for MTech(CSE) -II Sem 2016
under VTU syllabus-14SCS24
Faculty : DR.S.SRIDHAR, Director-RVCT, R.V.College of Engineering , Bangalore -560059
Prerequisite: Logical thinking , PROLOG Programming, KnowledgeBase, inferencial analysis
Course Overview
This course will allow studentsto Understand uncertainty and Problem solving techniques, various symbolic knowledge
representation to specify domains and reasoning tasks of a situated software agent, different logical systems for
inference over formal domain representations, various learning techniques and agent technology, Identify symbolic
knowledge representation to specify domains and reasoning tasks of a situated software agent, different logical
systems for inference over formal domain representations, a particular inference algorithm for a given problem
specification and agent technology, Analyze intelligent agents for problem solving, reasoning, planning, decision
making, performance constraints for a large system and Implement AI technique to a given concrete
problemrelatively by considering a large system
Course Learning Objectives (CLO):
At the end of the course the students should be able to:
1. Understand uncertainty and Problem solving techniques, various symbolic knowledge representation to specify
domains and reasoning tasks of a situated software agent
2. Understand different logical systems for inference over formal domain representations, various learning
techniques and agent technology
3. Identify symbolic knowledge representation to specify domains and reasoning tasks of a situated software agent,
different logical systems for inference over formal domain representations, a particular inference algorithm for a
given problem specification and agent technology
4. Analyze intelligent agents for problem solving, reasoning, planning, decision making, performance constraints for
a large system
5. Implement AI technique to a given concrete problemrelatively by considering a large system
Relevance of the Course:
This course will allow students to Understand uncertainty and Problem solving techniques, various symbolic knowledge
representation to specify domains and reasoning tasks of a situated software agent, different logical systems for
inference over formal domain representations, various learning techniques and agent technology, Identify symbolic
knowledge representation to specify domains and reasoning tasks of a situated software agent, different logical
systems for inference over formal domain representations, a particular inference algorithm for a given problem
specification and agent technology, Analyze intelligent agents for problem solving, reasoning, planning, decision
making, performance constraints for a large system and Implement AI technique to a given concrete
problemrelatively by considering a large system
. Course Outcomes
Upon completion of the course the students will be able to do
1. Understand uncertainty and Problem solving techniques, various symbolic knowledge representation to specify
domains and reasoning tasks of a situated software agent
2. Understand different logical systems for inference over formal domain representations, various learning
techniques and agent technology
3. Identify symbolic knowledge representation to specify domains and reasoning tasks of a situated software agent,
different logical systems for inference over formal domain representations, a particular inference algorithm for a
given problem specification and agent technology
4. Analyze intelligent agents for problem solving, reasoning, planning, decision making, performance constraints for
a large system
5. Implement AI technique to a given concrete problemrelatively by considering a large system
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Lesson Plan
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Calendar of events
To receive Teaching Materials.......send mail to : drssridhar@yahoo.com
Video from MIT, USA
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Assignment 1 ...Date of Submission : 25th Feb.2016
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1) What do you understand by AI Problems, The Underlying assumption, and AI Technique?,
2) What are the Level of the model, Criteria for success ?
3) Define the problem as a state space search.
4) What are Production systems ?
5) What do you understand by Problem characteristics and Production system characteristics ?
6) Give the Issues in the design of search programs.
7) How do you relate Agents and Environments ?
8) How do you define the nature of environments ?
9) Design the structure of agents
10) How do you implement Generate-and-test, Hill climbing, Best-first search ?
11) Give details on Problem reduction, Constraint satisfaction, Mean-ends analysis.
12) How do you design Representations and mappings ?
13) What are the Approaches to knowledge representation ?
14) Detail the Issues in knowledge representation, while framing problem.
15) How do you Represent simple facts in logic, instance and ISA relationships,
16) Explain Computable functions and predicates, Resolution, Natural Deduction.
17) What do you mean by Knowledge –based agents and the Wumpus world
18) Give details on :Propositional logic, Propositional theorem proving, Effective propositional model checking
19) What are Agents based on propositional logic ?
Assignment 2 ................. Submission Date : 30th Mar 2016
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1. How do you design Logic for nonmonotonic reasoning ?
2. What do you understand by Bayesian Networks ?
3. Explain Dempster-Shafer Theory
4. Give an example for Fuzzy logic with details
5. What are Implementation Issues in reasoning systems ?
6. How do you Augment a problem-solver ?
7. How do you implement Depthfirst search ?
8. Explain Probability with dice, tossing a coin, all events, and various
probability like marginal, conditional probabilities.
9. Given an example to apply Bayes Theorem
10. Certainty factors and rule-based systems
11. How do you act under uncertainty?
12. What are Basic probability notations ? Give examples.
13. What do you mean by Inference using full joint distributions
14. Give examples for Independence, mutually exhaustive events while rolling dice.
15. What doyou mean by The Wumpus world revisited ?
Assignment 3 ............... Submission Date : 30th April 2016
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1) What do you mean by Semantic Nets and Semantic Frames ?
2) How do you define Conceptual dependency, Conceptual scripts, CYC ?
3) How do you implement Optimal Decision in Games ?
4) Explain : Alpha-Beta Pruning
5) Describe Imperfect Real-Time Decisions
6) What do you mean by Stochastic Games, Partially Observable Games ?
7) Give examples for State-Of-The-Art Game Programs
8) Where is the need for Alternative Approaches ?
9) Explain : Forms of learning, Supervised learning, Learning decision trees
10) How do you Evaluate and choose the best hypothesis ?
11) Explain : The theory of learning ,PAC, Regression
12) Explain : Nonparametric models, Support vector machines
13) How do you classify with linear models ?
14) What do you mean by Statistical learning, learning with complete data ?
15) Explain learning with hidden variables
16) How do you implement The EM algorithm ?
17) Give one Case study of large system
18) What do you mean by Implementation process ?
