The document defines different approaches to artificial intelligence including: 1. Systems that think like humans through cognitive modeling of human thought processes. 2. Systems that think rationally by following logical rules and principles like Aristotle's laws of thought. 3. Systems that act rationally by perceiving the environment, acting to achieve goals based on beliefs, and being modeled as rational agents.

1. ARTIFICIAL INTELLEGENCE
UNIT I
1. Define AI as” Systems that think like humans”?
The exciting new effort to make computers think . . . machines with minds, in the full
and literal sense.
The automation of activities that we associate with human thinking, activities such as
decision-making, problem solving, learning.
2. Define AI as” Systems that think rationally”?
The study of mental faculties through the use of computational models.
The study of the computations that make it possible to perceive, reason, and act.
3. Define AI as” Systems that act like humans”?
The art of creating machines that perform functions that require intelligence when
performed by people.
The study of how to make computers do things at which, at the moment, people are
better.
4. Define AI as” Systems that act rationally”?
Computational intelligence is the study of design of intelligent agents.
Artificial intelligence is concerned with intelligent behavior in artifacts.
5. Explain the turning test approach for ‘acting humanly’?
Turing defined intelligent behavior as the ability to achieve human-level performance in
all cognitive tasks, sufficient to fool an interrogator.
The test he proposed is that the computer should be interrogated by a human via a
teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the
other end.
6. What are the things the computer needs to act as human?
The computer would need to possess the following capabilities:
 natural language processing to enable it to communicate successfully in English (or
some other human language).
 knowledge representation to store information provided before or during the
interrogation.
 automated reasoning to use the stored information to answer questions and to draw new
conclusions.
 machine learning to adapt to new circumstances and to detect and extrapolate patterns.
7. To pass the total Turing Test, the computer needs what?
To pass the total Turing Test, the computer will need the, (COMPUTER VISION )
computer vision to perceive objects, and ROBOTICS robotics to move them about.

2. 8. Explain the cognitive modelling approach for’ Thinking humanly’?
If we say that a given program thinks like a human, we must have some way of
determining how humans think. We need to get inside the actual workings of human minds.
There are two ways to do this: through introspection—trying to catch our own thoughts as they
go by—or through psychological experiments.
9. Explain the laws of thought approach for’ Thinking rationally’?
The Greek philosopher Aristotle was one of the first to attempt to codify "right
thinking," that is, irrefutable reasoning processes. His famous syllogisms provided patterns for
argument structures that always gave correct conclusions given correct premises. For example,
"Socrates is a man; all men are mortal; therefore Socrates is mortal." These laws of thought were
supposed to govern the operation of the mind, and initiated the field of logic.
10. Explain the rational agent approach for’ acting rationally’?
Acting rationally means acting so as to achieve one's goals, given one's beliefs. An
agent is just something that perceives and acts. (This may be an unusual use of the word, but you
will get used to it.) In this approach, AI is viewed as the study and construction of rational
agents.
11.Give the advantages when we study AI as rational agent design?
o it is more general than the "laws of thought" approach, because correct inference
is only a useful mechanism for achieving rationality, and not a necessary one.
o it is more amenable to scientific development than approaches based on human
behavior or human thought, because the standard of rationality is clearly defined
and completely general.
12.What are all the fields from which AI can be inherited?
AI can be inherited from
 Psychology
 Linguistic
 Philosophy
 Mathematics
 CS
 Economics
 Neuro science
 Control theory and cybernetics
13.what is the first successful knowledge-intensive system. Explain?
the first successful knowledge-intensive system is DENDRAL.
DENDRAL determines the 3D structure of the difficult chemical compound.
14. what is the first successful rule based expert system. Explain?
the first successful knowledge-intensive system is MYCIN.
MYCIN is used to diagonise the blood infectious disease.

3. 15.give the applications of AI?
The areas where AI can be applied are
 Autonomous planning and scheduling
 Game playing
 Autonomous control
 Diagnosis
 Logistic planning
 Robotics
16.Define an ‘Intelligent agent’?
An agent is anything that can be viewed as perceiving its environment through
sensors and acting upon that environment through actuators.
Eg: vaccum cleaner
17. Give the sensors and actuators for human, robotics and software agent?
Robotics agent:
Sensors: infrared rays, cameras
Actuators:motor
software agent:
Sensors: keys in keyboard
Actuators:output displayed on screen
Human agent:
Sensors: eyes, ears, and other organs
Actuators: other body parts
18. How does an Agents interact with environments through sensors and effectors.?
PERCEPTS
SENSORS
ACTION
ACTUATORS
19. 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 and whatever built-in knowledge the agent has.
ENVIRONMENT
AGENT

