Annauniversity CSE 2marks AI

2m & 16m question bank AI 6sem CSE Anna university

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ARTIFICIAL INTELLIGENCE

Unit 1

PART - A

1. Define Artificial Intelligence formulated by Haugeland.

The exciting new effort to make computers think machines with minds in the full and literal sense.

2. Define Artificial Intelligence in terms of human performance.

The art of creating machines that perform functions that require intelligence when performed by

people.

3. Define Artificial Intelligence in terms of rational acting.

A field of study that seeks to explain and emulate intelligent behaviors in terms of computational

processes-Schalkoff. The branch of computer science that is concerned with the automation of

intelligent behavior-Luger&Stubblefield.

3. Define Artificial in terms of rational thinking.

The study of mental faculties through the use of computational models-Charniak&McDermott. The

study of the computations that make it possible to perceive, reason and act-Winston.

4. What does Turing test mean?

The Turing test proposed by Alan Turing was designed to provide a satisfactory operati onal definition of

intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all

cognitive tasks, sufficient to fool an interrogator.

Natural Language Processing:

To enable it to communicate successfully in English.

Knowledge Representation:

To store information provided before or during interrogation.

Automated Reasoning:

To use the stored information to answer questions and to draw new conclusion.

Machine Language:

To adapt new circumstances and to detect and explorate pattern.



5. Define an agent.

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon

the environment through effectors.

7. Define rational agent.

A rational agent is one that does the right thing. Here right thing is one that will cause agent to be more

successful. That leaves us with the problem of deciding how and when to evaluate the agent’s success.

8. Define an Omniscient agent.

An omniscient agent knows the actual outcome of its action and can act accordingly; but omniscience is

impossible in reality.

9. What are the factors that a rational agent should depend on at any given time?

1. The performance measure that defines degree of success.

2. Ever thing that the agent has perceived so far. We will call this

complete perceptual history the percept sequence.

3. When the agent knows about the environment.

4. The action that the agent can perform.

10. Define an Ideal rational agent.

For each possible percept sequence, an ideal rational agent should do whatever action is expected to

maximize its performance measure on the basis of the evidence provided by the percept sequence &

whatever built-in knowledge that the agent has.

11. Define an agent program.

Agent program is a function that implements the agents mapping from percept to actions.

12. Define Architecture.

The action program will run on some sort of computing device which is called as Architecture.

13. List the various type of agent program.

• Simple reflex agent program.

• Agent that keep track of the world.



• Goal based agent program.

• Utility based agent program

14. State the various properties of environment.

Accessible Vs Inaccessible:

If an agent’s sensing apparatus give it access to the complete state of the environment then we can say

the environment is accessible to he agent.

Deterministic Vs Non deterministic:

If the next state of the environment is completely determined by the current state and the actions

selected by the agent, then the environment is deterministic.

Episodic Vs Non episodic:

In this, agent’s experience is divided into episodes. Each episodes consists of agents perceiving and then

acting. The quality of the action depends on the episode itself because subsequent episode do not

depend on what action occur in previous experience.

Discrete Vs Continuous:

If there is a limited no. of distinct clearly defined percepts & action we say that the environment is

discrete.

15. What are the phases involved in designing a problem solving agent?

The three phases are: Problem formulation, Search solution, Execution.

16. What are the different types of problem?

Single state problem, Multiple state problem, Contingency problem, Exploration problem.

17. Define problem.

A problem is really a collection of information that the agent will use to decide what to do.

18. List the basic elements that are to be include in problem definition.

Initial state, operator, successor function, state space, path, goal test, path cost.

19. Mention the criteria for the evaluation of search strategy.

There are 4 criteria: Completeness, time complexity, space complexity, optimality.



20. Differentiate blind search & heuristic search.

Blind search has no information about the no. of steps or the path cost from the current state to the

goal, they can distinguish a goal state from nongoal state. Heuristic search-knowledge given. Problem

specification solution is best.

