CS2351
ARTIFICIAL INTELLIGENCE
PART A
TWO MARKS UNIT – I
1)
1.
What is AI?
The
study of how to make computers do things at which at the moment people are
better.
|
Systems
that think like humans
|
Systems
that think rationally
|
|
Systems
that act like humans
|
Systems
that act rationally
|
2)
Define
an agent.
An agent is anything that can be viewed as perceiving its environment
through sensors
and acting
upon that environment through actuators.
3)
What is
an agent function?
An
agent’s behavior is described by the agent
function that maps any given percept
sequence to an action.
4)
Differentiate
an agent function and an agent program.
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Agent Function
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Agent Program
|
|
An abstract mathematical description
|
A concrete implementation, running on the agent
Architecture.
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5)
What
can Ai do today?
6)
What is
a task environment? How it is specified?
Task environments are essentially the
"problems" to which rational agents are the "solutions." A
Task environment is specified using PEAS (Performance, Environment, Actuators, Sensors) description.
Give an example of PEAS description for an
automated taxi.
7)
Give
PEAS description for different agent types.
8)
List
the properties of task environments.
Ø
Fully observable vs. partially observable.
Ø
Deterministic vs. stochastic.
Ø Episodic vs. sequential
Ø
Static vs, dynamic.
Ø
Discrete vs. continuous.
Ø
Single agent vs. multiagent
9)
Write a
function for the table driven agent
10) What
are the four different kinds of agent programs?
Simple reflex agents;
Model-based reflex agents;
Goal-based agents; and
Utility-based agents.
11) Explain a simple reflex agent with a
diagram.
Simple reflex agents
The simplest kind of agent is the simple
reflex agent. These agents select actions on the basis AGENT
of the current percept,
ignoring the rest of the percept history.
12) Explain with a diagram the model based
reflex agent.
13a)
Explain with a diagram the goal based reflex agent.
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, turn right, or go straight on. The
correct 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
that describes situations that are desirable-for example, being at the
passenger's destination.
13b)
What are utility based agents?
Goals alone are not really
enough to generate high-quality behavior in most environments. 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. A utility function maps a state (or a sequence of
states) onto a real number, which describes the associated degree of happiness.
13c)
What are learning agents?
A
learning agent can be divided into four conceptual components, as shown in Fig-
2.15. The most important distinction is
between the learning element, which is re-ELEMENT sponsible for making
improvements, and the performance element, which 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. The
learning element uses CRITIC feedback from the critic on how the agent is doing
and determines how the performance element should be modified to do better in
the future. 
13) Define the problem solving agent.
A Problem solving agent is a goal-based agent . It decide what to do
by finding sequence of actions that lead to desirable states. The agent can
adopt a goal and aim at satisfying it. Goal
formulation is the first step in problem solving.
14) Define the terms goal formulation and
problem formulation.
Goal formulation,based
on the current situation and the agent’s performance measure,is the first step
in problem solving. The agent’s task is to find out which sequence of actions
will get to a goal state.
Problem
formulation
is the process of deciding what actions and states to consider given a goal
15) List the steps involved in simple problem
solving agent.
(i)
Goal
formulation
(ii)
Problem
formulation
(iii)
Search
(iv)
Search
Algorithm
(v)
Execution
phase
16) Define search and search algorithm.
The process of looking for
sequences actions from the current state to reach the goal state is called search.
The search algorithm takes a problem as input and returns a solution
in the form of action sequence. Once
a solution is found, the execution phase
consists of carrying out the recommended action..
17) What are the components of well-defined
problems?
o
The
initial state that the agent starts
in . The initial state for our agent of example problem is described by In(Arad)
o
A
Successor Function returns the possible actions available to the agent. Given a state x,SUCCESSOR-FN(x)
returns a set of {action,successor} ordered pairs where each action is one of
the legal actions in state x,and each successor is a state that can be reached
from x by applying the action.
For example,from the state In(Arad),the
successor function for the Romania
problem would return
{
[Go(Sibiu),In(Sibiu)],[Go(Timisoara),In(Timisoara)],[Go(Zerind),In(Zerind)] }
o
Thr
goal test determines whether the
given state is a goal state.
o
A
path cost function assigns numeric
cost to each action. For the Romania problem the cost of path might be its
length in kilometers.