4. 20.what are the four things rational agent depend on?
the rational agent depends on four things:
• The performance measure that defines degree of success.(P)
• What the agent knows about the environment.(E)
• Everything that the agent has perceived so far. We will call this complete perceptual history
the percept sequence.(A)
• The actions that the agent can perform.(S)
21.what is an agent function?
An agent program tries to implement the agent architecture is called agent function.
Agent function=agent architecture + agent program
22.what are four types of environment?
The four types of environment are
1. Accessible vs. inaccessible
2.deterministic vs nondeterministic
3.episodic vs nonepisodic
4.static vs dynamic
5.discrete vs continuous
6.single agent vs multi agent
23.explain Accessible vs. inaccessible environment?
If an agent's sensory apparatus gives it access to the complete state of the
environment, then we say that the environment is accessible to that agent. An environment is
effectively accessible if the sensors detect all aspects that are relevant to the choice of action. An
accessible environment is convenient because the agent need not maintain any internal state to
keep track of the world.
24. explain Deterministic vs. nondeterministic environment?
If the next state of the environment is completely determined by the current state and
the actions selected by the agents, then we say the environment is deterministic. In principle,
an agent need not worry about uncertainty in an accessible, deterministic environment. If
the environment is inaccessible, however, then it may appear to be nondeterministic.

5. 25. explain Episodic vs. nonepisodic environment?
In an episodic environment, the agent's experience is divided into "episodes." Each
episode consists of the agent perceiving and then acting. The quality of its action depends just on
the episode itself, because subsequent episodes do not depend on what actions occur in previous
episodes. Episodic environments are much simpler because the agent does not need to think
ahead.
26. explain Static vs. dynamic environment?
If the environment can change while an agent is deliberating, then we say the
environment is dynamic for that agent; otherwise it is static. Static environments are easy to deal
with because the agent need not keep looking at the world while it is deciding on an action, nor
need it worry about the passage of time. If the environment does not change with the passage of
time but the agent's performance score does, then we say the environment is
semidynamic.
27. explain Discrete vs. continuous environment?
If there are a limited number of distinct, clearly defined percepts and actions we
say that the environment is discrete. Chess is discrete—there are a fixed number of possible
moves on each turn. Taxi driving is continuous—the speed and location of the taxi and the other
vehicles sweep through a range of continuous values.
28.what are the four types of agent program?
the four types of agent program:
• Simple reflex agents
• model based agent
• Goal-based agents
• Utility-based agents
29.what is simple reflex agent?
It is the simplest kind of agent.these agent selects action on the basis of the current
percept,ignoring the rest of the percept history.
Eg:vaccum agent
30. what is model based agent?
The most effective way to handle partial observability is for the agent to keep track
of the part of the world it can’t see now. That is ,the agent should maintain some sort of internal
states that depends on the percept history and therby reflects atleast some of the unobserved
aspects of the current state.
31.what is Goal-based agents?
Knowing about the current state of the environment is not always enough to decide
what to do. For example, at a road junction, the taxi can turn left, right, or go straight on. The

6. right decision depends on where the taxi is trying to get to. In other words, as well as a current
state description, the agent needs some sort of goal information, which describes situations that
are desirable— for example, being at the passenger's destination.
32.what is utility based agent?
Goals alone are not really enough to generate high-quality behavior. For example, there
are many action sequences that will get the taxi to its destination, thereby achieving the goal, but
some are quicker, safer, more reliable, or cheaper than others.
the customary terminology is to say that if one world state is preferred to another,
then it has higher utility for the agent.
Utility is therefore a function that maps a state9 onto a real number, which describes
the associated degree of happiness.
33.what is the advantage of learning agent?
It allows the agent to operate in initially unknown environment and to become more
competent than its initial knowledge alone might alow.
34.what are the four components of learning agent?
The four components of learning agent are
Learning element
Performance element
Critic
Problem generator
35.explain the components of learning agent?
Learning element-it is responsible for improvement of the agent.
Performance element-what action it does for particular percept.
Problem generator- one which improves the learning element.it is responsible for giving
suggestions and actions to learning element.
Critic-used to get feedback from environment
UNIT –II
1. What is knowledge based agent?
The central component of a knowledge-based agent is. Its knowledge base or KB.
Informally, a knowledge base is a set of representations of facts about the world. Each individual
representation. SENTENCE" is called a sentence the sentences are expressed in a language
called a knowledge representation language.
2. What are the three levels of KB?
 The knowledge level or epistemological level
 The logical level
 The implementation level
3. Describe Wumpus world?

