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ARTIFICIAL INTELLIGENCE best Lecture notes pdf download
Neeraj Yadav

ARTIFICIAL INTELLIGENCE best Lecture notes pdf download

Neeraj Yadav | 18-Jan-2016 |
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
Improvement Algorithms.
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
Wumpus World.
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
Goal-Based Agent.
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
Neural Networks.
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.


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