21. List the various search strategies.

a. BFS

b. Uniform cost search

c. DFS

d. Depth limited search

e. Iterative deepening search

f. Bidirectional search

21. List the various informed search strategy.

• Best first search –greedy search

• A* search

• Memory bounded search-Iterative deepening A*search

• simplified memory bounded A*search

• Iterative improvement search –hill climbing

• simulated annealing

23. Differentiate BFS & DFS.

BFS means breath wise search DFS means depth wise search

BFS DFS

Space complexity is more Space complexity is less

Do not give optimal solution Gives optimal solution

Queuing fn is same as that of queue operator Queuing fn is somewhat different

from queue operator



24. Whether uniform cost search is optimal?

Uniform cost search is optimal & it chooses the best solution depending on the path cost.

25. Write the time & space complexity associated with depth limited search.

Time complexity =O (bd) , b-branching factor, d-depth of tree

Space complexity=O(bl)

26. Define iterative deepening search.

Iterative deepening is a strategy that sidesteps the issue of choosing the best depth limit by trying all

possible depth limits: first depth 0, then depth 1,then depth 2& so on.

27. Define CSP

A constraint satisfaction problem is a special kind of problem satisfies some additional structural

properties beyond the basic requirements for problem in general. In a CSP; the states are defined by the

values of a set of variables and the goal test specifies a set of constraint that the value must obey.

28. Give the drawback of DFS.

The drawback of DFS is that it can get stuck going down the wrong path. Many problems have very

deep or even infinite search tree. So dfs will never be able to recover from an unlucky choice at one of

the nodes near the top of the tree.SoDFS should be avoided for search trees with large or infinite

maximum depths.

29. What is called as bidirectional search?

The idea behind bidirectional search is to simultaneously search both forward from the initial state &

backward from the goal & stop when the two searches meet in the middle.

30. Explain depth limited search.

Depth limited avoids the pitfalls of DFS by imposing a cut off of the maximum depth of a path. This

cutoff can be implemented by special depth limited search algorithm or by using the general search

algorithm with operators that keep track of the depth.

31. Differentiate greedy search & A* search.

Greedy Search

If we minimize the estimated cost to reach the goal h(n), we get greedy search The search time is usually

decreased compared to uniformed alg, but the alg is neither optimal nor complete



32 . Give the procedure of IDA* search.

Minimize f(n)=g(n)+h(n) combines the advantage of uniform cost search + greedy search A* is complete,

optimal It’s space complexity is still prohibitive.

Iterative improvement algorithms keep only a single state in memory, but can get stuck on local

maxima. In this alg each iteration is a dfs just as in regular iterative deepening. The depth first search is

modified to use an f-cost limit rather than a depth limit. Thus each iteration expands

A*search

All nodes inside the contour for the current f-cost.

33. What is the advantage of memory bounded search techniques?

We can reduce space requirements of A* with memory bounded alg such as IDA* & SMA*.

34. List some properties of SMA* search.

* It will utilize whatever memory is made available to it.

* It avoids repeated states as for as its memory allow.

* It is complete if the available memory is sufficient to store the shallowest path.

* It is optimal if enough memory is available to store the shallowest optimal solution path. Otherwise it

returns the best solution that can be reached with the available memory.

*When enough memory is available for entire search tree, the search is optimally efficient.

*Hill climbing.

*Simulated annealing.

35. List some drawbacks of hill climbing process.

Local maxima: A local maxima as opposed to a goal maximum is a peak that is lower that the highest

peak in the state space. Once a local maxima is reached the algorithm will halt even though the solution

may be far from satisfactory.

Plateaux: A plateaux is an area of the state space where the evaluation fn is essentially flat. The search

will conduct a random walk.



36. List the various AI Application Areas

natural language processing - understanding,

generating, translating;

planning;

vision - scene recognition, object recognition, face

recognition;

robotics;

theorem proving;

speech recognition;

game playing;

problem solving;

expert systems etc

Unit II

PART - A

1. What are Logical agents

Logical agents apply inference to a knowledge base to derive new information and make decisions.

2. What are the limitations of simple reflex agents

Reflex agents are also unable to avoid infinite loops

In Wumpus world -A pure reflex agent cannot know for sure when to Climb, because neither having the

gold nor being in the start square is part of the percept; they are things the agent knows by forming a

representation of the world.