7. The Wumpus world is a grid of squares surrounded by walls, where each square can
contain agents and objects. The agent always starts in the lower left corner, a square that we will
label [1, 1]. The agent's task is to find the gold, return to [1, 1] and climb out of the cave.
4. What is Knowledge Representation?
Knowledge representation is to express knowledge in computer-tractable
Form, such that it can be used to help agents perform well. A knowledge representation language
is defined by two aspects:
 The syntax
 The semantics
5. What is Sound OR Truth-Preserving?
An inference procedure that generates only entailed sentences is called sound or truth-
preserving.
6. Define Inference?
The terms "reasoning" and "inference" are generally used to cover any process by which
conclusions are reached. In this chapter, we are mainly concerned with sound reasoning, which
we will call logical inference or deduction. Logical inference is a process that implements the
entailment relation between sentences.
7. Define Validity?
A sentence is valid or necessarily true if and only if it is true under all possible
interpretations in all possible worlds, that is, regardless of what it is supposed to mean and
regardless of the state of affairs in the universe being described.
8. Define Satisfiability?
A sentence is satisfiable if and only if there is some interpretation in some world for
which it is true. The sentence "there is a wumpus at [1, 2]" is satisfiable because there might well
be a wumpus in that square, even though there does not happen to be one in
Sentence that is not satisfiable is unsatisfiable. Self-contradictory sentences are unsatisfiable, if
the contradictoriness does not depend on the meanings of the symbols.
9. Define Logic?
Logic consists of the following:
1. A formal system for describing states of affairs, consisting of
(a) The syntax of the language, which describes how to make sentences, and
(b) The semantics of the language, which states the systematic constraints on how
sentences. Relate to states of affairs.
2. The proof theory—a set of rules for deducing the entailments of a set of
sentences.
10. Define Propositional Logic?

8. In propositional logic, symbols represent whole propositions (facts); for example, D
might have the interpretation "the wumpus is dead." which may or may not be a true proposition.
Proposition symbols can be combined using Boolean connectives to generate sentences with
more complex meanings.
11. Define First-Order Logic?
First-order logic commits to the representation of worlds in terms of objects and
predicates on objects, as well as using connectives and quantifiers, which allow sentences to be
written about everything in the universe at once. First-order logic seems to be able to capture a
good deal of what we know about the world, and has been studied for about a hundred years.
12. State Rules of inference for propositional logic?
The process by which the soundness of an inference is established through truth tables
can be extended to entire classes of inferences. There are certain patterns of inferences that occur
over and over again, and their soundness can be shown once and for all. Then the pattern can be
captured in what is called an inference rule.
13. What is Models?
Any world in which a sentence is true under a particular interpretation is called a model
of that sentence under that interpretation.
14. Define Monotonicity?
The use of inference rules to draw conclusions from a knowledge base relies implicitly on
a general property of certain logics (including prepositional and first-order logic) called
monotonicity.
15. Define Horn Sentences?
There is also a useful class of sentences for which a polynomial-time inference procedure
exists. This is the class called Horn sentences.8 A Horn sentence has the form:
PI A P2 A ... A Pn => Q Where the P, and Q are no negated atoms.
16. Define Terms?
A term is a logical expression that refers to an object.
17. Define Atomic Sentences?
An atomic sentence is formed from a predicate symbol followed by a parenthesized list of
terms. An atomic sentence is true if the relation referred to by the predicate symbol holds
between the objects referred to by the arguments.
18. Define Complex Sentences?
Use logical connectives to construct more complex sentences, just as in propositional
calculus. The semantics of sentences formed using logical connectives is identical to that in the
propositional case.

9. 19. Define Ground Term?
A term with no variables is called a ground term.
20. Define Situation Calculus?
Situation calculus is the name for a particular way of describing change in first-order
logic. It conceives of the world as consisting of a sequence of situations, each of which is a
"snapshot" of the state of the world. Situations are generated from previous situations by actions.
21. Give the three Inference Rules?
The three new inference rules are as follows:
 Universal Elimination
 Existential Elimination
 Existential Introduction
22. What is Universal Elimination?
Universal Elimination: For any sentence a, variable v, and ground term g:
V v a
SUBST ({v/g}, )
For example, from x, Likes(x, Ice Cream), we can use the substitution {x/Ben} and infer
Likes (Ben, Ice Cream).
23. What is Existential Elimination?
Existential Elimination: For any sentence a, variable v, and constant symbol k that does
not appear elsewhere in the knowledge base:
v a
SUBST ({v/k}, )
For example, from x Kill(x, Victim), we can infer Kill (Murderer, Victim), as long as
Murderer does not appear elsewhere in the knowledge base.
24. What is Existential Introduction?
Existential Introduction: For any sentence o, variable v that does not occur in a, and
Ground term g that does occur in a:

v SUBST ({g/v}, )
25. What is Unification?