18) Differentiate toy problems and real world
problems.
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TOY
PROBLEMS
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REAL
WORLD PROBLEMS
|
|
A toy problem is intended to illustrate various problem solving
methods. It can be easily used by different
researchers to compare the performance of algorithms.
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A real world problem is one whose solutions people actually care
about.
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19) Give examples of real world problems.
(i)
Touring
problems
(ii)
Travelling
Salesperson Problem(TSP)
(iii)
VLSI
layout
(iv)
Robot
navigation
(v)
Automatic
assembly sequencing
(vi)
Internet
searching
20) List the criteria to measure the
performance of different search strategies.
o
Completeness : Is
the algorithm guaranteed to find a solution when there is one?
o
Optimality : Does
the strategy find the optimal solution?
o
Time
complexity
: How long does it take to find a solution?
o
Space
complexity
: How much memory is needed to perform the search?
21) Differentiate Uninformed Search(Blind
search) and Informed Search(Heuristic Search) strategies.
|
Uninformed
or Blind Search
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Informed
or Heuristic Search
|
|
|
22) Define Best-first-search.
Best-first
search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in
which a node is selected for expansion based on the evaluation function f(n ).
Traditionally, the node with the lowest
evaluation function is selected for expansion
23) Define Artificial Intelligence formulated
by Haugeland.
The
exciting new effort to make computers think machines with minds in the full and
literal sense.
24) Define Artificial Intelligence in terms of
human performance.
The art of creating
machines that perform functions that require intelligence when performed by
people.
25) 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.
26) . Define Artificial in terms of rational
thinking.
he 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.
27) . 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.
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 explorative pattern.
28) What capabilities computer should poses to
behave humanly or to pass turing test approach?
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 explorative pattern.
29) 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.
30) Define an Omniscient agent.
An omniscient agent knows the
actual outcome of its action and can act accordingly; but omniscience is
impossible in reality.
31) 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.
32) 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.
33) Define an agent program.
Agent program is a function
that implements the agents mapping from percept to actions.
34) Define Architecture.
The action program will run on
some sort of computing device which is called as Architecture.
35) 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
36) 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 the 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.
37) What
are the phases involved in designing a problem solving agent?
The three phases are: Problem
formulation, Search solution, Execution.
38) What
are the different types of problem?
Single state problem, Multiple
state problem, Contingency problem, Exploration problem.
39) Define problem.
problem is really a collection of information
that the agent will use to decide what to do.
40) List
the basic elements that are to be include in problem definition.
Initial state, operator,
successor function, state space, path, goal test, path cost.
41) Mention the criteria for the evaluation of
search strategy.
There are 4 criteria:
Completeness, time complexity, space complexity, optimality.
42) Define autonomy.
If an agent relies on the prior
knowledge of its designer rather than on its own percepts, we say that the
agent lacks autonomy.
43) . 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.
44) List
the various search strategies.
a. BFS
b. Uniform
cost search
c. DFS
d. Depth
limited search
e. Iterative
deepening search
f. Bidirectional
search
45) 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
46) Differentiate BFS & DFS.
BFS means breath wise search DFS means depth wise search
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
47) Whether uniform cost search is optimal?
Uniform cost search is optimal
& it chooses the best solution depending on the path cost.
48) 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)
49) 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.
50) What is Manhattan distance h2?
The sum of horizontal and
vertical distances of the tiles from their goal positions in a 15 puzzle
problem is called Manhattan distance or city block distance.
51) 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.
52) What are the types of constraints?
(i)
Unary
constraints relates to one variable
(ii)
Binary
constraints relates 2 variable
(iii)
Higher
order constraints relates more than 2 variables
(iv)
Absolute
Constraints
(v)
Preference
Constraints
53) Define MRV
Minimum Remaining Value
heuristic chooses the variable with the fewest legal values.
54) Define LCV.
Least constraining value
heuristic prefers the value that rules out the fewest choices for the
neighboring variables in the constraint graph.
