DCA2109 ARTIFICIAL INTELLIGENCE FOR PROBLEM SOLVING

Description

SESSION	JULY/SEPTEMBER 2025
PROGRAM	BACHELOR OF COMPUTER APPLICATIONS (BCA)
SEMESTER	III
COURSE CODE & NAME	DCA2109 ARTIFICIAL INTELLIGENCE FOR PROBLEM SOLVING

 

 

Assignment SET – I

 

Q1a. Explain the concept of Artificial Intelligence (AI) and discuss its major real-world applications with suitable examples. 5  

1. Explain in detail the working of a Problem-Solving Agent in AI. Discuss each step in the process with a real-life example. 5

Ans 1.

1. Concept of Artificial Intelligence and Its Real-World Applications

Artificial Intelligence (AI) refers to the branch of computer science that aims to create systems capable of performing tasks that normally require human intelligence. It enables machines to learn from experience, reason, and make decisions like humans. AI combines disciplines such as machine learning, data science, and natural language processing to enable intelligent behavior in systems. The primary goal of AI is to develop agents that can perceive their environment, understand problems, and act rationally to achieve goals.

In real-world applications, AI has become an essential part of various industries. In healthcare, AI-based tools like diagnostic imaging and predictive analytics help detect diseases such as cancer or diabetes at early

 

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Q2a. Describe the various search strategies used in Artificial Intelligence. Compare uninformed and informed search techniques with examples. 5

1. What are the advantages of using heuristic search techniques in Artificial Intelligence? Also explain any two commonly used heuristic search methods with suitable examples.5

Ans 2.

1. Search Strategies in Artificial Intelligence and Comparison of Uninformed and Informed Search

Search strategies in Artificial Intelligence (AI) are systematic techniques used by agents to explore possible solutions and find the optimal path to reach a goal state. These strategies help an agent decide which states to examine and in what order. The process begins with an initial state and explores successor states until the desired goal is achieved. There are mainly two types of search strategies — uninformed (blind) search and informed (heuristic) search.

Uninformed search strategies do not use any domain-specific knowledge beyond the problem definition. They explore all possible paths systematically. Common uninformed techniques include Breadth-First

 

Q3a. Explain the working of the AO* algorithm in Artificial Intelligence. How does it handle AND and OR nodes differently during the search process?         5         

1. Discuss about Expert Systems. Also explain their architecture, working mechanism, advantages, and real-world applications. 5

Ans 3.

1. Working of AO* Algorithm and Handling of AND/OR Nodes

The AO* (And-Or Star) algorithm is a heuristic search algorithm designed to solve problems represented as AND-OR graphs, where the solution may involve multiple interdependent subproblems. Unlike simple search algorithms such as A*, which operate on linear paths, AO* can deal with situations where certain goals must be achieved simultaneously (AND nodes) or where alternative options exist (OR nodes).

The AO* algorithm

 

Assignment SET – II

 

 

Q4a. Describe the role of Artificial Intelligence in game playing. Also explain the use of the Minimax algorithm in making strategic decisions. 5       

1. What is Knowledge Representation in Artificial Intelligence? Explain its importance and describe any four core methods commonly used for representing knowledge in AI systems. 5

Ans 4.

1. Role of Artificial Intelligence in Game Playing and the Minimax Algorithm

Artificial Intelligence plays a significant role in game playing by enabling machines to compete intelligently against humans or other computer systems. Game playing in AI involves designing algorithms that analyze possible moves, anticipate an opponent’s response, and choose the best strategy to win. It provides a platform to test various AI concepts such as search algorithms, reasoning, learning, and decision-making. Games like chess, checkers, tic-tac-toe, and Go have been important benchmarks for AI research.

AI-based game-playing programs simulate human cognitive abilities such as prediction, pattern recognition, and strategy formulation. They use heuristic search, evaluation functions, and probability-based decision-

 

Q5a. Define reasoning in the context of artificial intelligence. Also explain the main steps involved in forward chaining in brief. 5   

1. What are AI Planning Systems? Explain the concept of planning in AI and discuss any two approaches used for effective decision-making and goal achievement. 5

Ans 5.

1. Reasoning and Steps in Forward Chaining

In Artificial Intelligence, reasoning refers to the process of drawing logical conclusions from known facts or data. It enables AI systems to derive new knowledge, make decisions, and solve problems using inference mechanisms. Reasoning can be of different types, including deductive, inductive, and abductive reasoning. Deductive reasoning derives specific conclusions from general facts, while inductive reasoning generalizes from examples. In AI, reasoning helps expert systems and decision-making algorithms simulate human-like logic.

Forward chaining is a data-driven reasoning approach that starts with known facts and applies inference rules to extract

 

Q6a. What is probabilistic reasoning in AI? Explain the role of Bayes’ theorem with prior and posterior probabilities using a real-world example. 5           

1. Compare and explain supervised, unsupervised, and reinforcement learning methods with examples of their practical applications. 5

Ans 6.

1. Probabilistic Reasoning and Bayes’ Theorem with Example

Probabilistic reasoning in AI deals with reasoning under uncertainty by using probability theory to make decisions when complete information is unavailable. It enables AI systems to evaluate possible outcomes and make predictions based on incomplete or uncertain data. Unlike deterministic reasoning, probabilistic reasoning provides a measure of belief in an outcome, allowing systems to handle real-world complexity such as noisy data or ambiguous evidence.

Bayes’ Theorem plays a central role in probabilistic reasoning. It relates the conditional and marginal probabilities of random