10. Unification: The job of the unification routine, UNIFY, is to take two atomic sentences p and q
and return a substitution that would make p and q look the same. (If there is no such substitution,
then UNIFY should return fail.) Formally,
UNIFY (p, q) = 6 where SuBST ( , p) = SuBST ( , q)
is called the unifier of the two sentences.
FORWARD AND BACKWARD CHAINING:
26. What is forward chaining?
Start with the sentences in the knowledge base and generate new conclusions that in turn
can allow more inferences to be made. This is called forward chaining. Forward chaining is
usually used when a new fact is added to the database and we want to generate its consequences.
27. What is backward chaining?
Start with something we want to prove, find implication sentences that would allow us to
conclude it, and then attempt to establish their premises in turn. This is called backward
chaining, because it uses Modus Ponens backwards. Backward chaining is normally used when
there is a goal to be proved.
28. Define Renaming?
One sentence is a renaming of another if they are identical except for the names of the
variables. For example,
Likes(x, Ice Cream) and Likes(y, Ice Cream) are renaming of each other because they only
differing the choice of x or y, but Likes(x, x) and Likes(x, y) are not renaming of each other.
29. What is Semantic Net?
A Semantic net is a representation of nodes and links. It describes how the representation
is done in Artificial Intelligence.
30. Give the four fundamental parts of Semantic Net?
The four fundamental parts of Semantic net are
 Lexical part
 Structural part
 Procedural part
 Semantic part
31. What is Describe and Match method?
The basic idea is that you can identify an object by describing it and then searching for a
matching description in a description library.
32. What do you mean by Frames?
Each node and links that emanate from the node can be collected together, is called
Frame. Semantic net is either as a collection of nodes and links (or) collection of frames.
33. What do you mean by Chunk?

11. Each chunk is represented as a frame with slot and slot values.
34. What do you mean by Instances or Instance frames?
Frames which describe individual thing is called as Instances (or) Instances frames.
35. What do you mean bye classes (or) Class frames?
Frames which describe entire classes is called classes (or) class frames.
36. What do you mean by Access Procedures?
Access Procedures are required to construct and manipulate instances and classes of the
frame representation which include
 Class constructor
 Instance constructor
 Slot Writer
 Slot Reader
UNIT –III
1. State the conditions under which an uncertainity can arise?
Ans: Uncertainity can arise because of incompleteness and incorrectness in the agent's
understanding of the properties of the environment.
2.What are the three reasons why FOL fails in medical diagnosis?
Ans: The three reasons why the FOL fails in medical diagonsis are:
(i) Laziness: Too much work to lilst the complete set of antecedents and consequents
needed.
(ii) Theoretical ignorance: Medical science has no complete theory for domain.
(iii) Pratical ignorance: Even if we know all the rules uncertainity arises because some
tests cannot be run on the patient's body.
3. What is the tool that is used to deal with degree of belief?
Ans: The tool that is used to deal with the degree of belief is the probability theory which assigns
a numeric degree of belief between 0 and 1.
4. For what the utility theory is useful and in what way it is related to decision theory?
Ans: The utility theory is useful to represent and reason with preferences.

12. The decision theory is related to utility theory as addition of the proability theory and the
utility theory.
5. What is the fundamental idea of the decision theory?
Ans: The fundamental idea of the decision theory is that an agent is rational if and only if it
chooses the action that yields the highest expected utility .
6.How are the probability over the simple and complex propositions are classified?
Ans: The probability over the simple and complex propositions are classified as
(i) Unconditional or Prior probabilities.
(ii)Conditional or Posterior probabilities.
7. State the axioms of probability.
Ans: The axioms are:
(i) All probability are between 0 and 1. 0<= P(A)<=1
(ii) Necessarily true propositions have probability 1 and necessarily false propositions
have probability 0.P(True)=1,P(False)=0.
(iii) The probability of a disjunction is given by
P(A V B)= P(A) + P(B) - P(A ^ B)
(iv) Let B=(negation)A in the axiom3.
P(A V (negation)A )= P(A) + P( (negation)A) - P(A ^ (negation)A)
(v)P(true)=P(A) + P( (negation)A)-P(false)(by logical equivalence)
(vi)1= P(A) + P( (negation)A) (by step2)
(vii)P((negation)A)= 1- P(A) (by algebra)
8.What is Joint Probability Distribution?
Ans: An agent's probabitlity assignments to all propositions in the domain(both simple and
complex) is called as Joint Probability Distribution.
9.What is the disadvantage of Baye's rule?
Ans: It requires three terms to compute one conditional probability (P(B/A))
*One conditional probability -> P(A/B)

13. *Two unconditional probability -> P(B) & P(A)
10.What is the advantage of Baye's rule?
Ans: If three values are known then the unknown fourth value -> P(B/A) is computed easily.
11.What is a belief network?
Ans: A Belief network is a graph in which the following holds:
1. A set of random variables makes up the nodes of the network.
2. A set of directed links or arrows connects pairs of nodes X->Y, X has a direct influence on
Y.
3. Each node has a conditional probability table that quantity the effects that the parents have
on the node. The parents of a node are all nodes that have arrows pointing to it.
4. Graph has no directed cycles -> DAG.
12.What is the task of any Probabilistic inference system?
Ans: The Task of any Probabilistic inference system is to compute the posterior probability
distribution for a set of query variables,given exact values of some evidence variable.
13. State the uses of belief networks.
Ans: 1.Making decisions based on probabilities in the network & on the agent's abilities.
2.Deciding which additional evidence variables should be observed in order to gain useful
information.
3.Sensitivity Analysis: which model has great impact.
4.Explaining the probabilistic inference result to user.
14. What are the two ways in which one can understand the semantics of belief networks?
Ans: The two ways are
1.Network as a representation of the Joint Probabiltity distribution
-used to know how to construct networks.
2.Encoding of a collection of conditional independence statements.
-designing inference procedures.