55) Define Constraint Propagation.
Is propagating the implication
of a constraints on one variable on to the other variables as done by forward
checking and then on to the constraints
to detect the inconsistency.
56) 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.
57) 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.
58) 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.
59) 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
60) What
is Simulated Annealing?
Simulated Annealing (SA) is motivated by an analogy to annealing in
solids. The idea of SA comes from a paper published by Metropolis etc al in
1953 [Metropolis, 1953). The algorithm in this paper simulated the cooling of
material in a heat bath. This is a process known as annealing.
If you heat a solid past melting point and then cool it, the structural
properties of the solid depend on the rate of cooling. If the liquid is cooled
slowly enough, large crystals will be formed. However, if the liquid is cooled
quickly (quenched) the crystals will contain imperfections.
Metropolis’s algorithm simulated the material as a system of particles.
The algorithm simulates the cooling process by gradually lowering the
temperature of the system until it converges to a steady, frozen state.
61) 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.
62) What
is the advantage of memory bounded search techniques?
We can reduce space
requirements of A* with memory bounded alg such as IDA* & SMA*.
63) 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.
64) 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.
65) 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
1. What
are Logical agents?
Logical
agents apply inference to a knowledge base to derive new information and make
decisions.
2.What
is first-order logic?
The
first-order logic is sufficiently expressive to represent a good deal of our
common sense knowledge. It also either subsumes or forms the foundation of many
other representation languages.
3. What
is a symbol?
The
basic syntactic elements of first-order logic are the symbols. It stands for objects,
relations and functions.
4. What
are the types of Quantifiers?
The
types of Quantifiers are,
·
Universal
Quantifiers;
·
Existential
Quantifiers.
5.
What are the three kinds of symbols?
The
three kinds of symbols are,
·
Constant
symbols standing for objects;
·
Predicate
symbols standing for relations;
·
Function
symbols standing for functions.
6. What
is Logic?
Logic
is one which consist of
·
A
formal system for describing states of affairs, consisting of a)Syntax b)
Semantics;
·
Proof
Theory – a set of rules for deducing the entailment of set sentences.
7.
Define a Sentence?
Each
individual representation of facts is called a sentence. The sentences are expressed
in a language called as knowledge representation language.
8.
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.
9.
Define Interpretation
Interpretation
specifies exactly which objects, relations and functions are referred to by the
constant predicate, and function symbols.
10.
What are the three levels in describing knowledge based agent?
The
three levels in describing knowledge based agent
·
Logical
level;
·
Implementation
level;
·
Knowledge
level or epistemological level.
11.
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.
12.
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
within an agent, the agent believes the corresponding sentence.
13.
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.
14.
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.
15.
Define an inference procedure.
An
inference procedure reports whether or not a sentence is entitled 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 basic Components of propositional logic?
The
basic Components of propositional logic
·
Logical
Constants (True, False)
·
Propositional
symbols (P, Q)
·
Logical
Connectives (^,=,⋁,⌐)
17.
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.
α1^ α2^
α3^…………………. αn
αi
18.
Define AND-Introduction rule in propositional logic.
AND-Introduction
rule states that from a list of sentences we can infer their
conjunctions.
α1, α2,
α3, …………………. αn
_ α1^ α2^ α3^………………….
αn
19.
What is forward chaining?
A
deduction to reach a conclusion from a set of antecedents is called forward chaining.
In other words, the system starts from a set of facts, and a set of rules, and
tries to find the way of using these rules and facts to deduce a conclusion or
come up with a suitable course
of
action.
20.
What is backward chaining?
In
backward chaining, we start from a conclusion, which is the hypothesis we wish to
prove, and we aim to show how that conclusion can be reached from the rules and
facts in the data base.
21. What is first order logic?
First
order logic is sufficiently expressive to represent a good deal of our
commonsense knowledge. It also either subsumes or forms the foundation of many
other representation languages and has been studied intensively for many
decades.
22. Define compositionality?
In
a compositionality language, the meaning of a sentence is a function of the
meaning of its parts. For example, “S1,4 ^ S1.2” is related to the meaning of
“S1.4” and S1.2”.
