CS 4365 Exam 2

[Prolog] Negation in Prolog is negation by ____.

default

failure

proof

inference

success

failure

[Prolog] Consider the following program (all 5 facts):p(1,3,5). p(2,4,1). p(3,5,2). p(4,3,1). p(5,2,4).What is the result of the following query for Bag?

?- findall(Z,p(X,Y,Z),Bag).

Bag = 5

Bag = [p(1,3,5), p(2,4,1), p(3,5,2), p(4,3,1), p(5,2,4)]

Bag = [[1,3,5], [2,4,1], [3,5,2], [4,3,1], [5,2,4]]

Bag = [5, 1, 2, 1, 4]

Bag = [1, 2, 4, 5]

Bag = []

Bag = [5, 1, 2, 1, 4]

[Prolog] Consider the following op/3 predicate.?- op(500,yfx,'#').What is the result of the following query?

?- (A#B) = 1#2#3#4.

A = 1. B = 2 # 3 # 4.

A = 1 # 2. B = 3 # 4

A = 1 # 2 # 3. B = 4.

A = []. B = 1 # 2 # 3 # 4

error

A = 1 # 2 # 3. B = 4.

[Prolog] Write a prolog program (factorial/3 or factorial(N,A,F)) to compute a factorial F of an integer N, in tail-recursion with an accumulating variable A.

factorial(0,1,1).factorial(N,A,F) :- N > 0, A1 is N*A, N1 is N -1, factorial(N1,A1,F).

factorial(0,F,F).factorial(N,A,F) :- N > 0, A1 is N*A, N1 is N -1, factorial(N1,A1,F).

factorial(0,1,F).factorial(N,A,F) :- N > 0, A1 is N*A, N1 is N -1, factorial(N1,A1,F).

factorial(0,F,F).factorial(N,A,F) :- N > 0, A1 is N1*A, N is N1 -1, factorial(N1,A1,F).

factorial(0,F,F).factorial(N1,A,F) :- N1 > 0, A is N*A1, N1 is N -1, factorial(N,A1,F).

factorial(0,F,F).factorial(N,A,F) :- N > 0, A1 is N*A, N1 is N -1, factorial(N1,A1,F).

[Prolog] What is it called for the variable matching process in Prolog?

equalization

simplification

binding

back-tracking

unification

unification

[Prolog] What is the following mystery/2 about (using member/2)?

mystery([X|Y],M,[X|Z]) :- member(X,M), mystery(Y,M,Z).

mystery([X|Y],M,Z) :- \+ member(X,M), mystery(Y,M,Z).

mystery([],M,[]).

append

member tail-recursive

merge-sort

intersection

set-difference

subset

union

intersection

[Prolog] Which of the following selections is correct?This ____ is to delete or remove a fact or rule (clause):

args/3

assert/1

atom/1

clause/2

call/1

findall/3

functor/3

ground/1

op/3

retract/1

var/1

=, \=

==, \==

retract/1

[Prolog] Consider the following mystery/3 predicate.mystery(X,[X|R],R).mystery(X,[F|R],[F|S]) :- mystery(X,R,S).What is the result L of the following query:

?- mystery(1,[1,2,3], L).

L=1

L=2

L=3

L=[2,3]

L=[1,1,2,3]

L=[1]

L=[2,3]

[Prolog] Which of the following selections is correct?This ____(H,B) retrieves clauses in memory whose head matches H and body matches B. H must be sufficiently instantiated to determine the main predicate of the head.

args/3

assert/1

atom/1

clause/2

call/1

findall/3

functor/3

ground/1

op/3

retract/1

var/1

=/2

==/2

clause/2

[Prolog] What is the result of the following query?

?- parent(a,X) = .. L.

L = []

L = [parent, a, _X001]

L = 2

L = [parent(a,X)]

L = [parent]

L = [parent, a, _X001]

[Prolog] Explain the behavior or goal of the following program (mystery/3).

What would be the result of the query below?

mystery(A,B) :- mystery(A,[],B).

mystery([X|Y],Z,W) :- mystery(Y,[X|Z],W).

mystery([],X,X).

?- mystery([1,2,3], A).

A = [1,2,3].

A = [ ].

A = [1].

A = [2,3].

A = [3,2,1].

A = [3,2,1].

[Prolog] Which of the following selections is correct?