3. What are Causal Rules?

Causal rules reflect the assumed direction of causality in the world:

(Al1,l2,s) At(Wumpus,l1,s) ^ Adjacent(l1,l2) => Smelly(l2)

(A l1,l2,s) At(Pit,l1,s) ^ Adjacent(l1,l2) => Breezy(l2)



Systems that reason with causal rules are called model-based reasoning systems

4. What is Situation Calculus?

A situation is a snapshot of the world at an interval of time during which nothing changes

• Every true or false statement is made with respect to a particular situation.

– Add situation variables to every predicate.

– at(hunter,1,1) becomes at(hunter,1,1,s0): at(hunter,1,1) is true in situation (i.e. , state) s0.

• Example: The action agent-walks-to-location-y could be represented by

( x)( y)( s) (at(Agent,x,s) ^ ~onbox(s)) -> at(Agent,y,result(walk(y ),s))

5. What are Diagnostic rules?

Diagnostic rules infer the presence of hidden properties directly from the percept-derived information.

We have already seen two diagnostic rules:

(A l,s) At(Agent,l,s) ^ Breeze(s) => Breezy(l)

(A l,s) At(Agent,l,s) ^ Stench(s) => Smelly(l)

6. Give an example rule for Goal Based Agent.

Once the gold is found, it is necessary to change strategies. So now we need a new set of action values.

We could encode this as a rule:

a. ( s) Holding(Gold,s) => GoalLocation([1,1]),s)

7. What are the components of Propositional Logic?

• Logical constants: true, false

• Propositional symbols: P, Q, S, ... (atomic sentences)

• Wrapping parentheses: ( … )

• Sentences are combined by connectives:

...and [conjunction]

...or [disjunction]



...implies [implication / conditional]

..is equivalent [biconditional]

...not [negation]

• Literal: atomic sentence or negated atomic sentence

8. What is Horn Clause?

• A Horn sentence or Horn clause has the form:

P1 P2 P3 ... Pn Q

or alternatively

P1 P2 P3 ... Pn Q

where Ps and Q are non-negated atoms

• To get a proof for Horn sentences, apply Modus Ponens repeatedly until nothing can be done

9. Define First Order Logic.

• First-order logic (FOL) models the world in terms of

– Objects, which are things with individual identities

– Properties of objects that distinguish them from other objects

– Relations that hold among sets of objects

– Functions, which are a subset of relations where there is only one “value” for any given “input”

• Examples:

– Objects: Students, lectures, companies, cars ...

– Relations: Brother-of, bigger-than, outside, part-of, has-color, occurs-after, owns, visits, precedes, ...

– Properties: blue, oval, even, large, ...

– Functions: father-of, best-friend, second-half, one-more-than ...

10. What are the types of Quantifiers?

Universal Quantifiers & Existential Quantifiers



11. What is Universal Quantification?

Universal quantification

a. ( x)P(x) means that P holds for all values of x in the domain associated with that variable

b. E.g., ( x) dolphin(x) mammal(x)

12. What is Existential quantification

a. ( x)P(x) means that P holds for some value of x in the domain associated with that variable

b. E.g., ( x) mammal(x) lays-eggs(x)

c. Permits one to make a statement about some object without naming it

Connections between All and Exists

13. Define a knowledge Base

Knowledge base is the central component of knowledge base agent and it

is described as a set of representations of facts about the world.

14. Define a Sentence?

Each individual representation of facts is called a sentence. The

sentences are expressed in a language called as knowledge representation

language.

15. Define an inference procedure



An inference procedure reports whether or not a sentence is entiled by

knowledge base provided a knowledge base and a sentence . An inference

procedure ‘i’ can be described by the sentences that it can derive.

If i can derive from knowledge base, we can write.

KB Alpha is derived from KB or i derives alpha from KB

16. What are the three levels in describing knowledge based agent?

Logical level

Implementation level

Knowledge level or epistemological level

17. Define Syntax?

Syntax is the arrangement of words. Syntax of a knowledge describes the

possible configurations that can constitute sentences. Syntax of the language

describes how to make sentences.

