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.
4. 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.
5. What does Turing test mean?
The Turing test proposed by Alan Turing was designed to provide a
satisfactory operational definition of intelligence. Turing defined intelligent
behavior as the ability to achieve human-level performance in all cognitive tasks,
sufficient to fool an interrogator.
Turing test?
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.
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7. What is called materialism?
An alternative to dualism is materialism, which holds that the
entire world operate according to physical law. Mental process and
consciousness are therefore part of physical world, but inherently unknowable
they are beyond rational understanding.
8. Define an agent.
An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon the environment through effectors.
9. 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.
10. Define an Omniscient agent.
An omniscient agent knows the actual outcome of its action and can
act accordingly; but omniscience is impossible in reality.
11. 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.
12. 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.
13. Define an agent program.
Agent program is a function that implements the agents mapping
from percept to actions.
14. Define Architecture.
The action program will run on some sort of computing device which
is called as Architecture.
15. List the various type of agent program.
 Simple reflex agent program.
 Agent that keep track of the world.
 Goal based agent program.
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 Utility based agent program.
16. 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.
17. What are the phases involved in designing a problem solving agent?
The three phases are:
Problem formulation, Search solution, Execution.
18. What are the different types of problem?
Single state problem, Multiple state problem, Contingency problem,
Exploration problem.
19. Define problem.
A problem is really a collection of information that the agent will use to
decide what to do.
20. List the basic elements that are to be include in problem definition.
Initial state, operator, successor function, state space, path, goal test,
path cost.
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UNIT II
1. What is the use of QUEUING_FN?
QUEUING_FN inserts asset of elements into the queue. Different
varieties of queuing fn produce different varieties of the search algorithm.
2. Mention the criteria for the evaluation of search strategy.
There are 4 criteria:
Completeness, time complexity, space complexity, optimality.
3. 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.
4. List the various search strategies.
a. BFS
b. Uniform cost search
c. DFS
d. Depth limited search
e. Iterative deepening search
f. Bidirectional search
5. 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
6. Differentiate BFS & DFS.
BFS
BFS means breath wise search DFS means depth wise search
Space complexity is more
Do not give optimal solution
Queuing fn is same as that of
queue operator
Space complexity is less
Gives optimal solution
Queuing fn is some what different
from queue operator.
7. Whether uniform cost search is optimal?
Uniform cost search is optimal & it chooses the best solution depending
on the path cost.
8. Write the time & space complexity associated with depth limited search.
Time complexity =O (bd) , b-branching factor, d-depth of tree
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Space complexity=o (bl)
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14.
Function: GENERAL_SEARCH (problem, queuing fn) return success
(failure)
Nodes MAKE_QUEUE (MAKE_NODE (INITIAL STATE [problem]))
Loop do
If nodes is empty then return failure
NodeREMOVE_FRONT (nodes)
If GOAL_TEST (problem) apply to STATE (node) succeeds then return
node
Nodes QUEUING_FN (nodes, EXPAND (nodes, OPERATORS
[problem]))
End
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15. Differentiate greedy search & A* search.
Greedy Search
A*search
If we minimize the estimated cost
to reach the goal h(n),we get
greedy search
The search time is usually
d e c r e a s e d c o m p a r e d t o
uniformed alg,but the alg is
neither optimal nor complete
16. 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 all nodes inside the contour for
the current f-cost.
17. What is the advantage of memory bounded search techniques?
We can reduce space requirements of A* with memory bounded alg such
as IDA* & SMA*.
18. 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.
20. 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.
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1.Define a knowledge Base:
UNIT III
Knowledge base is the central component of knowledge base agent and it
is described as a set of representations of facts about the world.
2.Define a Sentence?
Each individual representation of facts is called a sentence. The
sentences are expressed in a language called as knowledge representation
language.
3. 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
4.What are the three levels in describing knowledge based agent?
5.Define Syntax?


Logical level
Implementation level
Knowledge level or epistemological level
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.
6.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.
7.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.
8.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 .
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9.What is truth Preserving
An inference algorithm that derives only entailed sentences is called
sound or truth preserving .
10.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.
11.Define a Complete inference procedure
An inference procedure is complete if it can derive all true conditions from
a set of premises.
.Define Interpretation
Interpretation specifies exactly which objects, relations and functions are
reffered to by the constant predicate, and function symbols.
13.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.
14.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.
15.Define true sentence
A sentence is true under a particular interpretation if the state of affairs it
represents is the case.
16.What are the basic Components of propositonal logic?
i. Logical Constants (True, False)
17.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.
18.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 ^
2
------^n i
19.Define AND-Introduction rule in propositional logic
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AND-Introduction rule states that from a list of sentences we can infer their
conjunctions.

1
,2,……..
n12n^^…….^

20.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.
UNIT – IV
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:
o it is hard to lift complete set of antecedents of consequence,
needed to ensure and exceptionless rule.
 b. Theoritical Ignorance:
o 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.
3. 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.
4.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?
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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:
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
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=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.
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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.
UNIT-V
1. 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.
2.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.
3. What are the components that are needed for representing an action?
The components that are needed for representing an action are:
i.
ii.
iii.
Action description.
Precondition.
Effect.
4. What are the components that are needed for representing a plan?
The components that are needed for representing a plan are:
i.
ii.
A set of plans steps.
A set of ordering constraints.
5. What are the different types of planning?
The different types of planning are as follows:
i.
ii.
iii.
Situation space planning.
Progressive planning.
Regressive planning.
iv. Partial order planning.
v.
Fully instantiated planning.
6. 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,
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i.
ii.
Conditional planning agent.
Replanning agent.
7. Define a solution.
A solution is defined as a plan that an agent can execute and thjat
guarantees the achievement of goal.
8. Define a complete plan.
A complete plan is one in which every precondition of every step is
achieved by some other step.
9. Define a consistent plan.
A consistent plan is one in which there are no contradictions in the
ordering or binding constraints.
10. 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.
11. Give the classification of learning process.
The learning process can be classified as:
i.
ii.
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.
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.
12. What is Induction heuristics?
descriptions from positive and negative examples.
13. What are the different types of induction heuristics?
There are two different types of induction heuristics. They are:
i.
ii.
Require-link heuristics.
Forbid-link heuristics.
14. What are the principles that are followed by any learning procedure?
i.
The wait and see principle.
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ii.
iii.
The no altering principle.
Martin’s law.
15. State the wait and see principle.
The law states that, “When there is doubt about what to do, do nothing”
16. 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”.
17. State Martin’s law.
The law states that, “ You cannot learn anything unless you almost know it
already”.
18.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.
19. 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.
20.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.
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