Download CS2351 ARTIFICIAL INTELLIGENCE Unit – Wise Important

CS2351 ARTIFICIAL INTELLIGENCE
Unit – Wise Important Questions
UNIT - I
1. Define AI and Explain in detail about the 4 categories of AI. (8 marks)
Explain:a. Acting Humanly – Turing Test Approach
i. Natural Language processing
ii.Knowledge representation
iii.
Automated Reasoning
iv.
Machine Learning
b. Thinking Humanly – Cognitive Modeling Approach
c.
Thinking Rationally – Laws of thought Approach
d. Acting Rationally – The rational Agent approach
2. What are Agents? What are the types of agents? Explain in detail about Agent function and
Agent program with an example of Vacuum World Problem(8marks).
a. Definition
b. Types
i. Human Agent
ii.Robotic Agent
iii.
Software Agent
iv.
Software Agent
c. Example – Vacuum world Problem with diagram.
3. Explain in detail the foundations of Artificial Intelligence (12)
a. Introduction
b. Philosophy (428 B.C – present)
c. Mathematics (800 – present)
d. Economics (1776 – present)
e. Neuroscience (1861 – present)
f.
Psychology (1879 – present)
g. Computer Engineering (1940 – present)
h. Control Theory and Cybernetics (1948 – present)
i.
Linguistics (1957 – present)
4. Explain in detail the history of Artificial Intelligence (12)
a. Introduction
b. Gestation of Artificial Intelligence (1943 - 1955)
c. The birth of Artificial Intelligence (1956)
d. Early Enthusiasm, great expectations (1952 -1969)
e. A Dose of Reality (1966 – 1973)
f.
Knowledge Based System – Key to power? (1969-1979)
g. AI becomes an Industry (1980 – present)
h. The return of neural Networks (1986-present)
i.
AI becomes a science (1987 – present)
j.
Emergence of Intelligent Agents (1995 – present)
5. What is the Structure of an Intelligent Agent, Explain in detail about the 4 types of AGENT
PROGRAMS, with neat diagram.(16 marks)
a. Intelligent Agent = Architecture +program
b. Agent Program
c. Agent Function
d. Skeleton Agent
e. Table Driven Agent
f.
Types of Agent Programs
i. Simple Reflex Agent
ii.Model Based Agent
iii.
Goal Based Agent
iv.
Utility Based Agent
6. Applications of AI (4 marks)
a. Autonomous planning and scheduling
b. Game playing
c. Autonomous Control
d. Diagnosis
e. Logistics planning
f.
Robotics
g. Language understanding and problem solving
7. Explain in detail the steps involved in Problem Formulation with an example, and give the
Algorithm for Problem solving Agents (8 marks) (Example problems can be a. Route finding Problem
b. Vacuum World Problem
c. 8 puzzle problem
d. 8 Queens problem
e. TSP problem
f.
VLSI layout problem )
Explain:a. Initial State
b. Successor Function
c. Goal Test
d. Path Cost
(Note: - Explain the above steps for the given example)
8. Differentiate uninformed/Blind and Informed Search strategy & Explain in detail about
Uninformed Search strategies with example problems(16 marks)
a. Difference between Uninformed and Informed
b. Breadth First Search
c. Uniform cost Search
d. Depth First Search
e. Depth limit Search
f.
Iterative Deepening Depth limit Search
g. Bi directional Search
(Note :- Write Definition, example, Space, time Complexity, Optimality, Advantage Disadvantage of
each search)
9. Explain in detail about Informed/Heuristic Search strategies(16 marks)
a. Greedy best first search
b. A* search
c. Memory bounded heuristic search – RBFS(with algorithm)
i. IDA *
ii.SMA * (also difference of IDA* and SMA*)
10. Compare and Contrast Uninformed Search and Informed search strategies.(8 marks)
(Draw the table that compares the completeness, time & space complexity, optimality of both the
searches & Each search under informed search strategy can be asked separately for 8 marks)
11. Explain in detail about RBFS, give its algorithm and an example (8 marks)
i. Definition
ii.Algorithm
iii.
Example
iv.
Explanation
12. Differentiate IDA * and SMA* (8 marks)
Refer Notes
13. Explain in detail about CONSTRAINT SATISFACTION PROBLEM (16 marks)
a. Definition
b. Constraint graph
c. Problem formulation steps
d. Backtracking search for CSP
e. Constraint Propagation
f.
Forward Checking
g. AC – 3 Algorithm
h. Handling Special constraint
i.
Backward Checking
14. Explain in detail the local search problems (12 marks)
a. Hill Climbing Search +Algorithm
i. Local maxima
ii.Ridges
iii.
Plateau
b. Simulated Annealing Search +Algorithm
c. Local Beam Search
d. Genetic Algorithm +Algorithm
UNIT - II
1. Define KB, Explain in detail about knowledge based agent with algorithm (4 marks)
a. Definition
b. KB- Agent Algorithm
2. Explain in detail about the wumpus world problem with necessary steps and diagrams
(8 marks)
a. PEAS Description
b. Diagram
c. Steps involved
d. Explanation
3. Give an overview of logics and its types(8 marks)
a. Introduction
b. Syntax
c. Semantics
d. Entailment
e. Logical Inference
f.
