AI UT SOLVED

Q1.Explain artificial intelligence and its applications. CO1

Artificial Intelligence (AI) is a field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. It involves developing algorithms and models that enable machines to learn, reason, and make decisions based on data. AI aims to mimic human cognitive abilities such as problem-solving, pattern recognition, language understanding, and decision-making.

Healthcare: Early disease detection, personalized treatment plans, robotic surgeries, virtual health assistants.
Finance: Fraud prevention, robo-advisory services, algorithmic trading, customer service chatbots.
Retail & E-commerce: Personalized product recommendations, fraud detection, inventory management.
Marketing & Advertising: Target advertising, predictive analytics, social media management.
Education: Intelligent tutoring systems, adaptive learning platforms, plagiarism detection.
Manufacturing & Industrial Automation: Quality control inspections, robotics process automation, energy consumption reduction.
Transportation & Logistics: Autonomous vehicles, route planning, fleet management.
Agriculture: Crop yield estimation, precision agriculture techniques, livestock health monitoring.
Media & Entertainment: Content generation, recommendation engines, virtual reality environments.
Security & Defense: Cybersecurity threat detection, facial recognition software, unmanned aerial vehicles.

Q2.Explain PEAS descriptors & give PEAS descriptors for a robot meant for cleaning the house. CO1

PEAS descriptors are used to characterize an intelligent agent in the field of Artificial Intelligence. PEAS stands for Performance measure, Environment, Actuators, and Sensors. Let's break down the PEAS descriptors for a robot meant for cleaning the house:

Performance measure: This describes how the success of the robot's task is measured. In the case of a cleaning robot, the performance measure could be the cleanliness level of the house, such as the percentage of surfaces that are free from dirt and dust.
Environment: This defines the context or surroundings in which the robot operates. For a cleaning robot, the environment would be the house, including various rooms, furniture, walls, floors, and objects that need to be cleaned.
Actuators: These are the physical components or mechanisms that the robot uses to perform actions in the environment. In the case of a cleaning robot, actuators could include brushes, vacuum cleaners, mops, and other tools that enable the robot to physically clean surfaces and objects.
Sensors: These are the input devices that allow the robot to perceive and gather information about its environment. For a cleaning robot, sensors may include cameras or vision systems to detect dirt and objects, touch sensors to detect obstacles or walls, and possibly even chemical sensors to identify stains or spills.

PEAS Descriptors for a Cleaning Robot:

Performance measure: The percentage of surfaces that are clean and free from dirt and dust.
Environment: A residential house with various rooms, furniture, walls, floors, and objects.
Actuators: Brushes, vacuum cleaners, mops, and other tools for physical cleaning.
Sensors: Cameras or vision systems to detect dirt and objects, touch sensors to detect obstacles or walls, and possibly chemical sensors to identify stains or spills.

Q3.Explain defination of Agent and give basic building blocks of different types of agent.. CO1

An agent can be defined as an entity that perceives its environment through sensors, acts upon the environment via actuators, and pursues a set of objectives or goals guided by predefined rules, learned behaviors, or AI techniques. Basic building blocks of various kinds of agents include:

Simple Reflex Agents: Based on condition-action rules (if sensor input = x, then action y); no internal memory or long-term reasoning used.
Model-Based Reflex Agents: Use internal representations (models) of the world to make decisions considering past observations and future expectations.
Goal-Based Agents: Pursue specific goals determined by user preferences or environmental conditions.
Utility-Based Agents: Optimize utility functions representing subjective satisfaction levels derived from attaining desired outcomes.
Learning Agents: Improve performance based on experience gathered over time.

Q4.Explain uninformed search with BFS & DFS & gives comparision between them CO03

Uninformed search algorithms, such as Breadth-First Search (BFS) and Depth-First Search (DFS), are used to explore or traverse a search space without any specific information about the location of the goal state. Here's an explanation of BFS and DFS, along with a comparison between the two:

Breadth-First Search (BFS):

BFS explores the search space in a breadth-wise manner, i.e., it expands all nodes at the current depth level before moving on to nodes at the next depth level.
It uses a queue data structure to maintain the order of node expansion. The nodes are stored in the order they are encountered, and the first-in, first-out (FIFO) principle is followed.
BFS guarantees finding the shortest path to the goal state if the edges in the search space have uniform costs.
It may require a lot of memory as it needs to store all the nodes at each depth level.

Depth-First Search (DFS):

DFS explores the search space in a depth-wise manner, i.e., it explores a path as far as possible before backtracking.
It uses a stack data structure (implemented using recursion or an explicit stack) to keep track of the nodes to be explored.
DFS does not guarantee finding the shortest path to the goal state. It may find a long path before finding a shorter one.
It requires less memory compared to BFS as it only needs to store the nodes along the current path being explored.

Comparison between BFS and DFS:

Completeness: BFS is complete, meaning it is guaranteed to find a solution if one exists, provided that the search space is finite and there are no infinite loops. DFS is not complete as it can get stuck in infinite loops or go down an infinite path.
Optimality: BFS is optimal when the cost of each step is the same because it guarantees finding the shortest path. DFS is not optimal as it may find a longer path before finding a shorter one.
Memory Usage: BFS requires more memory compared to DFS because it needs to store all the nodes at each depth level. DFS requires less memory as it only needs to store the nodes along the current path.
Time Complexity: In terms of time complexity, both BFS and DFS have a time complexity of O(b^d), where b is the branching factor and d is the depth of the solution. However, in practice, BFS may be slower than DFS due to the larger number of nodes to be expanded.

