Artificial Intelligence (AI) is a branch of computer science that enables machines to perform tasks that normally require human intelligence.
It involves learning, reasoning, problem-solving, and decision-making using algorithms and data. AI is widely used in applications such as virtual assistants, healthcare, robotics, and autonomous systems.
PREREQUISITE: Computer Programming, Mathematical Foundations of Computer Science, linear algebra, data structures and algorithms
COURSE OUTCOMES (COs): At the end of the course, students will be able to
- CO1: Enumerate the history & foundation of AI. (Understand- L2)
- CO2: Applythe searching algorithms for AI in problem solving. (Apply- L3)
- CO3: Choose the appropriate representation of knowledge. (Apply- L3)
- CO4: Choose the appropriate logic concepts. (Apply- L3)
- CO5: Understand Expert systems techniques in AI (Understand-L2)
UNIT-1: Introduction: AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation.
• Unit-1 PPTs:
Click Here for PPT
- LECTURE NOTES -PDF:
- DESCRIPTIVE & QUESTIONS: (Click Below..)
UNIT II: Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Hill climbing, A*, AO* Algorithms, Problem reduction, Game Playing-Adversial search, Games, mini-max algorithm, optimal decisions in multiplayer games, Problem in Game playing, Alpha-Beta pruning, Evaluation functions.
• Unit-2 PPTs:
Click Here for PPT
- LECTURE NOTES -PDF:
- DESCRIPTIVE & QUESTIONS:
UNIT III: Representation of Knowledge: Knowledge representation issues, predicate logic- logic programming, semantic nets- frames and inheritance, constraint propagation, representing knowledge using rules, rules based deduction systems. Reasoning under uncertainty, review of probability, Bayes’ probabilistic interferences and dempstershafer theory.
UNIT IV: Logic concepts: First order logic. Inference in first order logic, propositional vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution, Learning from observation Inductive learning, Decision trees, Explanation based learning, Statistical Learning methods, Reinforcement Learning.
UNIT V: Expert Systems: Architecture of expert systems, Roles of expert systems – Knowledge Acquisition Meta knowledge Heuristics. Typical expert systems – MYCIN, DART, XCON: Expert systems shells.
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