This ____ tests whether X is bound to a Prolog variable.

args/3

assert/1

atom/1

clause/2

call/1

findall/3

functor/3

ground/1

op/3

retract/1, retractall/1

var/1

=, \=

==, \==

var/1

[Prolog] Which of the following selections is correct?

This ____ tests whether X is bound to a symbolic atom.

args/3

assert/1

atom/1

clause/2

call/1

findall/3

functor/3

ground/1

op/3

retract/1, retractall/1

var/1

=, \=

==, \==

atom/1

[Prolog] Which of the following selections is correct?

This ____(P) forces P to be a goal; succeed if P does, else fail.

args/3

assert/1

atom/1

clause/2

call/1

findall/3

functor/3

ground/1

op/3

retract/1, retractall/1

var/1

=, \=

==, \==

call/1

[Prolog] What is a correct definition of negation in Prolog?

not(P) :- call(P), !.

not(P) :- call(P), !.

not(P).

not(P) :- not call(P), fail.

not(P).

not(P) :- call(P), !, fail.

not(P).

not(P) :- \+ call(P), !, fail.

not(P).

not(P) :- call(P), !, fail.

not(P).

[Prolog] What would it be the result of the following prolog query?

?- p(X, f(Y), a) = p(a, f(b), Y).

X=a, Y=f(a).

X=f(a), Y=a.

X=f(a), Y=f(a).

X=a, Y=a.

Not unifiable

Not unifiable

[Prolog] Which one of the following statements is NOT correct?

Consider the following op/3 predicate.

:- op(1000,xfy,',').

This defines a comma (",") operator (as in Prolog).

This operator is left-associative.

This opeator is with precedence 1000.

There is no empty sequence (unlike for lists).

Longer sequences have elements separated by commas ",".

This operator is left-associative.

[Prolog-4] As discussed in the class. Select the correct definition of prefix/2 (assuming append/3 provided).

prefix(P, L) :- append(P, _, L).

prefix(P, _, L) :- append(P, _, L).

prefix(L, P) :- append(P, _, L).

prefix(P, L) :- append(L, _, P).

prefix(P, L) :- append(_, P, L).

prefix(P, L) :- append(P, _, L).

(Entailment)

α is valid if and only if True ⊨ α

True

False

True

(Entailment).(True ⊨ False) is ___.

True

False

False

If α ⊨ γ or β ⊨ γ, then (α ∧ β) ⊨ γ

True

False

True

(Entailment)

For any α, False ⊨ α

True

False

True

"(A ⇔ B) ⇔ C" is ___.

valid

unsatisfiable

satisfiable

undecidable

semidecidable

unsolvable

satisfiable

Entailment.

(A ∨ B) ∧ (￢C ∨ ￢D ∨ E) ⊨ (A ∨ B) is ___.

True

False

True

(A ∨ B) ∧ (￢C ∨ ￢D ∨ E) ⊨ (A ∨ B) ∧ (￢D ∨ E) is ___.

valid

unsatisfiable

satisfiable

undecidable

semidecidable

unsolvable

unsatisfiable

(Entailment.)

(False ⊨ True) is ___.

True

False

True

Consider a vocabulary with only four propositions, A, B, C, and D. How many models are there for the following sentence?

(A → B) ∧ A ∧ ¬B ∧ C ∧ D

0

1

2

3

4

8

12

15

16

0

3 multiple choice options

(Entailment).

(A ∧ B) ⇒ C ⊨ (A ⇒ C) ∨ (B ⇒ C) is ___.

True

False

True

(Entailment)

α ⊨ β if and only if the sentence (α ⇒ β) is valid.

True

False

True

(Entailment)α ⊨ β if and only if the sentence (α ∧ ￢β) is unsatisfiable.

True

False

Select an equivalent statement for each statement below.

-

¬∃x p(x)

-

¬∀x p(x)

-

∃x p(x)

-

∀x q(x)

A.

∃x ¬p(x)

B.

∃y p(y)

C.

∀x ¬p(x)

D.

∀y q(y)

C

A

B

D

1. ¬∃x p(x) = ∀x ¬p(x)

2. ¬∀x p(x) = ∃x ¬p(x)

3. ∃x p(x) = ∃y p(y)

4. ∀x q(x) = ∀y q(y)

(Entailment - True/False)

Note: a ⊨ b. This reads "a entails b". M(X) is a model of X.

a ⊨ b if and only if M(a) ⊆ M(b).

