank symbol, $\Gamma$ is the tape alphabet, where
$\_\in\Gamma$ and $\Sigma\subseteq\Gamma $, $\delta :
Q\times\Gamma\rightarrow Q\times\Gamma\times\left\{ L,R\right\}  $
is the transition function, $q_{0}\in Q$ is the start state,
$q_{accept}\in Q$ is the accept state, and $q_{reject}\in Q$ is the
reject state, where $q_{accept}\neq q_{reject}$.
CITATION(IntroToTheTheoryOfComputation2) \\ \hline

Turing test  &amp;  A human judge engages in a natural language
conversation with two other parties, one a human and the other a
machine; if the judge cannot reliably tell which is which, then the
machine is said to pass the test. CITATION(Wikipedia:TuringTest) \\
\hline

Unary constraint  &amp;  The simplest kind of constraint which restricts
a single variable. CITATION(aitextbook) (p 140) \\ \hline

Uniform-cost search  &amp;  This strategy expands the node $n$ with the
lowest path cost. Thus this will use a $min-priority-queue$ to
determine which nodes to explore and expand next. If all step costs
are equal this is identical to the BFS. Let $C^*$ be the cost of the
optimal solution, and assume that every action costs at least
$\epsilon$. Then the algorithms's worst-case time and space
complexity is $O(b^{1+\lfloor C^*/\epsilon \rfloor})$ which can be
much greater than $b^d$. \\ \hline

Uninformed search  &amp;  These types of searches have no information
beyond what is given in the problem definition (this is also called
the blind search). All they can do is generate successors and
distinguish a goals state from a non-goal state. There are five
given in Section 3.4: BFS, Uniform-Cost Search, DFS, Depth-limited
Search, Iterative deepening depth-first search, and bidirectional
search. \\ \hline

Unit clause  &amp;  A unit clause is a cloause with just one literal.
CITATION(aitextbook) (p 221) \\ \hline

Universal constraint  &amp;  A statement that the two variable are not
involved in a constraint together. CITATION(ainotes) \\ \hline

Utility Function  &amp;  A utility function maps a state (or sequence of
states) onto a real number, which describes the associated degree of
happiness. This allows rational decisions to be made when goals are
inadequate. (1) When goals conflict and (2) When uncertainty is
attached to several goals the utility function can give a way to
determine which is goal is most likely achievable. \\ \hline

Validity  &amp;  A sentence is valid if it is true in all models.
CITATION(aitextbook) (p 210) \\ \hline

Validity (of a logical sentence)  &amp;  A sentence is valid if it is
true in all models. CITATION(ainotes) \\ \hline World

Value ordering heuristic  &amp;  Determines the order in which you
choose values to assign to a variable from the domain.
CITATION(aitextbook) (p 143) \\ \hline

Variable (of a CSP)  &amp;  Variables are part of a CSP that can take
values defined by their domains. CITATION(aitextbook) (p 137) \\
\hline

Variable ordering heuristic  &amp;  Determines the order in which you
assign variables. CITATION(aitextbook) (p 143) \\ \hline

Zero-sum games  &amp;  Means environments in which the utility values at
the end of the game are always equal and opposite.
CITATION(aitextbook) (p 161) \\ \hline

\end{tabular}

\bigskip
All definitions without a citation come from CITATION(aitextbook)

\end{document}
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