CS2351 Artificial Intelligence

Syllabus

Important 16 Mark Questions

CS2351 Artificial Intelligence Important 16 Mark Questions

Anna University

Department of Computer Science Engineering

Semester : 6

Department : CSE

Subject Code : CS2351

Subject Name : Artificial Intelligence

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Unit 1

1) Explain in detail about Agents and their types.

2) Explain in detail about uninformed search strategies with example.

3) Explain in detail about informed search strategies with example.

4) Explain in detail about Constraint satisfaction problem with example.

Unit 2

1. Explain in detail about forward & backward chaining algorithm with example.

2. Explain in detail about First order Logic & Inferences in First Order Logic with example.

3. Explain in detail about logical agents with example.

4. Explain in detail about Resolution & Resolution inference Rule with example.

5. Explain with at least 4 examples for PEAS cycle.

Unit 3

1. Explain about partial order planning with an example.

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

3. Explain about partial order planning algorithm.

4. Describe in detail about planning graphs.

5. Explain in detail about graph plan algorithm.

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

7. Explain about replanning agent algorithm.

Unit 4

1. Explain in detail about Bayesian networks with an example.

2. Explain in detail about conditional probability.

3. Explain in detail about Markov Process with example.

4. Explain in detail about dynamic Bayesian networks.

5. Explain in detail about hidden markov models with example.

6. Explain in detail about inference in Bayesian network.

Unit 5

1. Explain the learning decision tree with algorithm with example.

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

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

3. Discuss learning with hidden variables?

4. Explain all the statistical learning method with example.

5. Explain in detail about Reinforcement learning.

CS2351 Artificial Intelligence-Syllabus

CS2351 ARTIFICIAL INTELLIGENCE

 L T P C

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AIM:

To learn the basics of designing intelligent agents that can solve general purpose problems, represent and process knowledge, plan and act, reason under uncertainty and can learn from experiences

UNIT I PROBLEM SOLVING 9

Introduction – Agents – Problem formulation – uninformed search strategies – heuristics

– informed search strategies – constraint satisfaction

UNIT II LOGICAL REASONING 9

Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder

logic – forward chaining – backward chaining – unification – resolution

UNIT III PLANNING 9

Planning with state-space search – partial-order planning – planning graphs – planning

and acting in the real world

UNIT IV UNCERTAIN KNOWLEDGE AND REASONING 9

Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –

inferences in Bayesian networks – Temporal models – Hidden Markov models

UNIT V LEARNING 9

Learning from observation - Inductive learning – Decision trees – Explanation based

learning – Statistical Learning methods - Reinforcement Learning

TOTAL: 45

 PERIODS TEXT BOOK:

1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second

Edition, Pearson Education, 2003.

REFERENCES:

1. David Poole, Alan Mackworth, Randy Goebel, ”Computational Intelligence : a logical

approach”, Oxford University Press, 2004.

2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem

solving”, Fourth Edition, Pearson Education, 2002.

3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.