14. 15.What is Probabilitic Reasoning?Explain
Ans: It explains how to build network models to reason under uncertainity according to the
laws of probability theory.
16.What are the disadvantages of Full joint Probability distribution?
Ans:The disadvantage is that, the interaction between the domain and the agent increases, the
number of variables is also increases and to overcome this we use a new datastructure called
Bayesian network.
17.Explain Bayesian Network.
Ans: Bayesian network is used to represnt the dependencies among variables and to give a
concise specification of any full joint probability distribution.
18.What makes the nodes of the Bayesian network and how are they connected?
Ans: A set of random variables makes up the nodes of the network.Variables may be discrete or
continuous and a set of directed links or arrows connect pair of nodes.
19.What is Conditional Probability Table(CPT)?
Ans: The table representation of the Bayesian network is called the Conditional probability
table.
20.What is Conditioning Case?
Ans: A Conditioning case is just a possible combination of values for the parent nodes.
21.What are the Semantics of Bayesian Network?
Ans: The Semantics can be represented as the
1.Global Semantics(Full joint probability distribution)
2.Local Semantics(Conditional independent statements)
22.State the way to representing the Full joint Distribution.
Ans: A generic entry in the joint distribution is the probability of a conjunction of particular
assignments to each variables,siuch as
p(x1=x1^----^xn)xn)=p(x1,---- xn)
23.When will a Bayesian Network ia said as Compact?
Ans: A Bayesian network is compact,when the information is complete and non-redundant.

15. The Compactness of Bayesian Network is an example of a very general property of locally
structured systems.
24.Explain Node Ordering.
Ans: The correct order in which to add nodes is to add the root causes(parents first and then the
variables) and the addition process is continued,until we reach the leaves which have no direct
casual inference on other variables.
25.What is Topological Semantics?
Ans: Topological Semantics is a graph structure and from these we can gerive the numerical
statements.
26.What is the specification for the Topological Semantics?
Ans: 1.A node is conditionally independent of its non-descendants,given its parents
2.A node is conditionally independent of all other nodes in the network,given its parents
children and childrens parents and this called the Markov blanket.
27.What are the two ways to represent Canonical Distributions?
Ans:1.Deterministic nodes
2.Noisy-OR relation
28.What are Deterministic nodes?
Ans: It is used to represent the certain knowledge, by means of the
x=f(parents(x)) for some function.
29.Explain Noisy-OR Relationship.
Ans: It is the generalization of logical OR and it is used to represent the uncertain knowledge.
30.What are the four types of inferences?
Ans: 1.Diagnostic Inferences
2.Casual Inferences
3.Intereasual Inferences
4.Mixed Inferences
31.What are the three basic classes of algorithms for evaluating multiply connected
networks?

16. Ans: The three basic classes of algorithms for evaluating multiply connected networks are
cluestring, conditioning methods and stochastic simulation methods.
32.What is done in clustering?
Ans: It transforms the network into a probabilistically equivalent polytrees by merging offending
nodes.
33.What is cutset?
Ans: A set of variable that can be instantiated to yield polytree is called a cutset i.e this method
transforms the network into several simpler polytrees.
34.What is a utility function?
Ans: An agent's preferences between world states are captured by a utility function which
assigns a single no. to express the derivability of a state utilities are combined with the outcome
probabilities for actions to give an expected utility for each action.
35.What is the principle behind the maximum expected utility?
Ans: The principle of MEU(Maximum Expected Utility) is that a rational agent should choose an
action that maximizes the agent's expected utility.
UNIT – IV
1.What is planning in AI?
Planning is done by a planning agent or a planner to solve the current world problem.
It has certain algorithms to implement the solutions for the given problem and this is called ideal
planner algorithm
2.How does a planner implement the solution?
Unify action and goal representation to allow selection
(use logical language for both)
Divide-and-conquer by subgoaling
Relax requirement for sequential construction of solutions
3.What is the difference between planner and problem solving agents?
Problem solving agent:it will represent the task of the problem and solves it using search
techniques and any other algorithm.
Planner:It overcomes the difficulties that arise in the problem solving agent.
4.what are the effects of non- planning?
 infinite branching factor in case of many tasks

