Page7
BCA
A
-
10
2
:
Artificial Intelligence
Course
Outcome
(
CO)
CO1
Design user interfaces to improve human–AI interaction and real-time decision-
making.
CO2
Evaluate the advantages, disadvantages, challenges, and ramifications of human–
AI
augmentation.
CO3
Design and develop symbiotic human–
AI systems that balance the information
processing power of computational
systems with human intelligence and decision
making.
CO4
Explain the benefits, limitations, and tradeoffs of designing engaging and ethical
conversational user interactions, including those supported by chatbots, smart
speakers, and other AI
-
driven, voice
-
based technologies.
CO
5
Design and evaluate conversational interfaces for different users and contexts of use.
DETAILED SYLLABUS
Unit
Topic
Proposed
Lecture
I
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.
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- Adversarial
search, Games, mini-max algorithm, optimal decisions in multiplayer games,
Problem in Game playing, Alpha
-
Beta pruning, Evaluation functions.
III
Knowledge representation issues, predicate logic- logic programming, semantic
nets- frames and inheritance, constraint propagation, representing knowledge using
rules, rules based deduction systems. Reasoning und er uncertainty, review of
probability, Baye’s probabilistic interferences and Dempster
-
Shafer Theory.
IV
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.
V
Expert systems:- Introduction, basic concepts, structure of expert systems, the
human element in expert systems how expert systems works, problem areas
addressed by expert systems, expert systems success factors, types of expert
systems, expert systems and the internet interacts web, knowledge engineering,
scope of knowledge, difficulties, in knowledge acquisition methods of knowledge
acquisition, machine learning, intelligent agents, selecting an appropriate knowledge
acquisition method, societal impacts reasoning in artificial intelligence, inference
with rules, with frames: model based reasoning, case based reasoning, explanation &
meta knowledge inference with uncertainty representing uncertainty.