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LESSON PLAN
Artificial Intelligence and Agent Technology
Faculty : Dr.S.Sridhar, Dean-CCCF
Subject Code : 14SCS24
Date
:
IA Marks : 50
Exam Hours : 3
Exam marks : 100
Total Lecture Hours : 50
Time Table....M.Tech.(CSE) –II Semester
Course Outcomes
CO1: 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
CO2: 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
CO3: Analyze intelligent agents for problem solving, reasoning, planning, decision making, performance
constraints for a large system
CO4: Implement AI technique to a given concrete problem relatively by considering a large system
UNIT TITLE : What is Artificial Intelligence
OBJECTIVE : To understand AI Problems, problem spaces and intelligent agents
S.No.
SUBJECT TOPIC
PERIODS
1.
The AI Problems, The Underlying assumption, What is an AI Technique?, The
Level of the model, Criteria for success, some general references, One final word
and beyond.
2
2.
Problems, problem spaces, and search: Defining, the problem as a state space
search, Production systems
Problem characteristics, Production system characteristics, Issues in the design of
search programs, Additional Problems
3
Intelligent Agents: Agents and Environments, The nature of environments, The
structure of agents.
3
3.
4.
2
UNIT TITLE : Heuristic search techniques
OBJECTIVE : To understand Heuristic search techniques, knowledge representation rules, predicate logic and
logical agents
S.No.
SUBJECT TOPIC
PERIODS
1.
Heuristic search techniques: Generate-and-test, Hill climbing, Best-first search,
Problem reduction, Constraint satisfaction, Mean-ends analysis.
2
2.
Knowledge representation issues: Representations and mappings, Approaches
to knowledge representation, Issues in knowledge representation, The frame
problem.
3
3.
Using predicate logic: Representing simple facts in logic, representing instance
and ISA relationships, Computable functions and predicates, Resolution, Natural
Deduction.
2
4.
Logical Agents: Knowledge –based agents, the Wumpus world, LogicPropositional logic, Propositional theorem proving, Effective propositional
model checking, Agents based on propositional logic.
3
UNIT TITLE : Symbolic Reasoning Under Uncertainty:
OBJECTIVE : To identify reasoning through Bayesian networks, statistical reasoning to quantify uncertainty
S.No.
SUBJECT TOPIC
PERIODS
1.
Logic for nonmonotonic reasoning Bayesian Networks, Dempster-Shafer Theory,
Fuzzy logic.
2
2.
Implementation Issues, Augmenting a problem-solver,
Depthfirst search, Implementation: Breadth-first search.
Implementation:
3
3.
Statistical Reasoning: Probability and bayes Theorem, Certainty factors and
rule-based systems,
3
4.
Quantifying Uncertainty: Acting under uncertainty, Basic probability notation,
Inference using full joint distributions, Independence, Bayes’ rule and its use,
The Wumpus world revisited.
2
UNIT TITLE : Weak Slot-and-filter structures:
OBJECTIVE : To analyze Semantic nets, strong slot and filter structures, optimal decisions and alternatives.
S.No.
SUBJECT TOPIC
PERIODS
1.
Semantic Nets, Frames
2
2.
Strong slot-and –filler structures: Conceptual dependency, scripts, CYC.
2
3.
Adversarial Search: Games, Optimal Decision in Games, Alpha-Beta Pruning,
Imperfect Real-Time Decisions,
Stochastic Games, Partially Observable Games, State-Of-The-Art Game
Programs, Alternative Approaches
3
4.
3
UNIT TITLE : Learning From examples:
OBJECTIVE : To implement AI technique to a given concrete problem relatively by considering a large
system
S.No.
SUBJECT TOPIC
PERIODS
Forms of learning, Supervised learning, Learning decision trees, Evaluating and
choosing the best hypothesis
The theory of learning ,PAC, Regression and Classification with linear models,
Nonparametric models, Support vector machines, Ensemble learning.
2
3.
Learning Probabilistic Models: Statistical learning, learning with complete data
3
4.
learning with hidden variables: The EM algorithm.
2
1.
2.
3
Suggested References
(Name of the Book, Authors, Publisher and year of Publication)
Text Books:
1. Elaine Rich,Kevin Knight, Shivashanka B Nair:Artificial Intelligence, Tata CGraw Hill 3rd
edition. 2013
2. Stuart Russel, Peter Norvig: Artificial Intelligence A Modern Approach, Pearson 3rd edition 2013.
Reference Books:
3. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier