Informed Search Strategies in Artificial Intelligence

Unlike blind search methods, informed search strategies uses problem-specific knowledge and heuristics to guide the search process in promising directions. It results in significantly faster search and reduce the computational resources needed to find quality solutions.

Informed search strategies have become an essential component of modern AI systems for different kinds of tasks like path planning, scheduling, game playing, and optimization. Reliable search capabilities are important in every field where AI is deployed. This article provides a comprehensive overview of informed search strategies in artificial intelligence, different types of algorithms, advantages and disadvantages of informed searches.

What is Informed Search in Artificial Intelligence?

Informed search means using problem-specific knowledge to make search more directed and efficient compared to uninformed search methods. The key idea is to use heuristics to evaluate which nodes or states to expand next during search. It helps in exploration of more promising areas of the search space.

In contrast, uninformed search algorithms like breadth-first search and depth-first search expand nodes blindly with no sense of direction. They treat all nodes alike during exploration. These are simple to implement, but are ineffective for large and complex spaces as they expend resources examining unnecessary nodes.

Informed search solves this issue by incorporating heuristics. The heuristics may estimate the shortest path cost, evaluate state proximity to the goal, or provide other relevant measures to rank node desirability. This domain knowledge helps prune away unproductive sections and drive faster convergence.

Heuristic Functions and Their Role in Informed Search

Heuristic functions are at the heart of guiding informed search algorithms towards good solutions. A heuristic provides a measure of the estimated cost or distance between the current node and goal node. This allows ranking and selective expansion of the most promising nodes first.

Well-designed heuristics can dramatically improve search performance. But heuristics also introduce challenges as they rely on human-crafted domain knowledge which may be suboptimal. Key considerations in using heuristics for informed search are mentioned below:

• Admissibility – The heuristic should not overestimate the actual minimum cost to reach the goal. Overestimates could mislead the search. Admissible heuristics guarantee optimality.
• Consistency – The heuristic value should decrease monotonically as the search expands nodes getting closer to the goal. Inconsistent heuristics can cause issues like looping.
• Efficiency – Computing heuristic values should not be too expensive. Simple heuristics tend to scale better than complex ones.
• Domain-specific – Heuristics designed for a problem are more informative than generic ones. But problem-specific heuristics lack reusability across applications.
• Accuracy – The heuristic should closely approximate actual goal distance as much as possible. More accurate heuristics yield more effective guidance.

Popular Informed Search Algorithms

Many artificial intelligence search algorithms use heuristics to guide their research. Some important informed search algorithms are:

A* Search

A* Search is a method to find the shortest path from a starting point to a destination. Imagine you are in a maze, and you need to find the quickest way to the exit. A* Search can help you do that.

Here’s how it works:

1. Start Where You Are: You begin at the starting point and consider all the paths that lead from it.
2. Score Each Path: You give each path a score. The score is made of two parts:
   • How far you’ve already traveled (say, the number of steps from the start).
   • An estimate of how far you still need to go to reach the destination.
3. Pick the Best Path: You choose the path with the lowest score because it’s probably the quickest way.
4. Repeat: You keep doing steps 1-3 with each new spot until you reach the destination.

The clever part about A* Search is the way it guesses the remaining distance. It uses some information about the layout of the maze to make a good guess. This guess helps it find the shortest path more quickly than just trying every possible way.

It’s like having a friend at a high point above the maze who guides you towards the exit. By picking the best path every time, you get to the destination quicker. That’s what A* Search does, and it’s used in various areas like computer games, robotics, and maps on your phone.

Greedy best-first focuses solely on heuristic value for node selection, expanding the one closest to the goal. This extreme exploitation of heuristics yields fast search on simple problems. But lack of exploration makes it prone to getting stuck in suboptimal areas on complex spaces.

So greedy search works well when the heuristics accurately estimate global distance. For imperfect heuristics, balancing greediness vs exploration becomes vital.

