PG S1 M.Sc. AI First internal examination, Introduction to Artificial Intelligence, September 2024

 

Solutions – Introduction to Artificial Intelligence

Section A

1. Graphs are collections of vertices (points) connected by edges (line segments), forming paths and cycles. Matching in graphs can be used to identify patterns, find optimal routes, and analyze relationships.

2. Uninformed search techniques, also known as blind search techniques, are a category of search algorithms that operate without any additional information about the goal's location beyond the problem's definition. These algorithms do not have any domain-specific knowledge or heuristic function to guide the search. They explore the search space in a systematic way, generating all possible states and paths to find a solution.

Here are the names of common uninformed search techniques:

1.    Breadth-First Search (BFS)

2.    Depth-First Search (DFS)

3.    Uniform Cost Search (UCS)

4.    Depth-Limited Search (DLS)

5.    Iterative Deepening Depth-First Search (IDDFS)

6.    Bidirectional Search

 

3. Some key approaches to AI that are similar to human behavior:

1. Cognitive Modeling Approach

The cognitive modeling approach aims to create computational models that mimic human cognitive processes. This approach is inspired by psychology and neuroscience, attempting to replicate how humans think, remember, solve problems, and make decisions. The primary goal is to build systems that think like humans by simulating various aspects of human cognition.

  • Examples:
    • Expert Systems: Mimic the decision-making ability of a human expert by using rules and logic derived from human knowledge.
    • Cognitive Architectures: Such as ACT-R (Adaptive Control of Thought-Rational) and Soar, which are designed to model the cognitive processes of the human mind, including memory, learning, and problem-solving.

4. In this approach, the agent uses logical reasoning, planning, and decision-making strategies to choose actions that lead to its goals. It does not necessarily mimic human behavior but instead aims to make the most rational choice in any given situation. This approach is foundational in AI because it provides a clear objective for creating intelligent systems: to act rationally and efficiently in their environments.

5. Best-First Search can be worse than BFS when the heuristic function is misleading or not informative, when there is a risk of heuristic overestimation, or when dealing with deceptive search spaces or uniform spaces where heuristics provide no advantage. In such scenarios, the straightforward, level-wise exploration of BFS can outperform the heuristic-driven approach of Best-First Search.

Section B

7.    Depth-First Search (DFS) is an uninformed search algorithm that explores as far as possible along a branch before backtracking. It uses a stack (or recursion) to keep track of the nodes to visit next.

Advantages:

1.    Memory Efficiency: DFS uses less memory compared to Breadth-First Search (BFS) because it stores only the nodes on the current path, not all nodes at a level.

2.    Can Find Solutions Quickly: In scenarios where the solution is located deep in the tree or graph, DFS can potentially reach it faster if it explores the correct branch early.

3.    Suitable for Infinite Search Spaces: DFS can be used in scenarios with infinite search spaces where BFS would be impractical due to memory constraints.

Disadvantages:

1.    Can Get Stuck in Infinite Loops: If the search space has cycles or an infinite depth, DFS might keep exploring the same nodes or paths indefinitely.

2.    May Not Find the Shortest Path: DFS does not guarantee the shortest path or optimal solution, as it explores nodes without considering the shortest path to the goal.

3.    Deep Branches May Lead to High Time Complexity: If the solution is in a deep branch, DFS might take a long time to reach it, especially if the search space is large.

 

 

8. Heuristic search uses a heuristic function to estimate the cost or distance to the goal, guiding the search process more efficiently than uninformed search methods.

Heuristic Functions:

1.    Euclidean Distance:
Estimates the straight-line distance between two points in a Euclidean space.

2.    Manhattan Distance:
Measures the sum of the absolute differences in the horizontal and vertical distances between two points.

 

9. Solving the Farmer, Fox, Goose, and Cereal Problem Using BFS

1.    State Representation:

o   Each state is a combination of where the farmer, fox, goose, and cereal are (Left or Right).

2.    Initial State:

o   All on the Left side: (Left, Left, Left, Left)

3.    Goal State:

o   All on the Right side: (Right, Right, Right, Right)

4.    BFS Steps:

o   Start with Initial State: Add it to the queue.

o   Explore All Possible Moves:

§  Farmer moves alone or with one item at a time.

o   Check Constraints: Ensure the fox isn't left alone with the goose, and the goose isn't left alone with the cereal.

o   Generate New States: From each state, generate new states based on valid moves.

o   Repeat: Continue exploring each state in the queue until you reach the goal state.

5.    Find Solution:

o   BFS ensures that you explore all possible ways to get to the goal and find the shortest path.

Example Solution Path:

1.    Move the goose across.

2.    Return alone.

3.    Move the fox across.

4.    Return with the goose.

5.    Move the cereal across.

6.    Return alone.

7.    Move the goose across.

 

Section C

10. Evolution of Artificial Intelligence (AI)

1.    Early Foundations (1940s - 1960s):

o   1940s-50s: Conceptual groundwork laid by Alan Turing with ideas like the Turing Test and the concept of a "universal machine."

o   1956: The term "Artificial Intelligence" was coined at the Dartmouth Conference, marking the formal start of AI research. Early AI focused on symbolic reasoning and logic-based problem-solving with programs like the Logic Theorist and General Problem Solver.

2.    Symbolic AI and Expert Systems (1970s - 1980s):

o   1970s: The rise of symbolic AI and expert systems, which used rule-based systems to simulate human expertise. Despite some initial success, these systems faced limitations such as high development costs and difficulty handling complex, real-world data.

o   1980s: The introduction of knowledge-based systems and expert systems like MYCIN showcased practical applications, but the field experienced the first AI winter due to overhyped promises and underwhelming results.

3.    Machine Learning and the Second AI Winter (1980s - 2000s):

o   Late 1980s: Shift towards machine learning approaches, with algorithms like decision trees and support vector machines gaining prominence. Research in neural networks also re-emerged with the rediscovery of the backpropagation algorithm.

o   1990s-2000s: AI faced another period of stagnation, known as the second AI winter, due to slow progress and high expectations not being met.

4.    Deep Learning and Modern AI (2000s - Present):

o   2010s: Major breakthroughs with deep learning, leveraging large datasets and powerful GPUs. Key milestones include AlexNet's success in the 2012 ImageNet competition and AlphaGo's victory over a world champion in 2016. These advances demonstrated AI's capabilities in image recognition, natural language processing, and game-playing.

o   Present: AI is now integrated into various applications, including healthcare, finance, and autonomous vehicles. Research continues to focus on improving ethics, fairness, and working towards Artificial General Intelligence (AGI).

 

11. AND-OR graphs are a type of graph used in AI and problem-solving to represent states and their relationships, particularly in the context of problems that involve both conjunctive (AND) and disjunctive (OR) conditions. They are used to model problems where some conditions must be satisfied together (AND) and others can be satisfied individually (OR).

Structure:

·        Nodes: Represent states or sub-goals.

·        Edges:

o   AND Edges: Indicate that all connected sub-goals must be achieved to satisfy the parent node.

o   OR Edges: Indicate that achieving any one of the connected sub-goals is sufficient to satisfy the parent node.

Advantages

1.    Clarity

2.    Flexibility

3.    Effective

4.    Decomposable

Disadvantages

1.    Complexity

2.    Overhead

3.    Optimization

 

 

 

 

 

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