18. Define Semantics

The semantics of the language defines the truth of each sentence with

respect to each possible world. With this semantics, when a particular configuration

exists with in an agent, the agent believes the corresponding sentence.



19. Define Logic

Logic is one which consist of

i. A formal system for describing states of affairs, consisting

of a) Syntax b)Semantics.

ii. Proof Theory – a set of rules for deducing the entailment of a set sentences.

20. What is entailment

The relation between sentence is called entailment. The formal definition

of entailment is this: if and only if in every model in which is true, is also true or if is true then must also

be true. Informally the truth of is contained in the truth of .

21. What is truth Preserving

An inference algorithm that derives only entailed sentences is called

sound or truth preserving .

22. Define a Proof

A sequence of application of inference rules is called a proof. Finding

proof is exactly finding solution to search problems. If the successor function is



defined to generate all possible applications of inference rules then the search

algorithms can be applied to find proofs.

23. Define a Complete inference procedure

An inference procedure is complete if it can derive all true conditions from

a set of premises.

24. Define Interpretation

Interpretation specifies exactly which objects, relations and functions are

reffered to by the constant predicate, and function symbols.

25. Define Validity of a sentence

A sentence is valid or necessarily true if and only if it is true under all

possible interpretation in all posssible world.

26. Define Satistiability of a sentence

A sentence is satisfiable if and only if there is some interpretation in some

world for which it is true.

27. Define true sentence



A sentence is true under a particular interpretation if the state of affairs it

represents is the case.

28. What are the basic Components of propositonal logic?

i. Logical Constants (True, False)

29. Define Modus Ponen’s rule in Propositional logic?

The standard patterns of inference that can be applied to derive chains of

conclusions that lead to the desired goal is said to be Modus Ponen’s rule.

30. Define AND –Elimination rule in propositional logic

AND elimination rule states that from a given conjunction it is possible to

inference any of the conjuncts.

1 ^ ------^ n

2 i

31.Define AND-Introduction rule in propositional logic



AND-Introduction rule states that from a list of sentences we can infer their conjunctions

, 2,……..

1n 12n ^ ^…….^

32. Define OR-Introduction rule in propositonal logic

1

____________________

1v 2v………v n

OR-Introduction rule states that from, a sentence, we can infer its disjunction with

anything.

PART- B QUESTIONS

1. Explain the backward chaining algorithm.

2. Explain alpha-beta pruning algorithm with its procedure.

3. Explain the resolution for first order logic and inference rule

4. Illustrate the use of first-order-logic to represent the knowledge.

5. Explain the unification algorithm with an example.

6. Explain the unification algorithm with an example.



Unit III

PART - A

1. Define state space search

The most straightforward approach is to use state-space search. Because the descriptions of actions in a

planning problem specify both preconditions and effects, it is possible to search in either direction:

either forward from the initial state or backward from the goal

2. What are the types of state space search

Forward state space search & Backward state space search

3. Define Forward state-space search

It is sometimes called progression planning, because it moves in the forward direction.

4. What are the advantages of Backward state-space search

The main advantage of backward search is that it allows us to consider only relevant actions.

5. Define Partial-Order Planning

A set of actions that make up the steps of the plan. These are taken from the set of actions in the

planning problem. The “empty” plan contains just the Start and Finish actions. Start has no

preconditions and has as its effect all the literals in the initial state of the planning problem. Finish has

no effects and has as its preconditions the goal literals of the planning problem.



6. What are the advantages of Partial-Order Planning

Partial-order planning has a clear advantage in being able to decompose problems into sub problems. It

also has a disadvantage in that it does not represent states directly, so it is harder to estimate how far a

partial-order plan is from achieving a goal.

7. What are Planning Graphs

A Planning graph consists of a sequence of levels that correspond to time steps in the plan where level 0

is the initial state. Each level contains a set of literals and a set of

Actions

8. What is Conditional planning?

Also known as contingency planning, conditional planning deals with incomplete information by

constructing a conditional plan that accounts for each possible situation or contingency that could arise

9. What is action plan?

The process of checking the preconditions of each action as it is executed, rather

than checking the preconditions of the entire remaining plan. This is called action monitoring



10. Define planning.

Planning can be viewed as a type of problem solving in which the agent

uses beliefs about actions and their consequences to search for a solution.

