Test 3

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[AI-Ch10Plan] According to the authors. The result of executing action A in state S is defined as a state S' which is represented by the set of fluents formed by starting with S.Which one of the following statements is NOT correct?

As with all states, the open-world assumption is used. That is, any atoms that are not mentioned are false. The fluents in the action schema should explicitly refer to time of each action.

[Prob] A sample space is ____________

a set of all possible outcomes;

[Prob] Bayes' Theorem is used in constructing ________

belief networks

[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 ___.

bounds propagation

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

constraint propagation

[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?

global search

[AI-CSP] (AI-216) The intuitive idea of choosing the variable with the fewest legal values is called the __ heuristics

minimum-remaining-values

Lecture13] According to the class discussion.Consider the following factoring equation and tables provided. What is the answer for entry (1) in Table f(AxMxJ) (that is, the right-most table)?

0.95×0.9×0.7 + 0.05×0.05×0.01

[Prob] Uncertainty is a property of all environments that are ____________

partially observable or stochastic

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

path

[Prob] Planning in a partially observable environment faces challenges due to ___________.

uncertainty

[Lecture12] Consider the following table as we discussed in the class.What is P(cavity | toothache)?

( (0.108 + 0.012) / (0.108 + 0.012 + 0.016 + 0.064) )

(1 - 0.6 x 0.2 x 0.1)

[Lecture13] According to the class discussion.New burglar alarm is installed at home. It is fairly reliable at detecting a burglary, but it also responds on occasion to minor earthquakes. There are 2 neighbors: John and Mary, who have promised to call you at work when they hear the alarm. What is P(¬B, ¬E, ¬A, ¬J, ¬M)?

(1-0.001)×(1-0.002) ×(1-0.001) ×(1-0.05) ×(1-0.01)

[Lecture13] According to the class discussion.Consider the following factoring equation and tables provided. What is the answer for entry (1) in Table f(ExAxJxM) (that is, the right-most table)?

(E1) x (F1) + (E2) x (F2)

[AI-Ch10Plan] According to the authors. Consider Planning Graph. Which one of the following choices is NOT correct?

-Literals decrease monotonically. -No-goods could increase monotonically. -Planning graphs can work withapredicated planning with some variables. -Planning graph shows each Ai level which contains all the fluents that are applicable in Si. -The consistency of an action at one level assures the preconditions and effects at one level to the next.

[Prob] Given a sample space S which is not empty, the probability of an empty set is ________.

[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

0.0

[Lecture13] According to the class discussion. Consider the following probability tables for fever (concerning only three causes of cold, flu, malaria). These causes are independent of each other and are all causes of Fever. What is the P(Fever |¬ Cold, ¬ Flu, Malaria) for the Entry (2)? Probabilities of individual inhibitions P( fever | cold, flu, malaria) = 0.6 P(-fever | -cold, flu, malaria) = 0.2 P(-fever| -cold, flu,malaria) = 0.1 From this information, the entire CPT can be built

0.9

0.90 x 0.70 x 0.001 x 0.999 x 0.998

[Prob] Given a sample space S, the probability of S is ________

[Lecture12] According to the class discussion.Consider the wumpus world as shown below. The Known facts are: { not Pit[1,1], not Pit[1,2], not Pit[2,1] }, and the presence of breeze (denoted as b) in cells: b = { b[1,2], b[2,1] }. The Query is for the probability of Pit in each frontier cell of { [1,3], [2,2], [3,1] }, and especially to compute what is the probability of Pit in the cell [1,3], that is, P(Pit[1,3]). What is the normalization constant for this case to compute P(Pit13|known,b)? Select the best answer as discussed in the class.

1/((0.2)3 + 3x(0.2)2x0.8 + 0.2x(0.8)2)

[Lecture12] Consider the following table as we discussed in the class. toothache not toothache ------------|------------------|---------------------| catch !catch | catch !catch ------------|-------|-----------|----------|-----------| cavity | 0.18 | 0.012 | 0.072 | 0.0008 ------------|-------|-----------|----------|-----------| !cavity | 0.016 | 0.064 | 0.144 | 0.576 ------------|-------|-----------|----------|-----------| Let X be P(cavity | toothache), where αbe the normalization constant and X = αP(cavity, toothache). Then α = __________.