True

False

True

(Entailment - True/False)

Note: a ⊨ b. This reads "a entails b". M(X) is a model of X.a ⊨ b means "a is stronger than b".

True

False

True

Consider a vocabulary with only four propositions, A, B, C, and D. How many models are there for the following sentence?

¬A ∨ ¬B ∨ ¬C ∨ ¬D

0

1

2

3

4

8

12

15

16

15then α ⊨ β or α ⊨ γ

(Entailment - True/False)

Note: a ⊨ b. This reads "a entails b". M(X) is a model of X.

a ⊨ b if, in every model in which a is true, b is also true.

True

False

True

If α ⊨ (β ∨ γ) then α ⊨ β or α ⊨ γ

True

False

False

As we discussed in the class,

a sentence is ___ if it is true for some interpretations.

valid

unsatisfiable

satisfiable

semidecidable

undecidable

decidable

satisfiable

As we discussed in the class, a sentence is ___ if it is false for all interpretations.

valid

unsatisfiable

satisfiable

semidecidable

undecidable

decidable

unsatisfiable

As we discussed in the class, a sentence is ___ if it is true for all interpretations.

valid

unsatisfiable

satisfiable

semidecidable

undecidable

decidable

valid

(Entailment)

α ≡ β if and only if the sentence (α ⇔ β) is valid.

True*

False

True

"(A ⇔ B) ∧ (￢A ∨ B)" is ____

valid

unsatisiable

satisfiable

undecidable

semidecidable

unsolvable

satisfiable

(Entailment - True/False)

Note: a ⊨ b. This reads "a entails b". M(X) is a model of X.

a ⊨ b if M(a) contains M(b).

True

False

False

In the process of proving: "Kills(Curiosity, Tuna)" with KB given below:

[A1] Animal(F(x)) ∨ Loves(G(x), x)

[A2] ¬Loves(x, F(x)) ∨ Loves(G(x), x)

[B] ¬Animal(y) ∨ ¬Kills(x,y) ∨ ¬Loves(z,x)]

[C] ¬Animal(x) ∨ Loves(Jack, x)

[D] Kills(Jack, Tuna) ∨ Kills(Curiosity, Tuna)

[E] Cat(Tuna)

[F] ¬Cat(x) ∨ Animal(x)

Which clause(s) is/are used to get the following resolvent:

Loves(G(Jack), Jack) ∨ ¬ Animal(F(Jack))

A1 & C

A2 & B

A1 & B

A2 & C

F & E; A1 & B

F & E; A2 & C

A1 & B; A2 & C

A2 & C

The following step in conversion from (1) FOL to (2) Clausal form is to ___.

[1] ∀x [Animal(F(x)) ∧ ¬ Loves(x, F(x))] ∨ Loves(G(x), x)

[2] [Animal(F(x)) ∧ ¬ Loves(x, F(x))] ∨ Loves(G(x), x)

Eliminate Implications

Move negation inward

Standardize the variable

Skolemize

Drop Universal Quantifier

Drop Existential Quantifier

Distribute ∨ over ∧

Distribute ∧ over ∨

Propositionalize

Substitute

Unify

Drop Universal Quantifier

The following step in conversion from (1) FOL to (2) Clausal form is to ___.

[1] ∀x [∃ y Animal(y) ∧ ¬ Loves(x, y)] ∨ [∃z Loves(z, x)]

[2] ∀x [Animal(F(x)) ∧ ¬ Loves(x, F(x))] ∨ Loves(G(x), x)

Eliminate Implications

Move negation inward

Standardize the variable

Skolemize

Drop Universal Quantifier

Drop Existential Quantifier

Distribute ∨ over ∧

Distribute ∧ over ∨

Propositionalize

Substitute

Unify

Skolemize

The following step in conversion from (1) FOL to (2) Clausal form is to ___.

[1] ∀x [¬ ∀y ¬ Animal(y) ∨ Loves(x, y)] ∨ [∃y Loves(y, x)]

[2] ∀x [∃ y Animal(y) ∧ ¬ Loves(x, y)] ∨ [∃y Loves(y, x)]

Eliminate Implications

Move negation inward

Standardise the variable

Skolemize

Drop Universal Quantifier

Drop Existential Quantifier

Distribute ∨ over ∧

Distribute ∧ over ∨

Propositionalize

Substitute

Unify

Move negation inward

What is the predicate form of the following statement? "All dogs are animals."