11. What are the features of an ideal planner?

i. The planner should be able to represent the states, goals and actions.

ii. The planner should be able to add new actions at any time.

iii. The planner should be able to use Divide and Conquer method for

solving very big problems.

12. What are the components that are needed for representing an action?

The components that are needed for representing an action are:

i. Action description.

ii. Precondition.

iii. Effect.

13. What are the components that are needed for representing a plan?

The components that are needed for representing a plan are:



i. A set of plans steps.

ii. A set of ordering constraints.

iii. A set of variable binding constraints.

iv. A set of casual link protection.

14. What are the different types of planning?

The different types of planning are as follows:

i. Situation space planning.

ii. Progressive planning.

iii. Regressive planning.

iv. Partial order planning.

v. Fully instantiated planning.

15. What are the ways in which incomplete and incorrect information’s can be handled in planning?

They can be handled with the help of two planning agents namely,

i. Conditional planning agent.

ii. Replanning agent.

16. Define a solution.

A solution is defined as a plan that an agent can execute and that guarantees the achievement of goal.



17. Define a complete plan.

A complete plan is one in which every precondition of every step is achieved by some other step.

18. Define a consistent plan.

A consistent plan is one in which there are no contradictions in the ordering or binding constraints.

19. Define conditional planning.

Conditional planning is a way in which the incompleteness of information is incorporated in terms of

adding a conditional step, which involves if – then rules.

20. Give the classification of learning process.

The learning process can be classified as:

i. Process which is based on coupling new information to previously

acquired knowledge

a. Learning by analyzing differences.

b. Learning by managing models.

c. Learning by correcting mistakes.

d. Learning by explaining experience.

ii. Process which is based on digging useful regularity out of data,



usually called as Data base mining:

a. Learning by recording cases.

b. Learning by building identification trees.

c. Learning by training neural networks.

21. What is Induction heuristics?

Induction heuristics is a method, which enable procedures to learn descriptions from positive and

negative examples.

22. What are the different types of induction heuristics?

There are two different types of induction heuristics. They are:

i. Require-link heuristics.

ii. Forbid-link heuristics.

23. What are the principles that are followed by any learning procedure?

i. The wait and see principle.

ii. The no altering principle.

iii. Martin’s law.



24. State the wait and see principle.

The law states that, “When there is doubt about what to do, do nothing”

25. State the no altering principle.

The law states that, “ When an object or situation known to be an example, fails to match a general

model, create a special case exception model”.

26. State Martin’s law.

The law states that, “ You cannot learn anything unless you almost know it already”.

27. Define Similarity nets.

Similarity net is an approach for arranging models. Similarity net is a representation in which nodes

denotes models, links connect similar models and links are tied to different descriptions.

28. Define Reification.

The process of treating something abstract and difficult to talk about as though it were concrete and

easy to talk about is called as reification.

29. What is reified link?



The elevation of a link to the status of a describable node is a kind of reification. When a link is so

elevated then it is said to be a reified link.

30. Define Backward state-space search

It searches backward from the goal situation to the initial situation.

31. Differentiate between Partial Order Plan & Total order plan.

Partial-order plan

• consists partially ordered set of actions

• sequence constraints exist on these actions

• plan generation algorithm can be applied to transform partial-order plan to total-order plan

Total-order plan

• consists totally ordered set of actions

32. Define action monitoring

The process of checking the preconditions of each action as it is executed, rather than checking the

preconditions of the entire remaining plan. This is called action monitoring.

33. What is meant by Execution monitoring

Execution monitoring is related to conditional planning in the following way. An agent that builds a plan

and then executes it while watching for errors is, in a sense, taking into account the possible conditions

that constitute execution errors.

34. List the two different ways to deal with the problems arising from incomplete and incorrect

information



• Conditional planning

• Execution monitoring

35. Differentiate between Forward state-space search and Backward state-space search.

1. Forward state-space search : It searches forward from the initial situation to the goal situation.

2. Backward state-space search: It searches backward from the goal situation to the initial situation.

36. What are the steps of planning problems using state space research

methodology?