23. Define objects?
The
elements of nouns and noun phrases refer to objects. Example people, houses and
etc.
24. Define relations.
The
elements of verbs and verb phrases refer relations. For example properties such
as red, round, prime.
25. Define functions.
The
relation in which there is only one “value” for a given “input” is called
functions.
26. Define total function.
There
must be value for every input for every input tuple.
27. Define symbols in FOL.
The
basic syntactic elements of first-order logic are the symbols that stand for
objects, relations, and functions.
28. What are the different kinds of
symbols?
i.
Constant symbols: for objects.
ii.
Predicate symbols: for relations.
iii.
Function symbols: for functions.
29. Define semantics in FOL.
The
semantics must relate sentences to models in order to determine truth.
30. Define interpretation.
It
specifies exactly which objects, relations and functions are referred to by the
constant, predicate, and function.
31. What is mean by term?
A
term is a logical expression that refers to an object. Ex: constant symbols.
32. Define tuples.
A
tuples is a collection of objects arranged in a fixed order and is written with
angle brackets surrounding the objects.
33.What is atomic sentence?
An
atomic sentence is true in a given model, under a given interpretation, if the
relation referred to by the predicate symbol holds among the objects referred
to by the algorithm.
34. Define qualifiers.
Expressions
the properties of entire collections of objects, instead of enumerating the
object by the name are called qualifiers.First order logic contains two
standard qualifiers called universal and existential.
35. Define the following.
Variable: The symbols are called variables.
Ground team: a term with no variables is called
a ground term.
36. Define Diagnostic rules.
Diagnostics
rules lead from observed effects to hidden causes.
37. Define causal rules.
Causal
rules reflect the assumed direction of causality in the world.
38.Define knowledge Engineering.
The general of process of
constructing a knowledge base. A knowledge engineer is someone who investigates
a particular domain, learns what concepts are important in that domain, and
creates a formal representation of the objects and relations in the domain.
39. What are the steps in knowledge
engineering process?
i. Identify the task
ii. Assemble the relevant knowledge
iii. Decide on a vocabulary of predicates,
functions and constants
iv. Encode general knowledge about the domain
v. Encode a description of the specific problem
instance
vi. Pose queries to the inference procedure and
get and get answers
vii. Debug the knowledge base
40. What are the rules to be
followed for universal insanitation?
The
rule of universal insanitation says that we can infer any sentence obtained by
substituting a ground team for variables. To write out the inference rule
formally, we use the notation of substitutions.
Let SUBSET (ø, α) denote the result of applying the substitution ø to
the sentence α. Then the rule is written as
"v α
SUBST ({v/g}, α) for any variable v and ground term g.
41. Define Existential Instantiation.
For any sentence α, variable v, and constant symbol k that does
not appear elsewhere in the knowledge base,
$v α
SUBST ({v/k}, α)
42. Define skolem constant.
Suppose
we discover that there is a number that is a little bigger than 2.71828 and
satisfies the equation d(xy)/dy= xy for x. we can give this new number a name,
such as e, but it would be mistake to give it the name of an existing object,
such as π. In logic, the new name is called a skolem constant.
43.Define unification.
It
is a key component of all first-order inference algorithms. The unify algorithm
takes two sentences and returns a unifier for them if one exists:
UNIFY (p, q) = ø where SUBST (ø, p)
=SUBST (ø, q)
Ex: UNIFY (Knows (John, x), Knows
(John, Jane)) = {x/Jane}.
44. Define standardizing apart.
If
two different sentences happen to use the same variable name, then we
standardizing apart one of the two sentences being unified, which means
renaming its variables to avoid name clashes.
45. What do you mean by forward
chaining algorithm?
Starting
from the known facts, it triggers the entire rule whose premises are satisfied,
adding their conclusions to the known facts. The process repeats until the
query is answered or no new facts are called.
46.Define backward chaining
algorithm.
It
is called with a list of goals containing a single element, the original query,
and returns the set of all substitutions satisfying the query. The list of
goals can be thought of as a “stack” waiting to be worked on; if all of them
satisfied, then the current branch of the proofs succeed.