17.  choosing heuristic function and works in the same sequence/order
5. what are the key ideas of planning approach?
3 key ideas to approach planning are
 open up the representation of states,goals ,actions using FOL
 the planner is free to add actions to the plan whenever and wherever they are needed rather
than an incremental sequence
 most part of the world are going to be independent of the other parts
6.State the 3 components of operators in representation of planning?
 Action description
 Pre-condition
 Effect
7. what is STRIPS?
STandford Research Institute Problem Solver
 Tidily arranged actions descriptions
 Restricted language (function-free literals)
 Efficient algorithms
8.What is situation space planner?
The planner takes the situation and searches for it in the KB and locates it.It is reused if
it is already present else a new plan is made for the situation and executed.
9. What are the drawbacks of programmer’s planner?
 High branching factor during searchin
 Huge search space
10. What are the Planning Algorithms for Searching a World Space:
Searching a World Space:
. There are two algorithms:
o Progression: An algorithm that searches for the goal state by searching through the states
generated by actions that can be performed in the given state, starting from the initial state.
o Regression: An algorithm that searches backward from the goal state by finding actions
whose effects satisfy one or more of the posted goals, and posting the chosen action's
preconditions as goals ( goal regression).
11.What is partial plan?
Partial plan is an incomplete plan which may be done during the Initial phase.
There are 2 main operation allowed in planning
 Refinement operator
 Modification operator
 (Partial) Plan consists of
 Set of operator applications Si
 Partial (temporal) order constraints Si ->Sj
 Causal links Si---c Sj

18. 12.what is fully instantiated plan?
A fully instantiated plan is formmaly defined as a data structure consisting of the following 4
components
 A set of plan steps
 A set of step monitoring constraints
 A set of variable binding constraints
 A set of casual links
13. What is Complete plan
A plan is complete iff every precondition is achieved
A precondition c of a step Sj is achieved (by Si) if
Si < Sj
c є effect(Si)
there is no Sk with Si <Sk < Sj and : ┐c є effect(Sk)
(otherwise Sk is called a clobberer or threat)
14. What are the Properties of POP?
Non-deterministic search for plan,
backtracks over choicepoints on failure:
. choice of Sadd to achieve Sneed
. choice of promotion or demotion for clobberer
Sound and complete
There are extensions for:
disjunction, universal quanti_cation, negation, conditionals
Ef_cient with good heuristics from problem description
But: very sensitive to subgoal ordering
Good for problems with loosely related subgoals
15. What is conditional planning?
Conditional planning
Plan to obtain information (observation actions)
Subplan for each contingency
Example: [Check(Tire1); If(Intact(Tire1); [In_ate(Tire1)]; [CallHelp])]
Disadvantage: Expensive because it plans for many unlikely cases
Similar to POP If an open condition can be established by observation action
. add the action to the plan
. complete plan for each possible observation outcome
16. What is monitoring/ replanning
Monitoring / Replanning
Assume normal states / outcomes
Check progress during execution, re plan if necessary
Disadvantage: Unanticipated outcomes may lead to failure.
17. Distinguish between the learning element and performance element
 Learning element is responsible for making improvements.

19. The learning element takes some knowledge about the learning element and some feedback on
how the agent is doing, and determines how the performance element should be modified to
(hopefully) do better in the future.
 Performance element is responsible for selecting external actions.
The performance element is what we have previously considered to be the entire agent: it takes
in percepts and decides on actions.
18. What is the role of critic in the general model of learning.
The critic is designed to tell the learning element how well the agent is doing. The critic
employs a fixed standard of performance. This is necessary because the percepts themselves
provide no indication of the agent's success.
19. Describe the problem generator.
It is responsible for suggesting actions that will lead to new and informative experiences. The
point is that if the performance element had its way, it would keep doing the actions that are best,
given what it knows. But if the agent is willing to explore a little, and do some perhaps
suboptimal actions in the short run, it might discover much better actions for the long run.
.
20. What is meant by Speedup Learning.
The learning element is also responsible for improving the efficiency of the performance
element. For example, when asked to make a trip to a new destination, the taxi might take a
while to consult its map and plan the best route. But the next time a similar trip is requested, the
planning process should be much faster. This is called speedup learning
21. What are the issues in the design of learning agents.
The design of the learning element is affected by four major issues:
 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.
22. List the Components of the performance element
 A direct mapping from conditions on the current state to actions.
 A means to infer relevant properties of the world from the percept sequence.
 Information about the way the world evolves.
 Information about the results of possible actions the agent can take.
 Utility information indicating the desirability of world states.
 Action-value information indicating the desirability of particular actions in particular
states.
 Goals that describe classes of states whose achievement maximizes the agent's utility.
23. What are the types of learning
 Supervised learning
 Unsupervised learning
 Reinforcement learning

