CS2351 ARTIFICIAL INTELLIGENCE SYLLABUS
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
Introduction – Agents – Problem formulation – uninformed search strategies – heuristics
– informed search strategies – constraint satisfaction
UNIT II LOGICAL REASONING
Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder
logic – forward chaining – backward chaining – unification – resolution
UNIT III PLANNING
Planning with state-space search – partial-order planning – planning graphs – planning
and acting in the real world
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING
Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –
inferences in Bayesian networks – Temporal models – Hidden Markov models
UNIT V LEARNING
Learning from observation - Inductive learning – Decision trees – Explanation based
learning – Statistical Learning methods - Reinforcement Learning
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.
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
Introduction – Agents – Problem formulation – uninformed search strategies – heuristics
– informed search strategies – constraint satisfaction
UNIT II LOGICAL REASONING
Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder
logic – forward chaining – backward chaining – unification – resolution
UNIT III PLANNING
Planning with state-space search – partial-order planning – planning graphs – planning
and acting in the real world
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING
Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –
inferences in Bayesian networks – Temporal models – Hidden Markov models
UNIT V LEARNING
Learning from observation - Inductive learning – Decision trees – Explanation based
learning – Statistical Learning methods - Reinforcement Learning
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.