Sound
g. Truth preserving
h. Completeness
4. Explain in detail about propositional or Boolean logics with syntax and semantics (8
marks)
a. Overview of Syntax , semantics
b. Algorithm of TT- Entails, TT-Check
c. Brief about resolution + algorithm
d. Wumpus World explanation using Propositional Logic rules
5. Explain in detail about First order logic or predicate logic with necessary examples(8)
a. Models for First Order logics
b. Richard the lion heart example & explanation
c. Symbols, rules
d. Atomic, Complex Sentences
e. Quantifiers, Types
f.
Assertions and Queries in FOL
g. Example – Wumpus World problem using FOL
6. Describe the Axioms of Kinship domain and explain them in detail (8)
7. Give the axioms of SETs with explanation (8)
8. Explain in detail the steps involved in knowledge engineering with an example of
electronics circuit domain(12)
a. Identify the task
b. Assemble the relevant knowledge
c. Decide on a vocabulary of predicates, functions and constants.
d. Encode general knowledge about domain
e. Encode a description of the specific problem instance
f.
Pose queries to the inference procedure and get answers
g. Debug the knowledge base
(Explain Electronic Circuit Domain as an example for this question. Give diagram and
explanation based on above steps.)
9. Explain in detail about Inferences in first order logics (16)
a. Inference Rules for Quantifiers
i. Universal Instantiation
ii.Existential Instantiation
iii.
Skolem constant
b. Overview of Unification and Lifting + Algorithm
c.
Storage and Retrieval
d. Definition of Forward Chaining and Backward Chaining
e.
10.
Resolution.
Give the algorithm for Forward and Backward chaining and explain with an
example(16)
a. Forward Chaining Definition
b. First order definite clauses
c. A simple Forward Chaining Algorithm
d. Efficient Forward Chaining
e. Incremental FC
f.
Diagram and Missiles Problem Explanation
g. Backward Chaining Definition
h. Algorithm + Diagram
i.
Logic Programming
j.
Implementation of logic program
k. Constraint logic programming.
11.
Explain in detail about the concepts involved in logic programming. (8)
a. Algorithm=Logic+Control
b. Aspects of Prolog
c. Efficient
implementation
of
logic
Programs,
APPEND
Procedure
d. Redundant Inference and infinite loops
e. Constraint Logic Programming
12.
Give the algorithm for UNIFICATION and explain the concepts involved in it (16)
a. Unification Definition
b. Firs order inference rule.
c. Unification algorithm
d. Storage and Retrieval.
13.
Explain the concepts involved in Resolution. (16)
a. Resolution Definition
b. CNF and Steps involved
c. Resolution inference rules
d. Completeness of resolution
e. Resolution strategies
14.
Given a paragraph or set of sentences, each and every sentences should be converted
to predicate calculus
UNIT – III
1. Explain in detail about Planning with state space search(12)
a. Planning Definition
b. Forward State space Search
c. Backward state space search
d. Heuristics for state space search
2. Describe in detail about Partial order Planning with an example (16)
b. Definition
c. Four components of plan
d. Problem formulation
e. Partial order Plan example (Flat tire, Spare tire) also diagrams.
f.
Heuristics for POP
3. Give the algorithm of planning graphs and explain in detail the concepts involved (16)
a. Definition
b. Have cake and eat cake too
c. planning graph for above example
d.
estimation
Planning
graphs
for
heuristic
e. GRAPH PLAN Algorithm
f. Termination of GRAPH PLAN.
4. Explain in detail about planning and acting real world (16)
a. Definition
b. Job Shop Scheduling
c. Critical path method
d. Scheduling with resource constraints.
e. HTN Overview.
f. Planning and acting in non deterministic Domains (types)
g. conditional Planning
5. Describe in detail about Job shop scheduling and critical path method (12)
Write Definition and Algorithm of both the methods and explain in detail
about the example for each method.
6. Explain in detail about Hierarchical task network planning (HTN) (10)
a. Definition
b. Representing action Decomposition
c. Modifying the planner for Decomposition
d. Discussions.
7. Explain in detail about Planning and Acting in Non deterministic Domains.
a. Definition
b. Bounded Indeterminacy
c. planning methods for Handling Indeterminacy
i) Sensor less Planning
ii) Conditional planning
iii) Execution monitoring and
replanning
iv) Continuous Planning
UNIT - IV
1. What is Uncertainty and How will you handle Uncertain Knowledge (8)
Definition
Dental diagnosis System + reason for failure
Uncertainty and rational decision
Design for decision theoretic agent +algorithm
2. Give the Design for Decision theoretic agent (4)
a. Definition
b. algorithm
3. Explain in detail the concepts involved in Review of Probability (12)
a.
