06CS764 - Artificial Intelligence |
PART A |
UNIT 1 |
INTRODUCTION: What is AI? Intelligent Agents: Agents and
environment; Rationality; the nature of environment; the structure of agents.
Problem-solving: Problem-solving agents; Example problems; Searching for
solution; Uninformed search strategies. |
UNIT 2 |
INFORMED SEARCH, EXPLORATION, CONSTRAINT
SATISFACTION, ADVERSIAL SEARCH: Informed search strategies;
Heuristic functions; On-line search agents and unknown environment.
Constraint satisfaction problems; Backtracking search for CSPs. Adversial
search: Games; Optimal decisions in games; Alpha-Beta pruning. |
UNIT 3 |
LOGICAL AGENTS : Knowledge-based agents; The wumpus world as an
example world; Logic; propositional logic Reasoning patterns in
propositional logic; Effective propositional inference; Agents based on
propositional logic. |
UNIT 4 |
FIRST-ORDER LOGIC, INFERENCE IN FIRST-ORDER LOGIC 1: Representation revisited; Syntax and semantics of first-order logic; Using
first-order logic; Knowledge engineering in first-order logic. Propositional
versus first-order inference; Unification and lifting. |
PART B |
UNIT 5 |
INFERENCE IN FIRST-ORDER LOGIC 2: Forward chaining;
Backward chaining; Resolution. |
UNIT 6 |
KNOWLEDGE REPRESENTATION: Ontological engineering;
Categories and objects; Actions, situations, and events; Mental events and
mental objects; The Internet shopping world; Reasoning systems for
categories; Reasoning with default information; Truth maintenance systems. |
UNIT 7 |
PLANNING, UNCERTAINTY, PROBABILISTIC REASONING: Planning: The problem; Planning with state-space approach; Planning
graphs; Planning with propositional logic. Uncertainty: Acting under
certainty; Inference using full joint distributions; Independence; Bayes rule
and its use.
Probabilistic Reasoning: Representing knowledge in an uncertain domain;
The semantics of Bayesian networks; Efficient representation of conditional
distributions; Exact inference in Bayesian networks. |
UNIT 8 |
LEARNING, AI: PRESENT AND FUTURE: Learning: Forms of
Learning; Inductive learning; Learning decision trees; Ensemble learning;
Computational learning theory. AI: Present and Future: Agent
components; Agent architectures; Are we going in the right direction? What
if AI does succeed? |
REFERENCE |
TEXT BOOKS: |
1. Artificial Intelligence: A Modern Approach Stuart Russel, Peter
Norvig, 2nd Edition, Pearson Education, 2003.
|
Reference Books |
1. Artificial Intelligence - Elaine Rich, Kevin Knight, 2nd Edition,
Tata McGraw Hill, 1991.
2. Principles of Artificial Intelligence Nils J. Nilsson, Elsevier,
1980. |