(651) Lecture 2 Uninformed Search

Introduction to Search Problems

  • Focus on creating a formalism for agents to plan ahead.

  • Aim is to formalize problems as search problems—resulting in a mathematical abstraction.

  • Allow algorithms to solve problems once they are framed in this abstraction.

Algorithms for Search Problems

  • Overview of the first three algorithms:

    • Depth-First Search (DFS)

      • Explores branches of the search tree as far as possible before backtracking.

      • Uses a stack data structure (or recursion) to keep track of the nodes to explore.

    • Breadth-First Search (BFS)

      • Explores all neighbors of a node before moving to the next level.

      • Utilizes a queue for tracking nodes yet to be explored.

    • Uniform Cost Search (UCS)

      • Expands the least-cost node first, similar to BFS but considers path cost.

      • Implements a priority queue to always explore the most promising path first.

Agents and Planning

  • Definition of agents: entities that identify goals and take actions to achieve them.

  • Example goal: reaching an apple.

  • Importance of planning: agents must evaluate options and determine the best course of action to fulfill goals.

Recap and Future Lectures

  • More algorithms will be introduced in future lectures.

  • The formulations established will allow for a systematic way to solve various planning and search problems in AI.

Introduction to Search Problems

  • Search problems are essential for agents in artificial intelligence (AI) to plan and make decisions effectively.

  • The goal is to create a formalism that enables agents to frame complex issues as search problems, translating them into a mathematical abstraction.

  • This abstraction allows algorithms to systematically solve problems by following established procedural frameworks.

Algorithms for Search Problems

Overview of the First Three Algorithms:

1. Depth-First Search (DFS)

  • Exploration Method: DFS delves deep into the branches of the search tree, progressing along a single path until it can no longer advance. At that point, it backtracks to explore other paths.

  • Data Structure: DFS can be implemented using a stack data structure or recursion, which helps manage the nodes to be explored.

  • Advantages and Disadvantages: It is memory efficient compared to BFS but may get trapped in deep branches, potentially ignoring shorter solutions.

2. Breadth-First Search (BFS)

  • Exploration Method: BFS examines all neighboring nodes at the present depth before moving deeper, ensuring that the shortest path in terms of the number of edges is found.

  • Data Structure: It uses a queue to manage nodes that need to be explored next, maintaining the order of exploration.

  • Advantages and Disadvantages: BFS guarantees the shortest path but can consume significant memory due to the storage of all explored nodes.

3. Uniform Cost Search (UCS)

  • Exploration Method: UCS expands the least-cost node first, similar to BFS, but considers the cumulative cost from the starting point to determine the most promising path.

  • Data Structure: UCS employs a priority queue, which allows it to continually explore the path with the lowest cost first.

  • Advantages and Disadvantages: UCS effectively finds the lowest-cost path but may still face memory limitations and can be slower than heuristics-based methods.

Agents and Planning

  • Definition of Agents: Agents are considered as active entities that have the ability to recognize goals and perform actions to achieve those goals.

  • Example Goal: An example goal for an agent could be successfully navigating a maze to reach an apple, which requires understanding the environment and making decisions.

  • Importance of Planning: Effective planning is critical for agents as it enables them to evaluate their options, assess potential outcomes, and determine the best series of actions to achieve their designated goals, maximizing efficiency and success rates.

Recap and Future Lectures

  • In subsequent lectures, a broader range of algorithms will be introduced, expanding upon the concepts presented and applying them to more complex search scenarios.

  • The formulations and methods discussed will provide a foundational understanding for systematically addressing various planning and search challenges in artificial intelligence, paving the way for both theoretical exploration and practical applications.