20. 24. What are the approaches for learning logical sentences.
The two approaches to learning logical sentences:
 Decision tree methods, which use a restricted representation of logical sentences
specifically designed for learning,
 Version-space approach, which is more general but often rather inefficient.
25. What are decision trees.
A decision tree takes as input an object or situation described by a set of properties, and outputs
a yes/no "decision." Decision trees therefore represent Boolean functions.
Each internal node in the tree corresponds to a test of the value of one of the properties,
and the branches from the node are labelled with the possible values of the test. Each leaf node in
the tree specifies the Boolean value to be returned if that leaf is reached.
26. Give an example for decision tree logical sentence.
The path for a restaurant full of patrons, with an estimated wait of 10-30 minutes when the agent
is not hungry is expressed by the logical sentence
Vr Patrons(r,Full)f WaitEstimate(r,0-0) A Hungry(r,N) => WillWait(r)
27. What are Parity Function & Majority Function.
 Parity Function is a finction which returns 1 if and only if an even number of inputs are 1,
then an exponentially large decision tree will be needed.
 Majority function is one which returns 1 if more than half of its inputs are 1.
28. Draw an example decisiontree

21. 29. Explain the terms Positive example, negative example and training set.
An example is described by the values of the attributes and the value of the goal
predicate. We call the value of the goal predicate the classification of the example. If the
goal predicate is true for some example, we call it a ositive example; otherwise we call it
a negative example. A set of examples X,... ,X2 for the restaurant domain. The positive
examples are ones where the goal WillWait is true (X,Xi,,...) and negative examples are
ones where it is false (X2,X5,...). The complete set of examples is called the training set.
30. Draw an example of training set.
31. Explain the methodologyused for accessing the performance of learning
algorithm.
 Collect a large set of examples.
 Divide it into two disjoint sets: the training set and the test set.
 Use the learning algorithm with the training set as examples to generate a
hypothesis H.
 Measure the percentage of examples in the test set that are correctlyclassified by
H.
 Repeat steps 1 to 4 for different sizes of training sets and different randomly
selected
 training sets of each size.

22. 32. What is Overfighting.
Whenever there is a large set of possible hypotheses, one has to be careful not to use the
resulting freedom to find meaningless "regularity" in the data. This problem is called
overfitting. It is a very general phenomenon, and occurs even when the target function is
not at all random. It afflicts every kind of learning algorithm, not just decision trees.
33. What is Decisiontree pruning
Pruning works by preventing recursive splitting on attributes that are not clearly relevant,
even when the data at that node in the tree is not uniformly classified.
34. What is Pruning.
The probability that the attribute is really irrelevant can be calculated with the help of
standard statistical software. This is called pruning.
35. What is Cross Validation.
Cross-validation is another technique that eliminates the dangers of overfitting. The
basic idea of cross-validation is to try to estimate how well the current hypothesis will
predict unseen data. This is done by setting aside some fraction of the known data, and
using it to test the prediction performance of a hypothesis induced from the rest of the
known data.
36. What is Missing Data
In many domains, not all the attribute values will be known for every example. The
values may not have been recorded, or they may be too expensive to obtain. This gives
rise to two problems. First, given a complete decision tree, how should one classify an
object that is missing one of the test attributes? Second, how should one modify the
information gain formula when some examples have unknown values for the attribute?
37.What are multivalued attributes
When an attribute has a large number of possible values, the information gain measure
gives an inappropriate indication of the attribute's usefulness. Consider the extreme case
where every example has a different value for the attribute—for instance, if we were to
use an attribute RestaurantName in the restaurant domain. In such a case, each subset of
examples is a singleton and therefore has a unique classification, so the information gain
measure would have its highest value for this attribute. However, the GAIN RATIO
attribute may be irrelevant or useless.
38.What is continuous valued attribute
Attributes such as Height and Weight have a large or infinite set of possible values. They
are therefore not well-suited for decision-tree learning in raw form. An obvious way to
deal with this problem is to discretize the attribute. For example, the Price attribute for
restaurants was discretized into $, $$, and $$$ values. Normally, such discrete ranges
would be defined by hand. A better approach is to preprocess the raw attribute values

23. 40.What are versionspace methods.
version space methods are probably not practical in most real-world learning problems,
mainly because of noise, they provide a good deal of insight into the logical structure of
hypothesis space.
41. what is PAC learning?
Any hypothesis that is seriouslywrongwill almost certainlybe "found out" with high
probabilityafter a small number of examples, because it will make an incorrect prediction.
Thus, any hypothesis that is consistent with a sufficiently large set of training examples is
unlikely to be seriouslywrong—that is, it must be Probably ApproximatelyCorrect. PAC-
learning is the subfieldof computational learningtheorythat is devoted to this idea.
42.Difference betweenlearning andperformance agent?
Learning agents can be divided conceptually into a performance element, which is
responsiblefor selecting actions, and a learning element, which is responsible for
modifying the performance element.
43. Give a function for decisionlist learning?
function DECisiON-LiST-LEARNiNG(e.ra;wp/<?.v) returns a decisionlist, No or failure
ifexamples is empty thenreturn the value No t — a test that matches a nonempty subset
examples, of examples
such that the members of examples, are all positive or all negative if there is no such t then
return failure
if the examples in examples, are positive then o <— Yes
else o — No
return a decision list with initial test / and outcome o
and remaining elements given by DEClsiON-LlST-LEARNING
44.What is decisionlist?
A decisionlist is alogical expressionof arestrictedform. It consists of aseries of tests, each
of which is a conjunction of literals. If a test succeeds when applied to an example
description,the decision list specifies the value to be returned. If the test fails,
processing continues with the next test inthe list.

