DCA62M1

Description

SESSION	JULY-AUG 2025
PROGRAM	MCA
SEMESTER	II
COURSE CODE & NAME	DCA62M1

 

 

SET-I

 

Q1. Discuss one real-world multi-agent system, describing how agents cooperate or compete. 10

Ans 1.

Multi-Agent Systems

A multi-agent system (MAS) refers to a computational environment where multiple intelligent agents interact with each other within a shared environment to achieve individual or collective goals. Each agent in such a system is autonomous, capable of decision-making, and may possess incomplete information about the overall system. Multi-agent systems are widely used in real-world applications such as traffic management, stock markets, robotics, healthcare systems, and online marketplaces. The defining feature of MAS is the interaction among agents, which may involve cooperation, competition, negotiation, or

 

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Q2. Explain the concept of state space representation using the 8-puzzle problem. 10

Ans 2.

State Space Representation

State space representation is a fundamental problem-solving approach used in Artificial Intelligence to model complex problems in a structured and systematic manner. In this approach, a problem is described in terms of states, actions, and transitions between states. Each state represents a possible configuration of the problem, while actions define how one state can change into another. The objective of state space representation

 

Q3. Describe how ensemble learning (e.g., Random Forest, AdaBoost) improves model accuracy compared to a single decision tree. 5 + 5  

Ans 3.

Limitations of a Single Decision Tree

A decision tree is a popular machine learning algorithm due to its simplicity and interpretability. It works by recursively splitting data based on feature values to make predictions. While decision trees are easy to understand and implement, they suffer from several limitations that affect their predictive accuracy.

One major drawback of a single decision tree is overfitting. A tree that grows too deep tends to memorize training data, capturing noise instead of general patterns. This leads to poor performance on unseen data. Decision trees are also highly sensitive to small changes in the dataset. A minor variation in training data

 

SET-II

 

 

Q4. Explain the working of the A* algorithm and show how the heuristic function affects path selection. 5 + 5           

Ans 4.

A Algorithm*

The A* algorithm is one of the most widely used informed search algorithms in Artificial Intelligence for solving pathfinding and graph traversal problems. It is designed to find the most optimal path from a given

 

 

 

Q5. Illustrate the alpha-beta pruning process on a minimax tree and show which branches are skipped. 10

Ans 5.

Minimax and Alpha-Beta Pruning

In game-playing artificial intelligence, the minimax algorithm is used to determine the optimal move for a player under the assumption that the opponent also plays optimally. The algorithm explores a game tree consisting of possible moves and counter-moves to evaluate the best possible outcome. However, as the size of the game tree grows, the minimax algorithm becomes computationally expensive. Alpha-beta pruning is an enhancement technique that improves the efficiency of minimax by eliminating

 

Q6. Explain how hill climbing can get stuck in local maxima and how simulated annealing helps overcome it. 5 + 5      

Ans 6.

Hill Climbing and Optimization Problems

Hill climbing is a simple and widely used local search algorithm in Artificial Intelligence for solving optimization problems. The main objective of hill climbing is to find a solution that maximizes or minimizes a given evaluation function. It works by starting from an initial solution and repeatedly moving to a neighboring solution that offers a better value according to the evaluation function. Due to its simplicity, low memory usage, and fast execution, hill climbing is often used in problems where finding an