1/P(cavity, toothache)

[Lecture12] According to the class discussion.Consider the wumpus world as shown below. The Known facts are: { not Pit[1,1], not Pit[1,2], not Pit[2,1] }, and the presence of breeze (denoted as b) in cells: b = { b[1,2], b[2,1] }. The Query is for the probability of Pit in each frontier cell of { [1,3], [2,2], [3,1] }, and especially to compute what is the probability of Pit in the cell [1,3], that is, P(Pit[1,3]). Assuming Pit[1,3] (that is, a pit in [1,3]) with the known and breeze information, how many models (cases) should it be considered? Select the best answer as discussed in the class.

[Lecture12] Consider the following table as we discussed in the class.Consider the wumpus world as shown below.The Known facts are: { not Pit[1,1], not Pit[1,2], not Pit[2,1] }, and the presence of breeze (denoted as b) in cells: b = { b[1,2], b[2,1] }. The Query is for the probability of Pit in each frontier cell of { [1,3], [2,2], [3,1] }, and especially what is the probability of Pit in the cell [1,3], that is, P(Pit[1,3]). To compute the probability of Pit[1,3] with the known and breeze information observed, how many models (cases) should it be considered for the possibility of Pit in the frontier cells { [1,3], [2,2], [3,1] }? Select the best answer as discussed in the class.

[AI-Ch10Plan] According to the authors. Consider PDDL. Which one of the following statements is NOT correct?

A fluent can be treated as a disjunction of potential actions, which can be manipulated with set operations. (A in ACTIONS(S)) if and only if PRECONDITION(A) entails S. The set of action schema provides the state of the system in planning.

[Lecture13] According to the class discussion. Which one of the following is NOT correct?The Bayesian Network consists of or is: ________.

A graph which may have a cycle but with leaf node only.

[AI-CSP] (AI-212) For example, in an airline-scheduling problem in CSP, let's suppose there are two flights, F1 and F2, for which the planes have capacities 165 and 385, respectively. The initial domains for numbers of passengers on each flight are then D1 = [0, 165] for F1, and D2 = [0,385] for F2. Now suppose we have the additional constraint that two flights together must carry 420 people (F1 + F2 = 420). We reduce the domains to ____

D1=[35,165] and D2=[255,385]

[AI-Ch10Plan] According to the authors. A planning graph with various heuristic methods can be used to give better heuristic "estimates" and can be used to search for a solution over the space formed by this method. One of the first and best-known planning graph algorithms is ____.

GraphPlan

[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.

LCV

[AI-CSP] (AI-218) Although forward checking detects many inconsistencies, it does not detect all of them. The problem is that it makes the current variable arc-consistent, but does not look ahead and make all the other variables arc-consistent. The ___ algorithm detects this inconsistency.

MAC

[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(c*d*d*d)

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

[AI-CSP] (AI-211) Assume a CSP with n variables, each with domain size at most d, and with c binary constraints (arcs). Then the worst-case time-complexity for any algorithm to check n-consistency is ___.

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

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

[Lecture12] According to the class discussion.Given three random variables X, Y and Z, with the product rule,P(a, b, c) = ________

P( a | b c ) P( b | c ) P( c )

[Lecture12] According to the class discussion.Consider the axioms of Probability (where a and b are random variables). Which one of the following is NOT correct?

P(a ∧ b) = P(a) * P(b)

Lecture12] According to the class discussion. Consider two random variables s and m. Which one of the following is NOT correct?

P(s|m) = P(s,m) / [P(m|s)P(m) + P(m|¬s)P(m)]

[AI-Ch10Plan] According to the authors. Consider Planning Graph. Planning is ______.

PSPACE-complete

[AI-Ch10Plan] According to the authors. Consider PDDL. Actions are described by a set of action schemas that implicitly define the ACTIONS(s) and _____ functions needed to do a problem-solving search.

RESULT(s, a)

[AI-Ch10Plan] According to the authors. Consider Planning. First-order logic lets us get around this limitation by replacing the notion of linear time with a notion of branching situations, using a representation called _____.

Situation Calculus

[Lecture13] According to the class discussion. Which one of the following is NOT correct?

The Bayesian network is an encoding of a collection of independence joint statements. Each entry in the joint probability distribution is the conditional probability of each variable, represented by each node of Bayesian Network

[AI-Ch10Plan] According to the authors. If we add function symbols to the language (e.g., blocks world), then _____.Which one of the following statements is NOT correct?

The Bounded problem remains undecidable even in the presence of function symbols.

[Lecture12] Consider the following table as we discussed in the class.Let X be P(cavity | toothache), and αbe the normalization constant.Which one of the following is NOT correct?