∀x y (dog(x) => animal(y))

∃x ¬(dog(A) => animal(x))

∀x (dog(x) => animal(x))

∀x ∃y (dog(x) => animal(y))

∀x (dog(x) => animal(f(x)))

∀x (dog(x) => animal(x))

(Entailment - True/False)

Note: a ⊨ b. This reads "a entails b". M(X) is a model of X.

a ⊨ b if and only if, in every model in which b is true, a is also true.

True

False

False

(Entailment).

(A ∧ B) ⊨ (A ⇔ B) is ___.

True

False

True

(Entailment).

A ⇔ B ⊨ A ∨ B is ____.

True

False

False

If α ⊨ (β ∧ γ) then α ⊨ β and α ⊨ γ

True

False

True

What is the clausal form of the following FOL clause

[1]?[1] ∀x [∀y Animal(y) ⇒ Loves(x, y)] ⇒ [∃y Loves(y, x)]

[¬Animal(y) ∨ Loves(y, x)] ∧ [¬ Loves(x, y)] ∨ Loves(y, x)]

[¬Animal(F(x)) ∨ Loves(y, x)] ∧ [¬ Loves(x, F(x))] ∨ Loves(y, x)]

[Animal(F(x)) ∨ Loves(G(z), x)] ∨ [¬ Loves(x, y)] ∨ Loves(y, x)]

[¬ (Animal(y) ∨ Loves(x, y)) ∨ Loves(G(x), x))

[¬ (Animal(y) ∨ Loves(x, y)) ∨ Loves(G(x), x))

(Animal(F(x)) ∨ Loves(G(x), x)) ∧ (¬Loves(x, F(x)) ∨ Loves(G(x), x))

(Animal(F(x)) ∨ Loves(G(x), x)) ∧ (¬Loves(G(x), x)) ∨ Loves(G(x), x))

(Animal(F(x)) ∨ Loves(G(x), x)) ∧ (¬Loves(x, F(x)) ∨ Loves(G(x), x))

(Entailment)

(C ∨ (￢A ∧ ￢B)) ≡ ((A ⇒ C) ∧ (B ⇒ C)) is ___.

True

False

True

(Entailment)

A ⇔ B ⊨ ￢A ∨ B is ____.

True

False

True

[AI-CSP] The key idea to reduce the number of legal values in CSP is ___.

consistent value

local consistency

global consistency

consistent algebra

consistent relation

local consistency

[AI-CSP] arc-consistency and path-consistency is the most known and used form of ____. Select one best answer.

backtracking

constraint propagation

local search

global search

local consistency

local consistency

[AI-CSP] (AI-209) Assume a CSP with n variables, each with domain size at most d, and with c binary constraints (arcs). Then the complexity of AC-3 algorithm for the worst case time is ___.

O(ddd)

O(cdd*d)

O(ccn*n)

O(cdnnn)

O(n!)

O(e^n) (that is, exponential in n)

O(cdd*d)

[AI-CSP] (AI-212) A CSP is ___ 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.

Alldiff

domain

label

resource

bounds propagation

bounds consistent

upper-bound

k-consistent

bounds consistent

[AI-CSP] (AI-212) For a large resource-limited problems with integer values in CSP, domains are represented by upper and lower bounds and are managed by ___.

Alldiff

domain

label

resource

bounds propagation

bounds consistent

upper-bound

k-consistent

bounds propagation

[AI-CSP] (AI-216) This ___ heuristic attempts to reduce the branching factor on future choices by selecting the variable that is involved in the largest number of constraints on other unassigned variables.

