DCA2109 ARTIFICIAL INTELLIGENCE FOR PROBLEM SOLVING JAN FEB 2026

Description

SESSION	JANUARY/FEBRUARY 2026
PROGRAM	BACHELOR OF COMPUTER APPLICATIONS (BCA)
SEMESTER	III
COURSE CODE & NAME	DCA2109 ARTIFICIAL INTELLIGENCE FOR PROBLEM SOLVING

 

 

Set – I

 

Q1. What is Artificial Intelligence? Describe the structure and functioning of different types of intelligent agents: simple reflex, model-based, goal-based, and utility-based, in details.

Ans 1.

What is Artificial Intelligence?

Artificial Intelligence (AI) is an area of computer science that focuses on the creation of machines and systems that can perform tasks which normally require human intelligence. These tasks include reasoning, learning, and problem solving, interpreting natural language, recognising patterns and making choices. AI systems are created to observe their environment as

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Q2. Provide a detailed explanation of goal formulation, characteristics of good goals, and how dynamic environments influence goal-setting in intelligent agents.

Ans 2.

Goal Formulation in Intelligent Agents

Goal formulation refers to the process that an intelligent agent defines what it wants to attain in its environment. When it comes to problem solving, goal formulation comes after the agent perceives the state in which it is currently located within the environment and realises that this isn’t what they would like to see. The term goal refers to the description of a list of desired state that the person wants to get to. The goal formulation process is the primary step of the process of

 

 

Q3. Explain the AO* algorithm in detail, including its evaluation function, cost updating mechanism, and search strategy using AND-OR graphs.

Ans 3.

AND-OR Graphs and AO*

This AO* algorithm works as a heuristic search algorithm created to search for optimal solutions for problem areas which naturally appear as AND-OR graphs. In contrast to standard graphs, where every node has multiple descendants connected by OR edges (meaning the agent picks one path) And-OR graphs additionally include AND nodes. All succeeding nodes must be solved

 

 

Set – II

 

Q4. Describe various categories of games used in AI research. Compare deterministic vs stochastic and perfect vs imperfect information games with examples.

Ans 4.

Games in AI

Games have been a central aspect of AI research due to the fact that they offer well-defined environments with clear rules, measurable outcomes, as well as a level of abstraction. AI researchers employ games for the development and testing of the effectiveness of search algorithms, decision-making techniques as well as learning methods. The games are classified

 

 

Q5. Explain Knowledge Representation in AI in detail and compare different KR techniques such as logical representation, semantic networks, frames, and scripts.

Ans 5.

Introduction to Knowledge Representation

Knowledge Representation (KR) is one of the fundamental aspects of AI that is concerned with how data about the world is stored in a format that the computer system is able to use to make rational decisions, think about and resolve problems. An AI machine must possess some degree of information about its subject to perform intelligently. Simply storing raw data does not suffice; the machine must represent relations, rules, concepts, as well as facts in a structured

 

 

Q6. Describe probability theory fundamentals (sample space, events, joint probability, conditional probability) and explain their role in uncertain reasoning.

Ans 6.

Sample Space

Probability theory is a mathematical framework for reasoning under uncertainty. The sample space is known as S is the collection of the outcomes possible in an experiment that is random. For example, when rolling an a 6-sided die the sample space would be is {1, 2, 3, 4, 5, 6}. Each possible outcome of the experiment is a member of the sampling space. The sample space