X = α[ <0.108, 0.012> + <0.016, 0.064> ]

[Lecture12] According to the class discussion.Consider a Medical diagnosis for Meningitis which is a disease caused by the inflammation of the protective membranes covering the brain and spinal cord known as the meninges. A doctor knows that meningitis causes a stiff neck 50% of the time: P(s|m)=0.5. The doctor also knows some unconditional facts: the prior probability that the patient has meningitis is 1/50000. That is, P(m)=1/50000. The prior probability that any patient has a stiff neck is 1/20. That is, P(s)=1/20. What is P(m | s)?

[ 0.5 x (1/50000) ] / (1/20)

[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.

[AI-CSP] (AI-212) The Alldiff constraint in CSP says that all the variables (Vars) involved must have distinct values (as discussed in the book and class). In SWI-Prolog CLP, it is done via ___ for variables Vars.

all_different(Vars)

[AI-Ch10Plan] According to the authors. One issue in planning is deciding which actions are candidates to regress over. In the forward direction we chose actions that were (1) _____ —those actions that could be the next step in the plan. In backward search we want actions that are (2) _____ —those actions that could be the step in a plan leading up to the current goal state.

applicable, relevant

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

arc-consistent

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

arc-consistent

[AI-CSP] (AI-209) The most popular algorithm for ___ is called AC-3 algorithm.

arc-consistent

[Prob] Beliefs ____________.

are changeable

Lecture12] According to the class discussion, a(n) ____ event is a complete specification of the state of the world about which the agent is uncertain. If the world is described by a set of random variables, a(n) _____ event is a particular assignment of values to the random variables.

atomic

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

backjumping

[AI-CSP] In CSP, ____ is a recursive algorithm. It maintains a partial assignment of the variables. In this algorithm, consistency is defined as the satisfaction of all constraints whose variables are all assigned.

backtracking

[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.

backtracking search

[Prob] Partial-information games are solved using ___________.

belief states

[Prob] Truth maintenance may alter ________.

beliefs

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

binary constraints

AI-Ch10Plan] According to the authors. For Algorithms for Planning as State-Space Search. The description of a planning problem defines a search problem: we can search from the initial state through the space of states, looking for a goal. From the earliest days of planning research (around 1961) until around 1998 it was assumed that forward state-space search _____. Which one of the following statements is NOT correct?

can be very efficient as it tries to narrow the search space by focusing on the goal forward.

[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.

commutative

conditional

[AI-CSP] In CSP, ____ is a technique used to check constraint satisfaction(s). More precisely, they are methods that enforce a form of local consistency.

constraint propagation

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

constraints

[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.

degree

[Prob] Bayesian reasoning is ___________

diagnostic

[Prob] Bayes' Theorem enables computation of probabilities of causes, given probabilities of ________ .

effects

[Prob] An outcome that is from a set of uncertain possibilities characterizes a(n) ____________

event

[AI-Ch10Plan] According to the authors. Consider Situation Calculus. A function or relation that can vary from one situation to the next is a(n) _____.

fluent

[AI-Ch10Plan] According to the authors. Consider PDDL. Any system for action description needs to solve the ____ problem—to say what changes and what stays the same as the result of the action.

frame

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

global constraint

[AI-Ch10Plan] According to the authors. Consider Planning Graph. For some heuristic guidance for choosing among actions during the backward search, one approach that works well in practice is a greedy algorithm based on the level cost of the literals. For any set of goals, we proceed in the fallowing order: Pick first the literal with the (1) ____ level cost. To achieve that literal, prefer actions with easier preconditions. That is, choose an action such that the sum (or maximum) of the level costs of its preconditions is (2) ____.

highest, smallest

[AI-Ch10Plan] According to the authors. The following statements describe the _____ heuristic.Assume for a moment that all goals and preconditions contain only positive. We want to create a relaxed version of the original problem that will be easier to solve, and where the length of the solution will serve as a good heuristic. We can do that by removing all negative literals from effects from all actions. That makes it possible to make monotonic progress towards the goal—no action will ever undo progress made by another action.

ignore delete-lists

[AI-Ch10Plan] According to the authors. The ______ heuristic drops all preconditions from action becomes applicable in every state, and any single goal fluent can be achieved in one step (if there is an applicable action—if not, the problem is impossible).