• The initial state of the search is the initial state from the planning problem. In general,

each state will be a set of positive ground literals; literals not appearing are false.

• The actions that are applicable to a state are all those whose preconditions are satisfied.

The successor state resulting from an action is generated by adding the positive effect

literals and deleting the negative effect literals. (In the first-order case, we must apply

the unifier from the preconditions to the effect literals.) Note that a single successor

function works for all planning problems—a consequence of using an explicit action

representation.

• The goal test checks whether the state satisfies the goal of the planning problem.



• The step cost of each action is typically 1. Although it would be easy to allow different

costs for different actions, this is seldom done by STRIPS planners.

37. What is the function of a replanning agent?

A simple replanning agent uses execution monitoring and splices in subplans as needed.

PART- B QUESTIONS

1. Explain about partial order planning with an example.

2. Explain about the different types of state space searches.

3. Explain about partial order planning algorithm.

4. Describe in detail about planning graphs.

5. Explain in detail about graph plan algorithm.

6. Explain in detail about conditional planning with an example.

7. Explain about re planning agent algorithm.

Unit IV

PART - A

1. Why does uncertainty arise ?

• Agents almost never have access to the whole truth about their

environment.



• Agents cannot find a caterorial answer.

• Uncertainty can also arise because of incompleteness, incorrectness in

agents understanding of properties of environment.

2. State the reason why first order, logic fails to cope with that the mind like

medical diagnosis.

Three reasons

a.laziness:

it is hard to lift complete set of antecedents of consequence,

needed to ensure and exceptionless rule.

b. Theoritical Ignorance:

medical science has no complete theory for the domain.

Practical ignorance:

even if we know all the rules, we may be uncertain about a particular item needed.

2. Define the term utility?

The term utility is used in the sense of \"the quality of being useful .\", utility of a state is relative to the

agents, whose preferences the utility function is supposed to represent.

3. What is the need for probability theory in uncertainty ?

Probability provides the way of summarizing the uncertainty that comes from our laziness and ignorance

. Probability statements do not have quite the same kind of semantics known as evidences.



5. What is the need for utility theory in uncertainty?

Utility theory says that every state has a degree of usefulness, or utility to In agent, and that the agent

will prefer states with higher utility. The use utility theory to represent and reason with preferences.

6. What is called as principle of maximum expected utility ?

The basic idea is that an agent is rational if and only if it chooses the action that yields the highest

expected utility, averaged over all the possible outcomes of the action. This is known as MEU.

7. What Is Called As Decision Theory ?

Preferences As Expressed by Utilities Are Combined with Probabilities in the General Theory of Rational

Decisions Called Decision Theory. Decision Theory = Probability Theory + Utility Theory.

8. Define Prior Probability?

p(a) for the Unconditional or Prior Probability Is That the Proposition A is True. It is important to

remember that p(a) can only be used when there is no other information.

9. Define conditional probability?

Once the agents has obtained some evidence concerning the previously

unknown propositions making up the domain conditional or posterior probabilities

with the notation p(A/B) is used. This is important that p(A/B) can only be used when all be is known.



10. Define probability distribution:

If we want to have probabilities of all the possible values of a random

variable probability distribution is used.

Eg.

P(weather) = (0.7,0.2,0.08,0.02). This type of notations simplifies many equations.

11. What is an atomic event?

An atomic event is an assignment of particular values to all variables, in

other words, the complete specifications of the state of domain.

12. Define joint probability distribution

This completely specifies an agent\'s probability assignments to all

propositions in the domain.The joint probability distribution p(x1,x2,--------xn)

assigns probabilities to all possible atomic events;where X1,X2------Xn=variables.

13. Give the Baye\'s rule equation

W.K.T P(A ^ B) = P(A/B) P(B) -------------------------- 1

P(A ^ B) = P(B/A) P(A) -------------------------- 2

DIVIDING BYE P(A) ; WE GET



P(B/A) = P(A/B) P(B)

--------------------

P(A)

14. What is meant by belief network?

A belief network is a graph in which the following holds

• A set of random variables

• A set of directive links or arrows connects pairs of nodes.

