ARTIFICIAL INTELLIGENCE – KOT-063
Section-wise Important Questions & Ready Answers
SECTION A
(Attempt all questions in brief – 2 marks each)
(a) Weak AI and Strong AI
Weak AI refers to systems designed to perform a specific task intelligently without possessing real understanding or consciousness, such as chatbots or recommendation systems. Strong AI refers to machines that possess human-like intelligence, consciousness, and reasoning ability, capable of understanding and learning any intellectual task like a human.
(b) Difference Between Artificial Intelligence and Human Intelligence
Artificial intelligence operates on algorithms, data, and programmed logic, whereas human intelligence involves emotions, creativity, consciousness, and moral reasoning. Humans can adapt intuitively to new situations, while AI systems depend on training data and predefined models.
(c) Significance of Heuristic Functions
Heuristic functions guide search algorithms by estimating the cost from the current state to the goal state. They reduce search time and improve efficiency by directing the search toward promising paths instead of exploring all possibilities.
(d) Issues in Knowledge Representation
Major issues include representing incomplete or uncertain knowledge, maintaining consistency, handling dynamic information, ensuring efficient inference, and selecting an appropriate representation scheme.
(e) Local Minima in Search Technique
A local minimum is a state where a search algorithm finds a solution better than its neighboring states but not the best overall solution. Algorithms like hill climbing may get stuck at local minima and fail to find the global optimum.
(f) Formal Logic with Example
Formal logic is a system of reasoning using symbols and well-defined rules to derive conclusions.
Example:
If p→qp \rightarrow qp→q and ppp is true, then qqq must be true (Modus Ponens).
(g) Software Agents with Examples
Software agents are programs that perform tasks autonomously on behalf of users. Examples include web crawlers, email spam filters, and virtual assistants like scheduling agents.
(h) Intelligent Software Agents vs Intelligent Agents
Intelligent software agents operate within software environments such as the internet or operating systems. Intelligent agents in AI may include both software and hardware agents like robots that interact with physical environments.
(i) N-gram Language Model
An N-gram language model predicts the probability of a word based on the previous N-1 words. It is widely used in speech recognition and text prediction systems.
(j) Machine Translation
Machine translation is the automatic conversion of text or speech from one natural language to another using AI techniques such as rule-based, statistical, or neural models.
SECTION B
(Attempt any three – 10 marks each)
2(a) Characteristics and Applications of Learning Agent
A learning agent improves its performance through experience. It consists of four components: performance element, learning element, critic, and problem generator. Learning agents adapt to changing environments and are used in robotics, game playing, recommendation systems, and adaptive control systems.
2(b) Constraint Satisfaction Problem (CSP) with Example
A CSP consists of variables, domains, and constraints. The goal is to assign values to variables such that all constraints are satisfied.
Example: In the map coloring problem, neighboring regions must not have the same color. CSP techniques include backtracking, constraint propagation, and heuristics.
2(c) Propositional Logic Formula Analysis
Given formula:
(p∧q)→(r∨¬q)(p \land q) \rightarrow (r \lor \neg q)(p∧q)→(r∨¬q)
This formula is satisfiable because there exist truth assignments that make it true.
It is not contradictory because it is not false for all interpretations.
It is not valid because it does not evaluate to true under all possible interpretations.
2(d) Negotiation and Bargaining
Negotiation is a process where agents communicate to reach a mutually acceptable agreement. Bargaining is a specific form of negotiation involving offers and counter-offers, often related to price or resource allocation. For example, automated trading agents negotiating prices in e-commerce systems.
2(e) Information Extraction
Information extraction involves automatically extracting structured information from unstructured text. It includes tasks such as named entity recognition, relationship extraction, and event detection. Applications include search engines, resume screening, and news analysis.
SECTION C
3(a) Branches of Artificial Intelligence and Their Progress
Major branches of AI include machine learning, natural language processing, expert systems, robotics, computer vision, and knowledge representation. Significant progress has been made in deep learning, speech recognition, autonomous vehicles, and intelligent decision-making systems.
3(b) Characteristics of Agent Environment with Example
Agent environments are characterized by properties such as observability, determinism, episodic or sequential nature, static or dynamic behavior, and discrete or continuous states.
Example: A chess game environment is fully observable, deterministic, sequential, static, and discrete.
