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Note for ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM - AIES by Ashutosh Jaiswal

  • ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM - AIES
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Ashutosh Jaiswal
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1 14ACS34 ARTIFICIAL INTELLIGENCE & EXPERT SYSTEMS III B. Tech -I Semester (IT) Objectives: The objective of this course is to make students to: 1. Understand the concepts of AI and Intelligent Agents. 2. Explore Problem solving using search techniques in AI. 3. Understand Logical Agents and First-Order logic. 4. Explore knowledge Representation issues. 5. Understand concepts of learning from examples. UNIT – I Introduction: What Is AI, the Foundations of Artificial Intelligence, The History of Artificial Intelligence, The State of the Art Intelligent Agents: Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of Environments, And The Structure of Agents UNIT – II Solving Problems by Searching: Problem-Solving Agents, Uninformed Search Strategies, Informed (Heuristic) Search Strategies, Heuristic Functions Beyond Classical Search: Local Search Algorithms and Optimization Problems, Searching with Nondeterministic Actions and Partial Observations, Online Search Agents and Unknown Environments Constraint Satisfaction Problems: Definition, Constraint Propagation, Backtracking Search, Local Search, The Structure of Problems UNIT – III Logical Agents: Knowledge-Based Agents, Propositional Logic, Propositional Theorem Proving, Effective Propositional Model Checking, Agents Based on Propositional Logic First-Order Logic: Syntax and Semantics, Knowledge Engineering in FOL, Inference in FirstOrder Logic, Unification and Lifting, Forward Chaining, Backward Chaining, Resolution UNIT – IV Planning: Definition, Algorithms, Planning Graphs, Hierarchical Planning, Multiagent Planning. Knowledge Representation: Ontological Engineering, Categories and Objects, Events, Mental Events and Mental Objects, Reasoning Systems for Categories, Reasoning with Default Information, The Internet Shopping World

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2 UNIT – V Learning from Examples: Forms of Learning, Supervised Learning, Learning Decision Trees, Evaluating and Choosing the Best Hypothesis, The Theory of Learning, Regression and Classification with Linear Models, Artificial Neural Networks. Expert Systems Architectures: Introduction, Rule Based System Architecture, NonProduction System Architecture, Dealing with uncertainty, Knowledge Acquisition and Validation, Knowledge System Building Tools. Outcomes: At the end of the course, students should be able to: 1. Understand foundation and basic concepts of AI and Intelligent Agents. 2. Evaluate Searching techniques for problem solving in AI. 3. Apply First-order Logic and chaining techniques for problem solving. 4. Handle knowledge representation techniques for problem solving. 5. Apply supervised learning and Neural Networks for solving problem in AI. TEXT BOOK: 1. Artificial Intelligence: A Modern Approach, 3rd edition, Pearson , Russel S, Norvig P, Education, 2010. 2. Introduction to Artificial Intelligence and Expert Systems, Dan W. Patterson ,PHI, New Delhi, 2006. REFERENCE BOOKS: 1. Artificial Intelligence, 3rd edition, Rich E, Knight K, Nair S B, Tata McGraw-Hill, 2009. 2. Artificial Intelligence: Structures and Strategies for Complex problem solving, 6th edition, Luger George F, Pearson Education, 2009 3. Minds and Computers An Introduction to the Philosophy of Artificial Intelligence, Carter M,Edinburgh University Press, 2007.

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3 UNIT – I Introduction: 1.1 1.2 1.3 1.4 What Is AI, the Foundations of Artificial Intelligence, The History of Artificial Intelligence, The State of the Art Intelligent Agents: 1.5 1.6 1.7 1.8 Agents and Environments, Good Behavior: The Concept of Rationality, The Nature of Environments, And The Structure of Agents

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4 1.1 WHAT IS AI? The eight definitions of AI, laid out along two dimensions. (i) (ii) The definitions on top are concerned with thought processes and reasoning, the second one on the bottom address behavior. The definitions on the left measure success in terms of fidelity to human performance, whereas the ones on the right measure against an ideal performance measure, called rationality. A system is rational if it does the "right thing," given what it knows. 1.1.1 Acting humanly: The Turing Test approach The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. The computer would need to possess the following capabilities: • natural language processing (NLP) to enable it to communicate successfully in English; • knowledge representation to store what it knows or hears;

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