• The conditional probability table for each node

• The graph has no directed cycles.

15. What are the ways in which one can understand the semantics of a belief

network?

There are two ways to see the network as a representation of the joint probability distribution to view it

as an encoding of collection of conditional independence statements.

16. What is the basic task of a probabilistic inference?

The basic task is to reason in terms of prior probabilities of conjunctions, but for the most part, we will

use conditional probabilities as a vehicle for probabilistic inference.

17. What are called as Poly trees?



The algorithm that works only on singly connected networks known as

Poly trees. Here at most one undirected path between any two nodes is present.

18. Define casual support

E+X is the casual support for X- the evidence variables \"above\" X that

Are connected to X through its parent.

19. Define evidential support

E-X is the evidential support for X- the evidence variables \"below\" X that

Are connected to X through its children.

20. What is called as multiple connected graph?

A multiple connected graph is one in which two nodes are connected by more than one path.

21. List the 3 basic classes of algorithms for evaluating multiply connected graphs.

• Clustering methods

• Conditioning methods

• Stochastic simulation methods

22. Define Uncertainty.



Uncertainty means that many of the simplifications that are possible with deductive inference are no

longer valid.

23. What is meant by deterministic nodes?

A deterministic node has its value specified exactly by the values of its parents, with no uncertainty.

24. What are all the various uses of a belief network?

• Making decisions based on probabilities in the network and on the agent\'s utilities.

• Deciding which additional evidence variables should be observed in order to gain useful

information.

• Performing sensitivity analysis to understand which aspects of the model have the greatest

impact on the probabilities of the query variables (and therefore must be accurate).

• Explaining the results of probabilistic inference to the user.

25. What is the function of cutset conditioning method?

This method transforms the network into several simpler polytrees.

26. What is the use of Dempster- Shafer theory?

It is designed to deal with the distinction between uncertainty and ignorance.

27. What is the use of Fuzzy set theory?



Fuzzy set theory is a means of specifying how well an object satisfies a vague description.

PART- B QUESTIONS

1. Explain about Bayesian network with an example.

2. Explain in detail about conditional probability.

3. Explain about Markov Process.

4. Explain in detail about dynamic Bayesian networks.

5. Explain in detail about Enumeration algorithm.

6. Explain about inference in Bayesian network

Unit V

16 Marks

1. Explain the stages in communication.

2. Describe the augmented grammar.

3. What is the probabilistic Language model? Explain.

4. Describe the process involved in communication using the example sentence

“ The wumpus is dead”

5. Write short notes on semantic interpretation?

6. Illustrate the learning from examples by induction with suitable examples

7 Explain briefly about the following Information retrieval



8. Explain briefly about the following Information extraction

PART - A

1. What is meant by learning?

Learning is a goal-directed process of a system that improves the knowledge or the knowledge

representation of the system by exploring experience and prior knowledge.

2. Define informational equivalence.

A transformation from on representation to another causes no loss of information; they can be

constructed from each other.

3. Define computational equivalence.

The same information and the same inferences are achieved with the same amount of effort.

4. List the difference between knowledge acquisition and skill refinement.

• knowledge acquisition (example: learning physics) — learning new symbolic information coupled with

the ability to apply that information in an effective manner

• skill refinement (example: riding a bicycle, playing the piano) — occurs at a subconscious level by

virtue of repeated practice

5. What is meant by analogical reasoning?

Instead of using examples as foci for generalization, one can use them directly to solve new problems.



6. Define Explanation-Based Learning.

The background knowledge is sufficient to explain the hypothesis. The agent does not learn anything

factually new from the instance. It extracts general rules from single examples by explaining the

examples and generalizing the explanation

7. What is meant by Relevance-Based Learning?

• uses prior knowledge in the form of determinations to identify the relevant attributes

• generates a reduced hypothesis space

8. Define Knowledge-Based Inductive Learning.

Knowledge-Based Inductive Learning finds inductive hypotheses that explain set of observations with

the help of background knowledge.

