Artificial Intelligence Reasoning: Knowledge-Based Agents
1. Knowledge-Based Agents: Vacuum Cleaners
Two vacuum-cleaner agents operate in the same room:
- Agent 1: Follows fixed rules: “If dirt detected, then suck; if bump, then turn right.”
- Agent 2: Builds an internal map of where dirt and obstacles might be, updates it as it moves, and decides its next action.
Question: Explain why Agent 2 is considered a knowledge-based agent and how this capability changes the intelligence of its behavior compared to Agent 1.
Answer: Agent 2 maintains an internal knowledge
Read MoreDatabase Management Systems: Exam Questions and Solutions
Database Exam Solutions
2025 Q&A
(a) Q: A minimum cardinality of 0 specifies ______ type of participation.
Ans: Partial participation
(b)
Q: Let E1 and E2 be two entity sets with relationships R1 (1:M) and R2 (no attributes). What is the minimum number of tables required?
Ans: 2 tables
(c)
Q: Stud has 120 tuples and Enroll has 8 tuples. What are max and min tuples in Stud NATURAL JOIN Enroll?
Ans:
- Maximum = 8
- Minimum = 0
(d)
Q: Which normal form is considered adequate for normal relational database design?
Read MoreNLP with Python: Morphological Analysis and N-Grams
NLP Tasks with NLTK
Aim
Write a Python program to:
- Perform morphological analysis using the NLTK library.
- Generate n-grams using the NLTK n-grams library.
- Implement n-gram smoothing.
Description
- Morphological Analysis: This involves analyzing word structures to understand meaning and grammatical properties. NLTK provides tools like stemming and lemmatization for this purpose.
- N-Grams Generation: N-grams are contiguous sequences of n items from a text. NLTK provides functions to generate these from tokenized
Enterprise Software Platforms: Architecture and Best Practices
Enterprise Software Platform (ESP)
An Enterprise Software Platform (ESP) is an integrated, enterprise-wide environment of software, services, data, security, and governance used to run core business processes. It supports automation, integration, analytics, collaboration, scalability, and compliance across departments.
Platform Goals
- Business alignment and operational efficiency
- Agility, standardization, and reusability
- Security and digital transformation
Key Components and Requirements
Major components
Read MorePython String and List Built-in Functions Reference
Python String Built-in Functions
Case Conversion Functions
upper()
👉 Definition: Converts all characters in a string to uppercase. It returns a new string without modifying the original.
👉 Example: "hello".upper() # "HELLO"
lower()
👉 Definition: Converts all characters in a string to lowercase.
👉 Example: "HELLO".lower() # "hello"
title()
👉 Definition: Capitalizes the first letter of every word and converts the remaining letters to lowercase.
👉 Example: "hello world".title() # "Hello World"
Greedy Strategy and Dynamic Programming Algorithms
Module 2: Greedy Strategy
1. Huffman Coding (Data Compression)
Definition: A greedy algorithm used for lossless data compression. It assigns variable-length codes to characters based on their frequency.
Working Principle:
- Count the frequency of each character.
- Place characters in a priority queue (Min-Heap) based on frequency.
- Pick two nodes with the lowest frequencies and create a new internal node with the sum of their frequencies.
- Repeat until only one node (the root) remains.
- Assign ‘0’ to the left