Python Code Example of A* Search

Here’s a simple example of A* Search implemented in Python. The code will find the shortest path from the start to the goal in a grid-based maze.

import heapq

def heuristic(a, b):
    return abs(a[0] - b[0]) + abs(a[1] - b[1])

def a_star_search(grid, start, goal):
    neighbors = [(0, 1), (1, 0), (0, -1), (-1, 0)]
    priority_queue = [(0, start)]
    came_from = {start: None}
    cost_so_far = {start: 0}

    while priority_queue:
        _, current = heapq.heappop(priority_queue)

        if current == goal:
            break

        for dx, dy in neighbors:
            next = (current[0] + dx, current[1] + dy)
            if next[0] < 0 or next[0] >= len(grid) or next[1] < 0 or next[1] >= len(grid[0]) or grid[next[0]][next[1]] == 1:
                continue
            new_cost = cost_so_far[current] + 1
            if next not in cost_so_far or new_cost < cost_so_far[next]:
                cost_so_far[next] = new_cost
                priority = new_cost + heuristic(goal, next)
                heapq.heappush(priority_queue, (priority, next))
                came_from[next] = current

    path = []
    current = goal
    while current != start:
        path.append(current)
        current = came_from[current]
    path.append(start)
    path.reverse()
    return path

grid = [
    [0, 0, 0, 0, 1],
    [1, 1, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 1, 1, 1, 0],
    [0, 0, 0, 0, 0]
]
start = (0, 0)
goal = (4, 4)

path = a_star_search(grid, start, goal)
print("Path:", path)

Explanation of the grid:

• 0 means a cell is walkable.
• 1 means a cell is blocked.

The given code will output the path from the top-left corner (0, 0) to the bottom-right corner (4, 4) of the grid.

Visualizing the path:

S - - # G
# # - # -
- - - # -
- # # # -
- - - - -

The symbols represent:

• S: Start
• -: Open path
• #: Wall or obstacle
• G: Goal

The above code will find the shortest path avoiding the obstacles to reach the goal from the start.

Iterative Deepening A* (IDA*)

Iterative Deepening A* is a search algorithm used to find the shortest path between a starting point and a destination. It’s a combination of two other methods: A* and Iterative Deepening Depth-First Search (IDDFS). I

DA* works by repeatedly running a depth-first search with a gradually increasing cost limit. Initially, the cost limit is set to the heuristic estimate of the start to the goal. If a solution is not found within that limit, the search is run again with a higher limit. This process continues until a solution is found.

IDA* is mostly used when memory space is limited, as it doesn’t need to store all the paths, just the current one.

Iterative Deepening A* Python Code Example

Here’s a simple example of IDA* in a 3×3 grid to find a path from the top-left to the bottom-right corner:

def heuristic(x, y, goal):
    return abs(goal[0] - x) + abs(goal[1] - y)

def search(path, grid, g, threshold, goal):
    node = path[-1]
    x, y = node
    f = g + heuristic(x, y, goal)
    if f > threshold:
        return f
    if (x, y) == goal:
        return 'FOUND'
    min_cost = float('inf')
    for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:
        nx, ny = x + dx, y + dy
        if nx < 0 or nx >= len(grid) or ny < 0 or ny >= len(grid[0]) or grid[nx][ny] == 1 or (nx, ny) in path:
            continue
        path.append((nx, ny))
        grid[nx][ny] = 2
        t = search(path, grid, g + 1, threshold, goal)
        if t == 'FOUND':
            return 'FOUND'
        if t < min_cost:
            min_cost = t
        path.pop()
        grid[nx][ny] = 0
    return min_cost

def ida_star(grid, start, goal):
    threshold = heuristic(start[0], start[1], goal)
    path = [start]
    while True:
        t = search(path, grid, 0, threshold, goal)
        if t == 'FOUND':
            return path
        threshold = t

grid = [
    [0, 0, 0],
    [1, 0, 1],
    [1, 0, 0]
]
start = (0, 0)
goal = (2, 2)
path = ida_star(grid, start, goal)
print("Path:", path)

This code finds a path from (0, 0) to (2, 2) in the grid and avoid cells with 1, and will print the path as a list of coordinates.

