JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives

Knowledge on significance of planning, Learning, Perception and Action

Course Outcomes:

• 1. Understand the concepts of Game Playing and Planning.
• 2. Analyze parallel and Distributed AI and Learning
• 3. Illustrate Expert Systems, Perception and Action
• 4. Analyze Genetic algorithms, Artificial Immune Systems

UNIT - I

Game Playing: introduction, The Min - Max search procedure, adding alpha-beta cutoffs, Additional Refinements, Iterative Deepening, References on Specific Games Planning: Introduction, An Example Domain, The Blocks World, Components of a Planning System, Goal Stack Planning, Nonlinear planning using Constraint Posting, Hierarchical Planning, Reactive systems, Other Planning Techniques

UNIT - II

Parallel and Distributed AI: Psychological Modeling, Parallelism in Reasoning Systems, Distributed Reasoning Systems Learning: Introduction, Rote learning, Learning by Taking Advice, Learning in Problem Solving, Learning from Examples: Induction, Explanation based Learning, Discovery, Analogy, Formal Learning Theory, Neural Net Learning and Genetic learning

UNIT - III

Expert Systems: Representing and using Domain Knowledge, Expert System Shells, Explanation, Knowledge Acquisition Perception and Action: Real time Search, Perception, Action, Robot Architectures

UNIT - IV

Genetic algorithms: Copying Nature’s Approaches: A Peek into the Biological World, Genetic Algorithms (GAs), Significance of the Genetic Operators, Termination Parameters, Niching and Speciation, Evolving Neural Networks, Theoretical Grounding, Ant Algorithms Artificial Immune Systems: Introduction, The Phenomenon of Immunity, Immunity and Infection, The Innate Immune System- The first line of Defence, The Adaptive Immune System - The second line of defence, Recognition, Clonal Selection, Learning, Immune Network Theory, Mapping Immune Systems to Practical Applications, Other Applications

UNIT - V

Prolog - The Natural Language of Artificial Intelligence: Introduction, Converting English to Prolog facts and Rules, Goals, Prolog Terminology, Variables, Control Structures, Arithmetic Operators, Matching in Prolog, Backtracking, Cuts, Recursion, Lists, Dynamic Databases, Input/Output and Streams. Some Aspects Specific to LPA Prolog.

TEXT BOOKS:

• 1. Elaine Rich, Kevin Knight, Shivashankar B Nair, Artificial Intelligence, Third Edition, McGrawHill Education.

REFERENCES:

• 1. Artificial Intelligence, 3rd Edn, Patrick Henry Winston, Pearson Education.
• 2. Artificial Intelligence, Shivani Goel, Pearson Education.
• 3. Artificial Intelligence and Expert systems – Patterson, Pearson Education.