4. Describe in detail about constructing Bayesian Networks and represent the Full joint
probability distribution (8)
a. Definition
b. Burglary alarm system
c. Bayesian network formula derivation ( semantics of Bayesian
network)
5. Explain in detail about Inferences in Bayesian Networks (16)
a. BAYESIAN NETWORKS definition
b. Overview of burglary alarm system+diagram
c. BAYESIAN NETWORKS formula alone
d. Exact inference in BAYESIAN NETWORKS
i. Inference by Enumeration
ii.Variable Elimination Algorithm
iii.
Complexity of Exact inference
iv.
Clustering Algorithm
e. Approximate Inference in BAYESIAN NETWORKS
i. Direct Sampling Method
ii.Rejection Sampling in Bayesian Networks
6.
iii.
Likelihood weighting
iv.
Inference by Markov Chain simulation
Explain in detail about Approximate inference in Bayesian Network. (8)
a. Approximate Inference in BAYESIAN NETWORKS
i. Direct Sampling Method
ii.Rejection Sampling in Bayesian Networks
iii.
Likelihood weighting
iv.
Inference by Markov Chain simulation
7. Describe in detail about Inference by Markov chain Simulation (8)
a. The MCMC Algorithm
b. Why MCMC works
8. Explain in detail about Temporal Models (16)
a. Filtering or Monitoring
b. Prediction
c. Smoothing or Hindsight
d. Most likely explanation
9. Explain in detail about Hidden Markov Models (16)
a. Simplified Matrix algorithm
10. Explain in detail about Inference by enumeration and Variable Elimination Algorithm (12)
Refer Q.No 5
UNIT - V
1.
Explain in Detail about the concept of Learning From Observation (8)
a. 3 major design issues
i. Components
ii.Feedback
iii.
Representation
b. Supervised learning
c. Unsupervised learning
Overview
d. Reinforcement Learning
2.
What is Ensemble learning? Explain in detail about ADA BOOST algorithm. (16)
a. Definition of Ensemble learning
b. Boosting
c. Weighted Training set
d. Weak learning
e. Decision Stumps
Overview
f.
3.
ADA Boost Algorithm + Explanation.
Describe in detail about Inductive learning(12)
a. Definition
b. Pure Inductive Inference
c. Hypothesis
d. Problem of Induction
e. Hypothesis Space
f.
Definition+Expalnation
Consistent
g. Ockhams Razor
h. Realizable
i.
4.
Unrealizable
Briefly Explain the concepts involved in Decision tree algorithm (16)
a. Decision Tree as Performance elements
i. Decision Tree
ii.Classification
iii.
Regression
b. Example of Wait for a table
c. Expressiveness of Decision Trees
i. Parity function
ii.Majority Function
d. Inducing Decision Trees for Example(brief overview)
i. Training Set
e. Decision Tree Learning algorithm
f.
Choosing Attribute Tests
g. Assessing the performance of the learning algorithm(5 points)
h. Noise and over fitting
i. Definition of Over fitting, decision tree pruning
i.
Broadening Applicability of Decision trees
i. Missing data
ii.Multivalued attribute
5.
iii.
Continuous and integer valued input attributes
iv.
Continuous and output attributes
What is meant by Explanation Based Learning - EBL? Explain in detail about it. (16)
a. Definition/Overview
b. Memoization Technique
c. Extracting general rules from example
d. EBL process working steps(4 points)
e. Improving efficiency – overview
6.
Give an overview of statistical learning (16)
a. Definition/overview
b. Learning with complete data
i. Max Likelihood parameter learning- discrete models
ii.Max Likelihood parameter learning- continuous models
definitions
iii.
Bayesian parameter Learning
iv.
Learning bayes net Structure
c. Learning with Hidden variables – EM Algorithm (Separate 8
mark)
i. Unsupervised Clustering
ii.Learning Bayesian Networks with hidden variables
Overview
iii.
Learning hidden Markov models
iv.
General form of EM algorithm
7. Explain in Detail about Instance Based Learning (12 Marks)
a. Definition
b. Nearest Neighbor Models
Overview
c. Kernel Models
8.
Explain in detail about Reinforcement learning (16)
a. Definition
i. Utility Based agent
ii.Q – Learning Agent
iii.
Reflex Agent
b. Passive Reinforcement Learning
i. Direct Utility Estimation
ii.Adaptive Dynamic Programming
iii.
Temporal difference Learning
c. Active Reinforcement Learning
i. Exploration
ii.Learning an Action Value Function
d. Generalization in Reinforcement Learning
i. Application to Game playing
ii.Application to Robot control
e. Policy Search
9.
Explain in detail about Decision List (8)
a. Decision List Overview
b. Diagram
c. Decision list Learning Algorithm
d. Discussion
10.
Explain in detail about neural networks and the concepts behind it (16)
a. Definition
b. Units in neural Networks
c. Network structures
d. Single layer feed Forward Neural Network
e. Multi Layer Feed Forward Neural Network
f.
11.
Learning Neural Network Structure
Explain in detail about Naïve bayes model and concept of Bayes Net.
a. Refer Learning With Complete data under Statistical Learning