Beam Search

Beam Search is also an informed search strategy that’s kind of like looking for something with a flashlight that has a narrow beam. Imagine you are trying to find the best path in a maze, but you can only look at a few paths at a time, not all of them.

Here’s how beam search algorithm works:

1. Start at the Beginning: You look at the paths that begin from the starting point.
2. Pick a Few Good Ones: Out of all the paths you can see, you only keep a fixed number of the best ones. This number is called the “beam width.”
3. Repeat: You then look at the paths coming out from these best ones and again only keep a fixed number of the best. You keep doing this until you find the destination or run out of paths.

This algorithm can be much faster but might miss the absolute best path since you’re not looking at everything.

Python Code Example of Beam Search

Here’s a simple code example:

def beam_search(graph, start, goal, beam_width):
    # Initialize the beam with the start node
    beam = [(start, [start])]
    
    while beam:
        # Store the next level of nodes
        new_beam = []
        
        # Iterate through each node in the current beam
        for node, path in beam:
            # Check neighbors
            for neighbor in graph[node]:
                # Create a new path to this neighbor
                new_path = list(path)
                new_path.append(neighbor)
                
                # Check if the goal is reached
                if neighbor == goal:
                    return new_path
                
                new_beam.append((neighbor, new_path))
        
        # Sort the new beam by some heuristic (optional) and select the top beam_width paths
        beam = sorted(new_beam, key=lambda x: heuristic(x[0], goal))[:beam_width]

    return None

graph = {
    'A': ['B', 'C'],
    'B': ['D', 'E'],
    'C': ['F'],
    'D': ['G'],
    'E': ['G'],
    'F': [],
    'G': []
}
start = 'A'
goal = 'G'
beam_width = 2

path = beam_search(graph, start, goal, beam_width)
print("Path:", path)

Suppose we have a heuristic function that provides some way to guess how good each path is (the details depend on the specific problem). This code tries to find a path from ‘A’ to ‘G’ in the given graph with a beam width of 2. The output might be:

Path: ['A', 'B', 'D', 'G']

This tells you the path Beam Search found from ‘A’ to ‘G’. Because it’s not looking at every possible path, it might not find the best path if there’s a better one that was outside the “beam” at some point. But it can be much faster, especially if the number of possible paths is very large.

Advantages of Informed Search Strategies

Informed search strategies helps in solving problems orders of magnitude faster as compared to blind search.

These are main advantages of informed search strategies:

• Faster convergence – By expanding promising nodes first based on heuristics, informed search prunes large unproductive areas of the state space. This focused search leads to exponentially faster goal discovery.
• Optimal solutions – Algorithms like A* can guarantee optimality given admissible heuristics. This combined power of speed and accuracy is important for mission-critical applications.
• Scalability – The ability to leverage heuristics lets informed search tackle much bigger problems with large search spaces and higher complexity.
• Efficiency – Reduced nodes expanded means informed search uses computational resources very economically. This enables applications on resource-constrained devices.
• Directed probing – Heuristics guide exploration into the most relevant promising corners of the space. This qualitative improvement in search is invaluable for complex real-world problems.
• Tractability – Many hard combinatorial problems like game playing become practically solvable only using informed search. The heuristics tame complexity.

Disadvantages of Informed Search

Informed search algorithms also have some disadvantages.

• Suboptimal heuristics – Bad heuristics can grossly misrepresent actual distances and mislead search. This degrades performance greatly.
• Design complexity – Crafting high-quality heuristics requires extensive domain expertise and insight. This makes scaling informed search to new problems difficult.
• Overhead cost – Computing complex heuristic values at each node adds overhead to the search process, slowing it down. Simpler heuristics are more efficient.
• No guarantee of optimality – Unlike algorithms like A*, greedy searches will settle for suboptimal solutions because they get trapped following misleading heuristics.
• Lack of exploration – Over-reliance on exploitation of heuristics can prevent discovery of novel solutions. Balancing exploration is key.
• Problem specificity – Heuristics designed for one problem usually don’t transfer over to new domains. Reusability is limited.