In summary, BFS guarantees finding the shortest path, is complete, but can be memory-intensive. DFS, on the other hand, is not complete or optimal, but it requires less memory. The choice between BFS and DFS depends on the specific problem, the available resources, and the desired properties of the search algorithm.

Q5.Explain informed search, Define Heuristic function & give example of heuristic function for 8-puzzle problem CO2

Sure, here's a pointwise explanation of informed search, heuristic function, and an example of a heuristic function for the 8-puzzle problem:

Informed Search:

Informed search is a type of search algorithm that uses additional information about the problem to guide its search for the optimal solution.
Unlike uninformed search algorithms, which explore all possible paths to find the solution, informed search algorithms use heuristics or other domain-specific knowledge to guide their search.
Informed search algorithms are typically more efficient than uninformed search algorithms because they can focus on promising paths and avoid exploring unpromising ones.

Heuristic Function:

A heuristic function is a function that estimates the cost of reaching the goal from a given state.
The heuristic function is used to guide the search by prioritizing states that are closer to the goal.
The heuristic function is typically defined as follows: h(n) = estimated cost of reaching the goal from state n
The heuristic function is used to estimate the cost of reaching the goal from a given state. The estimated cost is then used to guide the search by prioritizing states that are closer to the goal.

Example of Heuristic Function for 8-Puzzle Problem:

In the 8-puzzle problem, the goal is to arrange the tiles in a 3x3 grid so that they are in the correct order.
A heuristic function for this problem could be the number of tiles that are in the wrong position.
This heuristic function would estimate the cost of reaching the goal from a given state by counting the number of tiles that are in the wrong position.
The estimated cost would then be used to guide the search by prioritizing states that have fewer tiles in the wrong position.

Q6.What are the problems/frustrations that occur in hillclimbing technique? Illustrate with an example.CO2

Hill climbing, a simple optimization algorithm, is prone to several issues that can affect its effectiveness:

Local Optima: The algorithm tends to get stuck in local optima, unable to reach the global optimum if the search space is rugged.
Plateaus: When the algorithm reaches a flat region (plateau) with little to no change in the objective function, progress slows down significantly.
Ridges: Similar to plateaus, ridges are regions where the algorithm moves slowly because the gradient is shallow.
Initial Solution Dependency: The algorithm's performance can heavily depend on the initial solution. A poor initial guess may lead to suboptimal results.

Example:

Let's consider an example where hill climbing is used to optimize a function with multiple local optima. Suppose we want to find the global maximum of the function f(x) = x^2 - 10x + 24.

Initial Solution: Let's start hill climbing from x = 2. The algorithm calculates the gradient, moves uphill, and reaches x = 5.
Local Optimum: At x = 5, the algorithm gets stuck as it perceives this point as the peak due to the local gradient. However, the global maximum is at x = 5.
Failure to Reach Global Optimum: Since hill climbing only considers local improvements, it fails to explore other regions and remains trapped at the local optimum.

In this example, the hill climbing algorithm fails to reach the global maximum due to the presence of local optima, showcasing one of the frustrations encountered in this technique.

Q7.Explain Best First Search & A* Search & give comparision between them. CO3

Best First Search and A* Search are both algorithms used in the field of artificial intelligence for solving problems like pathfinding, graph traversal, and optimization. Here is an explanation of each algorithm followed by a comparison between Best First Search and A* Search:

Best First Search:

Best First Search is an informed search algorithm that selects the most promising node for expansion based on a heuristic evaluation function.
It uses a priority queue to select the node with the lowest heuristic value, indicating the most likely path to the goal.
Best First Search is not guaranteed to find the optimal solution but focuses on exploring the most promising paths first.
It is memory-intensive as it needs to store all generated nodes until the goal is reached or the search is terminated.

A Search*:

A* Search is also an informed search algorithm that combines the cost of the path from the start node with a heuristic estimate of the remaining cost to the goal.
It evaluates nodes based on the sum of the cost-so-far and the heuristic value, known as the f(n) value.
A* Search guarantees finding the optimal path if certain conditions are met, such as having an admissible heuristic (never overestimates the actual cost).
It is more computationally efficient than Best First Search in finding the optimal path while still leveraging heuristic information.

Comparison:

Completeness:

Best First Search: Not guaranteed to be complete as it may not find a solution if it gets stuck in a loop or a non-terminating path.
A* Search: Complete if the heuristic function is admissible, ensuring it will always find the optimal solution if one exists.

Optimality:

Best First Search: Does not guarantee finding the optimal solution, as it focuses on the most promising path without considering the total cost.
A* Search: Guarantees finding the optimal solution if the heuristic is admissible and consistent, considering both path cost and heuristic estimate.

Memory Usage:

Best First Search: Can be memory-intensive, especially in large search spaces, as it stores all generated nodes until the goal is reached.
A* Search: Can be more memory-efficient than Best First Search due to its focus on the optimal path, potentially reducing the number of nodes stored in memory.

Performance:

Best First Search: May explore non-optimal paths first, leading to longer search times and potentially suboptimal solutions.
A* Search: More efficient in finding the optimal path due to its combination of path cost and heuristic estimate, leading to faster convergence to the goal.