24. UNIT-V
1. Define expert system.
An expert system is a computer program that simulates the thought process of a human expert to
solve complex decision problems in a specific domain.
An expert system is an interactive computer-based decision tool that uses both facts and
heuristics to solve difficult decision problems based on knowledge acquired from an expert.
2. What are applications of expert systems?
• Interpreting and identifying
• Predicting
• Diagnosing
• Designing
• Planning
• Monitoring
• Debugging and testing
• Instructing and training
• Controlling
3. What are the distinguishing characteristics of programming languages needed for expert
systems work?
• Efficient mix of integer and real variables
• Good memory-management procedures
• Extensive data-manipulation routines
• Incremental compilation
• Tagged memory architecture
• Optimization of the systems environment
• Efficient search procedures
4. How are expert systems are organized?
1. Knowledge base consists of problem-solving rules, procedures, and intrinsic data relevant to
the problem domain.
2. Working memory refers to task-specific data for the problem under consideration.
3. Inference engine is a generic control mechanism that applies the axiomatic knowledge in the
knowledge base to the task-specific data to arrive at some solution or conclusion.
5. What are the Needs for Expert Systems?
1. Human expertise is very scarce.
2. Humans get tired from physical or mental workload.
3. Humans forget crucial details of a problem.
4. Humans are inconsistent in their day-to-day decisions.
5. Humans have limited working memory.
6. Humans are unable to comprehend large amounts of data quickly.
7. Humans are unable to retain large amounts of data in memory.
8. Humans are slow in recalling information stored in memory.
9. Humans are subject to deliberate or inadvertent bias in their actions.
10. Humans can deliberately avoid decision responsibilities.

25. 11. Humans lie, hide, and die.
6. What are the effectiveness in implementing human-like decision processes
1. Are algorithmic in nature and depend only on raw machine power
2. Depend on facts that may be difficult to obtain
3. Do not make use of the effective heuristic approaches used by human experts
4. Are not easily adaptable to changing problem environments
5. Seek explicit and factual solutions that may not be possible
7. What are the Benefits of Expert Systems?
1. Increase the probability, frequency, and consistency of making good decisions
2. Help distribute human expertise
3. Facilitate real-time, low-cost expert-level decisions by the nonexpert
4. Enhance the utilization of most of the available data
5. Permit objectivity by weighing evidence without bias and without regard for the user’s
personal and emotional reactions
6. Permit dynamism through modularity of structure
7. Free up the mind and time of the human expert to enable him or her to concentrate on more
creative activities
8. Encourage investigations into the subtle areas of a problem
8. Write short notes on HEURISTIC REASONING?
Human experts use a type of problem-solving technique called heuristic reasoning. Commonly
called rules of thumb or expert heuristics, it allows the expert to arrive at a good solution quickly
and efficiently. Expert systems base their reasoning process on symbolic manipulation and
heuristic inference procedures that closely match the human thinking process. Conventional
programs can only recognize numeric or alphabetic strings and manipulate them only in a
preprogrammed manner.
9. Write short notes on Search Control Methods
All expert systems are searching intensive. Many techniques have been employed to make these
intensive searches more efficient. Branch and bound, pruning, depth-first search, and breadth-
first search are some of the search techniques that have been explored. Because of the intensity
of the search process, it is important that good search control strategies be used in the expert
systems inference process.
10. Write short notes on Forward Chaining
This method involves checking the condition part of a rule to determine whether it is true or
false. If the condition is true, then the action part of the rule is also true. This procedure
continues until a solution is found or a dead end is reached. Forward chaining is commonly
referred to as data-driven reasoning.
11. Write short notes on Backward Chaining
Backward chaining is the reverse of forward chaining. It is used to backtrack from a goal to the
paths that lead to the goal. Backward chaining is very good when all outcomes are known and
the number of possible outcomes is not large. In this case, a goal is specified and the expert

26. system tries to determine what conditions are needed to arrive at the specified goal. Backward
chaining is thus also called goal-driven.
12. Write short notes on Data Uncertainties
Expert systems are capable of working with inexact data. An expert system allows the user to
assign probabilities, certainty factors, or confidence levels to any or all input data. This feature
closely represents how most problems are handled in the real world. An expert system can take
all relevant factors into account and make a recommendation based on thebest possible solution
rather than the only exact solution