MRV

resource

LCV

bounds propagation

maximum-resource

degree

min-conflicts

MAC

backjumping

degree

[AI-CSP] ___ is a type of inference: using constraints to reduce the number of legal values in CSP.

constraint propagation

constraint backtracking

constraint consistency

constraint bounding

constraint minimization

constraint propagation

[AI-CSP] (AI-211) A CSP is ___ 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.

k-labelled

k-conjectured

k-consistent

k-constrained

k-global-consistent

k-local-consistent

k-consistent

[AI-CSP] Constraint satisfaction problems (CSP) on finite domains are typically solved using a form of search. What is not one of the most used techniques in CSP?

backtracking

constraint propagation

local search

global search

global search

[AI-CSP] (AI-225) One efficient heuristic to solve a complicated case of CSP graph is to make it a tree after removal of a subset S of the CSP's variables, and first to find each possible assignment to the variables in S to solve all the constraints on S. Here S is called a ___.

preferred constraint set

minimum-remaining-values set

cycle cutset

bound-minimization

min-conflicts set

degree-simplification set

min-conflicts set

MAC set

backjumping set

cycle cutset

[AI-CSP] (AI-210) ____ consistency tightens down the binary constraints using implicit constraints that are inferred by looking at triples of variables.

node

arc

path

unary

binary

tertiary

path

[AI-CSP] (AI-208 A CSP network is ___ if every variable is ___ with every other variable.

node-consistent

arc-consistent

path-consistent

global-consistent

heuristically consistent

admissibly consistent

optimally consistent

bounds consistent

arc-consistent

[AI-CSP] (AI-211) A CSP is strongly k-consistent if it is k-consistent and is also x-consistent for all x which is greater than ___ and less than ___.

0, k

1, k

0, k-1

1, k-1

0, x

1, x

1, x-1

0, k

[AI-CSP] (AI-214) Applying a standard depth-first search for a CSP problem, a state would be a partial assignment where each action (generating a child node) is to assign a value (from a domain of size d) to a variable. In particular, a CSP problem is ___ if the order of application of any given set of actions has no effect on the outcome.

reflective

commutative

transitive

complete

optimal

consistent

admissible

constrained

commutative

[AI-CSP] (AI-208) A variable in a CSP is ___ if every value in its domain satisfies the variable's binary constraints.

node-consistent

arc-consistent

path-consistent

global-consistent

heuristically consistent

admissibly consistent

optimally consistent

bounds consistent

arc-consistent

[AI-CSP] (AI-212) One important higher-order constraint is the ___ constraint, sometimes called the Atmost constraint. For example, in a scheduling problem, let P1, P2, P3, P4 denote the numbers of personnel assigned to each of four tasks. The constraint that no more than 10 personnel are assigned in total is written as Atmost(10, P1, P2, P3, P4).

Alldiff

domain

label

resource

bound

propagation

k-consistent

resource

[AI-CSP] arc-consistency and path-consistency is the most known and used form of ____. Select one best answer.

backtracking

constraint propagation

local search

global search

local consistency

local consistency

[AI-CSP] (AI-214) ___ is used for 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.

resource constraint

domain search

backtracking search

resource backtracking

bounds propagation

bounds consistent

k-consistent

domain bounding

labeled variable

backtracking search

[AI-CSP] (AI-208) A variable in a CSP is ___ if all the values in the variable's domain satisfy the variable's unary constraints.

node-consistent

arc-consistent

path-consistent

global-consistent

heuristically consistent

admissibly consistent

optimally consistent

bounds consistent

node-consistent

[AI-CSP] A Constraint satisfaction problem (CSP) consists of three components: a set of variables X and a set of domain D for X, and ___.

a set of constraints C restricting the values for the variables X simultaneously.

a set of heuristics H for constraints

a set of range R for the satisfying constraints

a set of assignments A of the solution

a set of constraints C restricting the values for the variables X simultaneously.

[AI-CSP] A constraint involving an arbitrary number of variable is called ___.

unary constraint

binary constraint

global constraint

local constraint

primitive constraint

single constraint

global constraint

[AI-CSP] (AI-208) Arc consistency tightens down the domains (unary constraints) using the ___.

unary constraints

binary constraints

tertiary constraints

general constraints

global constraints

k-constraints

binary constraints

[AI-CSP] A type of constraint which restricts the value of a single variable is ___.

unary constraint

binary constraint

global constraint

local constraint

primitive constraint

single constraint

unary constraint

[AI-CSP] (AI-219) The ___ method backtracks to the most recent assignment in the conflict set.

MRV

resource

LCV

bounds progagation

maximum-resource

degree

min-conflicts

MAC

backjumping

backjumping

[AI-CSP] In constraint hypergraph a hyper-node represents n-ary ___.

constraints

variables

paths

nodes

relations

arcs

constraints

[AI-CSP] (AI-214) Applying a standard depth-first search for a CSP problem, a state would be a partial assignment where each action (generating a child node) is to assign a value (from a domain of size d) to a variable. For a CSP with n variables of domain size d, there are only ___ possible complete assignments.

