ML4151 Artificial Intelligence Syllabus:
ML4151 Artificial Intelligence Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand basic problem solving strategies.
To outline game theory based search and constraint satisfaction
To study knowledge representation techniques
To explore reasoning and planning associated with AI.
To study the techniques of knowledge representation.
To understand probabilistic and other types of reasoning
To discuss ethical and safety issues associated with AI
UNIT I INTRODUCTION AND PROBLEM SOLVING
Artificial Intelligence -Introduction – Problem-solving -Solving Problems by Searching – Uninformed Search Strategies -Informed (Heuristic) Search Strategies – Local Search – Search in Partially Observable Environments
UNIT II ADVERSARIAL SEARCH AND CONSTRAINT SATISFACTION PROBLEMS
Game Theory- Optimal Decisions in Games – Heuristic Alpha–Beta Tree Search- Monte Carlo Tree Search – Stochastic Games – Partially Observable Games – Limitations of Game Search Algorithms Constraint Satisfaction Problems (CSP)– Examples – Constraint Propagation Backtracking Search for CSPs – Local Search for CSPs
UNIT III KNOWLEDGE, REASONING AND PLANNING
First Order Logic – Inference in First Order Logic -Using Predicate Logic – Knowledge Representation – Issues -Ontological Engineering – Categories and Objects – Reasoning Systems for Categories – Planning -Definition -Algorithms -Heuristics for Planning -Hierarchical Planning
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING
Quantifying Uncertainty – Probabilistic Reasoning – Probabilistic Reasoning over Time Probabilistic Programming -Making Simple Decisions – Making Complex Decisions – Case Based Reasoning –Explanation-Based Learning – Evolutionary Computation
UNIT V PHILOSOPHY, ETHICS AND SAFETY OF AI
The Limits of AI – Knowledge in Learning –Statistical Learning Methods – Reinforcement Learning – Introduction to Machine Learning and Deep Learning -Can Machines Really Think? – Distributed AI Artificial Life-The Ethics of AI – Interpretable AI- Future of AI – AI Components -AI Architectures
TOTAL : 45 PERIODS
SUGGESTED ACTIVITIES:
1. Solve puzzles with uninformed and informed searches.
2: Reasoning methods through puzzles and real life scenarios
3: Ontology creation using Protégé
4: Give example scenarios where probabilistic reasoning and case based reasoning can be applied
5: Discuss some case studies and their ethical issues
COURSE OUTCOMES:
CO1: Implement any three problem solving methods for a puzzle of your choice
CO2: Understand Game playing and implement a two player game using AI techniques
CO3: Design and Implement an example using predicate Logic
CO4: Implement a case based reasoning system
CO5:Discuss some methodologies to design ethical and explainable AI systems
REFERENCES:
1. Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, Pearson, 4th Edition, 2020.
2. Zhongzhi Shi “Advanced Artificial Intelligence”, World Scientific; 2019.
3. Kevin Knight, Elaine Rich, Shivashankar B. Nair, “Artificial Intelligence”, McGraw Hill Education; 3rd edition, 2017
4. Richard E. Neapolitan, Xia Jiang, “Artificial Intelligence with an Introduction to Machine Learning”, Chapman and Hall/CRC; 2nd edition, 2018
5. Dheepak Khemani, “A first course in Artificial Intelligence”, McGraw Hill Education Pvt Ltd., NewDelhi, 2013.
6. Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, Morgan Kaufmann Publishers Inc; Second Edition, 2003.