Course

Unit I

History and Foundations of AI, Introduction to AI and systems – search strategies, problem characteristics, system characteristics, Intelligent Agents – Agents and environments, nature of environments and structure of agents, Ethics in AI; Case study: Games and puzzles.

Unit II

Problem Solving by Search: Informed search – Heuristic, Hill climbing, Best first search, A*, AO*, Approaches in knowledge representation, Game Playing – minmax algorithm, Alpha beta pruning, Swarm intelligence, cooperative learning – constrained and unconstrained problems; Case study – Example: Group of Drones.

Unit III

Knowledge representation – Game playing – Knowledge representation, Knowledge representation using Predicate logic, Introduction to predicate calculus, Resolution, Use of predicate calculus, Knowledge representation using other logic-Structured representation of knowledge. Knowledge inference – Production based system, Frame based system. Inference – Backward chaining, Forward chaining, Rule value approach, Fuzzy reasoning – Certainty factors, Bayesian Theory-Bayesian Network-Dempster – Shafer theory Case Study-Examples: Recommendation systems, Knowledge based systems in application areas of computer science, Decision support systems, prediction and warning systems.

Unit IV

Classical Planning: Algorithms for Planning, Planning Graphs, Hierarchical Planning, Planning and Acting in Nondeterministic Domain, Map building, Multi-Agent Planning; Case study – Examples: vacuum cleaner, washing machine.

Course Objectives

This course introduces the various fundamental concepts of Artificial Intelligence, such as:

• To introduce classical AI, problem formulation and intelligent agents.
• To introduce techniques for problem solving by search, knowledge-representation and reasoning.
• To introduce game playing and algorithms associated with it.
• To introduce planning, acting, and multi-agent systems.

Course Outcomes

CO1: Understand the foundations of AI systems, intelligent agents, problems solving strategies and Ethics in AI.

CO2: Apply elementary principles of AI like search and game playing for problem solving.

CO3: Analyse real world problems under uncertainty and solve them using knowledge representation and reasoning.

CO4: Understand planning and decision making in intelligent systems.

CO-PO Mapping

Evaluation Pattern: 70:30

*CAL – Can be Lab Assessments, Project, and Report

**End Semester can be theory examination/ lab-based examination

Textbook(s)

Russell, Stuart, Jonathan, Norvig, Peter, Davis, Ernest. “Artificial Intelligence: A Modern Approach”. United Kingdom: Pearson, 4^th Edition, 2022

Deepak Khemani. “A First Course in Artificial Intelligence”. McGraw Hill Education (India), 2017.

Reference(s)

Denis Rothman. “Artificial Intelligence by Example”, Packt, 2^nd Edition, 2018.

Rich and Knight, “Artificial Intelligence”,3^rd Edition, Tata Mc Graw Hill, 2017.