ignore preconditions

[Prob] Any probability value is ____________

in the range of 0 to 1

[AI-Ch10Plan] According to the authors. The _____ assumption is that the cost of solving a conjunction of subgoals is approximated by the sum of the costs of solving each subgoal.

independence

[AI-Ch10Plan] According to the authors. The reason for the state, "not Poor," is NOT allowed in PDDL because _____.

it is negated

[AI-Ch10Plan] According to the authors. The reason for the state, At(x,y), is NOT allowed in PDDL because _____.

it is not grounded

[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-consistent

[AI-Ch10Plan] According to the authors. With a planning graph, one can estimate the cost of achieving any goal literal G from a state S as the level at which the goal literal G first appears in planning graph constructed from initial state S. We call this the ______ of G.

level cost

[AI-Ch10Plan] According to the authors. Consider Planning Graph. To estimate the cost of a conjunction of goals, there are three simple approaches. The ____ heuristic, following the subgoal independence assumption, returns the sum of the level costs of the goals.

level-sum

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

local consistency

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

local consistency

[AI-CSP] ____ methods are incomplete satisfiability algorithms. They work by iteratively improving a complete assignment over the variables. At each step, a small number of variables are changed value, with the overall aim of increasing the number of constraints satisfied by this assignment.

local search

Lecture12] According to the class discussion. The following formula is an example of ____ rule. Given any two random variables Y and Z,

marginalization rule

[AI-CSP] (AI-219) In choosing a new value for a variable, the most obvious heuristic is to select the value that results in the minimum number of conflicts with other variables. This is ___ heuristics in local search for CSP.

min-conflicts

[Prob] Logic is _______ in that adding new facts always expands a knowledge base.

monotonic

[AI-Ch10Plan] According to the authors. Consider Planning Graph. As a tool for generating accurate heuristics, we can view Planning Graph as a relaxed problem that is efficiently solvable. To understand the nature of the relaxed problem, we need to understand exactly what it means for a literal G to appear at level Si in the planning graph. Ideally, we would like it to have a guarantee that there exists a plan with i action-levels that achieves g, and also that if the literal G does not appear, there is no such plan. Unfortunately, making that guarantee is as difficult as solving the original planning problem. So Planning Graph makes the second half of the guarantee (if the literal G does not appear, there is no plan), but if the literal G does appear, then all Planning Graph promises is that there is a plan that possibly achieves the literal G and has no "obvious" flaws. An obvious flaw is defined as a flaw that be detected by considering two actions or two literals at a time—in other words, by looking at the _____ relations.

mutex

[Prob] If one can add new fact to knowledge base or delete it from a knowledge base, this is an example of ________reasoning.

nonmonotonic

[AI-Ch10Plan] According to the authors. The subgoal independence assumption can be ______ when subplans contain redundant actions—for instance, two actions that could be replaced by a single action in the merged plan.

pessimistic

[Prob] Stochastic methods are often used in ___________

planning under uncertainty

[Lecture12] According to the class discussion, _____ probability associated with proposition X is the degree of belief accorded to it in the absence of any other information

prior

[Prob] Probabilities of different event outcomes are a(n) ___________

probability distribution

[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).

resource

[Prob] Probabilities are employed in ________ methods.

stochastic

[Prob] Probability axioms are considered useful for rational-agent because ________.

they help the agent predict outcomes of possibility

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

unary constraint

[AI-Ch10Plan] According to the authors. Consider Situation Calculus. The agent can deduce that an action A is unique and different from other actions. This is a(n) _____ axiom.

unique action

[Lecture12] According to the class discussion.There are two discrete random variables X and Y where X describes weather conditions, with the domain: X={sunny, rain, cloudy, snow}, and Y describes clothes to wear, with the domain: Y={t-shirt, long-sleeves, coat}. The distributions of X and Y are: X = <0.331, 0.26, 0.159, 0.25> Y = <0.5, 0.3, 0.2> And the joint probabilities are: P(t-shirt, sunny)=0.32 P(t-shirt, rain)=0.08 P(t-shirt, cloudy)=0.09 P(t-shirt, snow)=0.01 P(long-sleeves, sunny)=0.01 P(long-sleeves, rain)=0.15 P(long-sleeves, cloudy)=0.05 P(long-sleeves, snow)=0.09 P(coat, sunny)=0.001 P(coat, rain)=0.03 P(coat, cloudy)=0.019 P(coat, snow)=0.15 To guess the weather from the clothes people wear: P(X|Y), What is Normalization Constant α to compute P(sunny | t-shirt).

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