O(n! * (d^n)). That is, n factorial times (d to the power of n).

O(n * (d^n)). That is, n times (d to the power of n).

O(n^d). That is, n to the power of d).

O(n! * (n^d)). That is, n factorial times (n to the power of d).

O(d^n). That is, d to the power of n

O(nnn)

O(nnd)

O(d^n). That is, d to the power of n

[AI-CSP] (AI-217) This ___ heuristic can be effective in some cases as it prefers the value that rules out the fewest choices for the neighboring variables in constraint graph.

MRV

resource

LCV

bounds progagation

maximum-resource

degree

min-conflicts

MAC

backjumping

LCV

[AI Ch12 KR] Frame Representation Languages are developed in the '70s and '80s. A frame is a lot like the notion of an object in OOP, but has more meta-data. Which one of the following choices is NOT correct?

A Semantic Network has one or more frames.

A frame has a set of slots.

A slot represents a relation to another frame (or value).

A slot has one or more facets.

A facet represents some aspect of the relation.

A Semantic Network has one or more frames.

[AI Ch12 KR] ______ is a reasoning process that tries to form plausible explanations for abnormal observations

Abduction

Description Logic

Frame

Ontology

Semantic Network

Semantic Web

Situation Calculus

Abduction

[AI Ch12 KR] Consider Frame. Which one of the following choices is NOT correct?

A slot in a frame can hold current fillers (e.g., values)

A slot in a frame can hold default fillers

A slot in a frame can hold minimum and maximum numbers of fillers

A slot in a frame can hold type restriction on fillers

A slot in a frame can hold attached procedures (if-needed, if-added, if-removed)

A slot in a frame can hold salience measure (salient - most noticeable or important)

A slot in a frame can hold attached constraints or axioms

A slot in a frame can hold an instance of frame in some systems.

None of the above.

All of the above

None of the above.

[AI Ch12 KR] Real knowledge representation and reasoning systems come in several major varieties. These differ in their intended use, expressivity, features, etc. Some major families are ________. Which one of the following choices is NOT correct?

Logic programming languages

Natural Language Tools (e.g., nltk)

Rule-based or production systems

Databases (deductive, relational, object-oriented, etc.)

Description logics

Natural Language Tools (e.g., nltk)

[AI Ch12 KR] Non-binary relationships can be represented or implemented by "turning the relationship into an object" This is an example of _______

Abstraction

Inheritance

Instantiation

Reification

Subsumption

Reification

AI Ch12 KR] A node can have any number of superclasses that contain it, enabling a node to inherit properties from multiple "parent" nodes and their ancestors in the network.

The following diagram shows one classical issue with multiple-inheritance which is known as ________

Classification

ISA Consistency

Nixon Diamond

Quaker-Republican Conflict

Subclass-Instance Problem

Nixon Diamond

[AI Ch12 KR] In _____, the graphical depiction is a significant reason for its popularity. Arcs define binary relationships that hold between objects denoted by the nodes.

Abduction

Description Logic

Frame

Ontology

Semantic Network

Semantic Web

Situation Calculus

Semantic Network

[AI Ch12 KR] In Semantic Network, the ____ or AKO (a-kind-of) relation is often used to link instances to classes, classes to superclasses

Abstract

Conceptual

Concrete

HasPart

ISA (is-a)

ISA (is-a)

[AI Ch12 KR] Description logics provide a family of frame-like KR systems with a formal semantics. Which one of the following choices is NOT correct?

Some systems are KL-ONE, KRL, LOOM, Classic, etc.

These logics can be used to determine whether categories belong within other categories.

It cannot handle subsumption tasks.

It can handle automatic classification finding the right place in a hierarchy of objects for a new description.

Current systems run all inference done in polynomial time (in the number of objects).

It cannot handle subsumption tasks.

[AI Ch12 KR] Upper Ontologies deal with highest-level categories of things such as _____. Which one of the choices is NOT correct?

Measurements

Objects and their properties (including fluent, or changing, properties)

Events and temporal relationships

Domain-specific Concepts (e.g., AND or OR Gates in Electrical Circuit)

Mental events, processes; "beliefs, desires, and intentions

Domain-specific Concepts (e.g., AND or OR Gates in Electrical Circuit)