9. What is truth preserving?

An inference algorithm that derives only entailed sentences is called sound or truth preserving.

10. Define Inductive learning.

Learning a function from examples of its inputs and outputs is called inductive learning.

11. How the performance of inductive learning algorithms can be measured?



It is measured by their learning curve, which shows the prediction accuracy as a function of the number

of observed examples.

12. List the advantages of Decision Trees

• It is one of the simplest and successful forms of learning algorithm.

• It serves as a good introduction to the area of inductive learning and is easy to implement.

13. What is the function of Decision Trees?

A decision tree takes as input an object or situation by a set of properties, and outputs a yes/no

decision. Decision tree represents Boolean functions.

14. List some of the practical uses of decision tree learning.

• Designing oil platform equipment

• Learning to fly

15. Define reinforcement learning.

The task of reinforcement learning is to use rewards to learn a successful agent function.



16. Differentiate between Passive learner and Active learner.

A passive learner watches the world going by, and tries to learn the utility of being in various states. An

active learner acts using the learned information, and can use its problem generator to suggest

explorations of unknown portions of the environment.

17. State the design issues that affect the learning element.

• Which components of the performance element are to be improved

• What representation is used for those components

• What feedback is available

• What prior information is available

18. State the factors that play a role in the design of a learning system.

• Learning element

• Performance element

• Critic

• Problem generator

19. What is memoization?

The technique of memorization is used to speed up programs by saving the results of computation. The

basic idea is to accumulate a database of input/output pairs; When the function is called, it first checks

the database to see if it can avoid solving the problem from scratch.



20. Define Q-Learning.

The agent learns an action-value function giving the expected utility of taking a given action in a given

state. This is called Q-Learning.

21. Differentiate between supervised learning & unsupervised learning.

Any situation in which both inputs and outputs of a component can be perceived is called supervised

learning. Learning when there is no hint at all about the correct outputs is called unsupervised learning.

22. Define Ockham’s razor.

Extracting a pattern means being able to describe a large number of cases in a concise way. Rather than

just trying to find a decision tree that agrees with example, try to find a concise one, too.

23. Define Bayesian learning

Bayesian learning simply calculates the probability of each hypothesis, given the data,

and makes predictions on that basis. That is, the predictions are made by using all the hypotheses,

weighted by their probabilities, rather than by using just a single “best” hypothesis.

24. What is meant by hidden variables?



Many real-world problems have hidden variables (sometimes called latent variables) which are not

observable in the data that are available for learning.

25. Define Cross validation.

The basic idea behind Cross validation is try to eliminate how well the current hypothesis will predict

unseen data.

26. What are the operations in Genetic algorithms?

It starts with a set of one or more individuals and applies selection and reproduction operators to evolve

an individual that is successful, as measured by a fitness function.

27. List the various Components of the performance element

1. A direct mapping from conditions on the current state to actions.

2. A means to infer relevant properties of the world from the percept sequence.

3. Information about the way the world evolves.

4. Information about the results of possible actions the agent can take.

5. Utility information indicating the desirability of world states.

6. Action-value information indicating the desirability of particular actions in particular states.

7. Goals that describe classes of states whose achievement maximizes the agent\'s utility.

27. Differentiate between Parity function and majority function.

If the function is the parity function, which returns 1 if and only if an even number of inputs are 1, then

an exponentially large decision tree will be needed.



A majority function, which returns 1 if more than half of its inputs are 1.

28. What is the function of a performance element?

The performance element is responsible for selecting external actions.

29. What is the function of a learning element?

Learning element is responsible for making improvements.

30. List the 3 approaches that can be used to learn utilities.

1. Least-mean-square Approach

2. Adaptive Dynamic Programming Approach

3. Temporal Difference Approach

PART- B QUESTIONS

1. Explain the learning decision tree with algorithm

2. (i).Explain the explanation based learning?

(ii).Explain how learning with complete data is achieved?



3. Discuss learning with hidden variables?

4. Explain all the statistical learning method available in AI.

5. Explain about Reinforcement learning.

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