47. Define logic programming.
Logic
programming is a technology that comes fairly close to embodying the
declarative idea that systems should be constructed by expressing knowledge in
a formal language and that problems should be solved by running inference
processes on that knowledge.
48. Define prolog.
In prolog terms, there must be a finite
number of solutions for any goals with unbound variables.
49.What is constraint logic
programming?
Constraint
logic programming allows variables to be constrained rather than bound. A
solution to a constraint logic program is the most specific set of constraints
on the query variables that can be derived from the knowledge base.
50.What is conjunctive normal form?
CNF
is defined as a conjunction of clauses, where each clause is a disjunction of
literals. Literals can contain variables, which are assumed to be universally
quantified.
51. What is skolemization?
Skolemization
is the process of removing existential quantifiers by elimination.
52. Define identical and unifying
Propositional
factoring reduces two literals to one if they are identical. First-order
factoring reduces two literals to one if they are identical.
53. Define refutation complete.
Refutation-complete
mean that if a set of sentences is unsatisfiable, then resolution will always
be able to derive a contradiction.
54.What are the steps to be followed
while preparing a problem for OTTER?
·
A set of clauses known as ste of
support, which defines the important facts about the problem.
·
A set of usable axioms that are
outside the set of support.
·
A set of equations known as rewrites
or demodulators.
·
A set of parameters and clauses that
define the control strategy.
55.Define Syntax?
Syntax is the arrangement of words. Syntax of knowledge
describes the possible
configurations that can constitute sentences. Syntax of the language describes how to make sentences.
56.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.
57.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.
5 8. What is entailment?
The
relation between sentences is called entailment. The formal definition of entailment is this:
kb |= α if and
only if in every model in which
59. What is truth preserving
An
inference algorithm that derives only entailed sentences is called sound or truth preserving.
60. 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.
61. Define a Complete
inference procedure
An inference procedure is complete if it can derive all
true conditions from a set of premises.
62. Define
Interpretation
Interpretation specifies exactly which objects, relations
and functions are referred to by the constant predicate, and function
symbols.
63. 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 possible worlds.
64.Define Satisfiability
of a sentence
A sentence is satisfiable if and only if there is some
interpretation in some world for which it is true.
65.Define true
sentence
A sentence is true under a particular interpretation if
the state of affairs it
represents is the case.
66.What are the basic
Components of propositonal logic?
i. Logical Constants (True, False)
67. 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.
68. 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.
α1^ α2^
α3^…………………. αn
_ α1, α2,
α3, …………………. αn
70. Define
OR-Introduction rule in propositonal logic
αi
α1⋁ α2⋁ α3⋁ …………………. αn
OR-Introduction rule states that from, a sentence, we can
infer its disjunction with
anything.
UNIT –III
PLANNING
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?
The types of state-space search are,
¶
Forward state space search;
¶Backward state space search.
3.What is 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.
4. What are the advantages and disadvantages of
Partial-Order Planning?
¶
Advantage: Partial-order planning has a
clear advantage in being able to decompose problems into sub problems.
¶
Disadvantage: Disadvantage is that it
does not represent states directly, so it is harder to estimate how far a
partial-order plan is from achieving a goal.
5. What is a Planning graph?
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.
6. What is Conditional planning?
Conditional planning is 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
7. What is 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.
8. 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.
9. List the features of an ideal planner?
The features of an ideal planner are,
¶
The planner should be able to represent
the states, goals and actions;
¶
The planner should be able to add new
actions at any time;
¶
The planner should be able to use Divide
and Conquer method for solving very big problems.
10. What are the components that are needed for
representing an action?
The components that are needed for representing an
action are,
¶
Action description;
¶
Precondition;
¶
Effect.
11. What are the components that are needed for
representing a plan?
The components that are needed for representing a plan
are,
¶
A set of plans steps;
¶
A set of ordering constraints;
¶
A set of variable binding constraints;
¶
A set of casual link protection.
12. What are the different types of planning?
The different types of planning are,
¶
Situation space planning;
¶
Progressive planning;
¶
Regressive planning;
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