Introduction to Artificial Intelligence , Agents That Reason Logically , First-Order Logic , Building a Knowledge Base , Planning A Simple Planning Agent Form Problem Solving to Planning , Planning in Situation Calculus , Basic Representations for Planning , Making Simple Decision , Learning in Neural and Belief Networks , Knowledge in Learning ,
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Module I ( 10 hrs. )
Introduction to Artificial Intelligence: The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Module II ( 10 hrs. )
Agents That Reason Logically; A Knowledge-Based Agent, The Wumpus World Environment,
Representation, Reasoning & Logic prepositional Logic : A very simple Logic, An agent for the
First-Order Logic; Syntax and Semantics, Extensions and National, Variations, using First Order
Logic, Logical Agents for the Wumpus World, A Simple Reflex Agent, Representing Charge in
the World, Deducing Hidden Properties of the World, Preferences Among Actions, Toward A
Building a Knowledge Base; Properties of Good and Bad Knowledge Bases, Knowledge
Engineering. The Electronic Circuits Domain, General Outology, The Grocery Shopping World.
Inference in First-Order Logic : Inference Rules Involving Quantifiers, An Example Proof.
Generalized Modus Ponens, Forward and Backward, Chaining & Completeness, Resolution: A
complete Inference Procedure, Completeness of Resolution.
Module III (10 hrs. )
Planning A Simple Planning Agent Form Problem Solving to Planning. Planning in Situation
Calculus. Basic Representations for Planning. A Partial-Order planning Example, A partial Order
planning algorithm, Planning With partially Instantiated Operators, Knowledge Engineering for
Making Simple Decision: Combining Beliefs and desires under uncertainty. The Basis of Utility
Theory, Utility Functions. Multi attribute utility Functions, Decision Networks. The Value of
Information. Decision – Theoretic Expert Systems.
Learning in Neural and Belief Networks’ How the Brain Works, Neural Networks, perceptions,
Multi-layered Feed Forward Networks Applications Back propagation algorithm Applications of
Module IV ( 10 hrs. )
Knowledge in Learning: Knowledge in Learning, Explanation-based Learning, Learning Using
Relevance Information, Inductive Logic Programming. Agents that Communicate:
Communication as action, Types of Communicating Agents, A Formal Grammar for A subset of
English Syntactic Analysis (Parsing), Definite Clause Grammar (DCG), Augmenting A
Grammar. Semantic Interpretation. Ambiguity and Disambiguation. A Communicating Agent.
Practical Natural Language processing Practical applications. Efficient Parsing Scaling up the
lexicon. Scaling up the Grammar Ambiguity. Discourse Understanding.
ARTIFICIAL INTELLIGENCE best Lecture notes pdf download for MCA and CS