Real-World Applications of Informed Search in AI

Informed search drives solutions for various real-world problems:

• Path planning – Applications like self-driving cars heavily use algorithms like A* with spatial heuristics for navigation and route planning.
• Scheduling – Heuristics help optimize constrained scheduling of machines, vehicles, personnel etc. Shorter makespans reduce costs.
• Game playing – Game-specific heuristics are indispensable for playing games like chess at scale human level and beyond. They estimate value of moves and positions.
• Constraint satisfaction – Solutions to constraint problems like bin packing and design layout leverage informed search with domain heuristics to explore large configuration spaces.
• Bioinformatics – Protein folding and drug discovery applications employ informed search with physics and chemistry-based heuristics to model molecular interactions.
• Network optimization – Heuristics that quantify traffic, reliability and other attributes guide informed network routing algorithms to improve performance.
• Machine translation – Beam search with linguistic heuristics produces higher quality translations by maintaining multiple candidate translations at each step rather than picking just one.

Combining Informed and Uninformed Search

Blending informed and uninformed search strategies can improve robustness. Uninformed methods like breadth-first search provide safe exploration, whereas heuristics guide exploitation.

One effective hybrid approach is bounded cost search. It combines A* heuristics with depth/cost limits from iterative deepening. This expands nodes informedly up to the limit, before expanding the lowest cost frontier node.

Such combinations counter issues like getting stuck in local optima when using just greedy search. The balance depends on factors like search space structure, optimality needs etc. But hybridizing appropriate search paradigms tailored to the problem offers significant advantages.

Comparison between Informed and Uninformed Search Strategies

Criteria	Informed Search	Uninformed Search
Exploration Efficiency	High	Low
Optimality	Guaranteed if admissible	Not guaranteed
Heuristic Dependency	Requires domain-specific knowledge	Domain-independent
Memory Consumption	Moderate to high	Low

Below we summarize the key differences between informed and uninformed search strategies:

• Informed algorithms use heuristic guidance whereas uninformed methods explore blindly with no notion of direction.
• Informed search is exponentially faster but dependent on heuristic quality. Uninformed techniques are simpler and more robust but scale poorly.
• Algorithms like A* find optimal solutions given good heuristics. Uninformed methods often provide suboptimal results.
• Informed methods require domain expertise to craft useful heuristics. Uninformed search is generic requiring no customization.
• Informed search exploits heuristics to minimize search effort. Uninformed techniques purely explore without any notion of exploiting promising areas.
• Informed search is narrow but deep, uninformed search is broad but shallow. Hybrid techniques combine the benefits of both.

Enhancing Informed Search with Machine Learning

An exciting modern research direction is using machine learning to improve informed search heuristics. Rather than manually designing heuristics, they can be learned automatically from experience and examples.

The techniques are:

• Supervised learning – Regression models predict heuristic values from state features. Neural networks are especially good for approximating complex heuristics.
• Reinforcement learning – Trial and error combined with Q-learning refines heuristics that maximize long-term cumulative reward.
• Imitation learning – Heuristics are learned by analyzing and imitating expert demonstrations of good human play on games like Go.
• Meta-heuristics – Generic heuristic models are adapted to new problems via transfer learning. This improves reusability.

Ethical Considerations in AI Informed Search

The incorporation of heuristics into AI search introduces potential ethical concerns:

• Bias – Heuristics designed manually may unintentionally encode harmful societal biases and unfairly discriminate against certain groups.
• Explainability – Learned neural heuristics behave more like black boxes lacking interpretability compared to simple handcrafted rules.
• Misuse – Adversaries could exploit heuristic guidance to more efficiently search for harmful solutions like cyberattacks.
• Job loss – Highly optimized informed search reduces need for human domain expertise and labor in some tasks like routing or scheduling.

Future Trends in Informed Search Strategies

• Real-time heuristics – Exploiting live data like traffic for more accurate time-dependent heuristics for applications like ride-sharing and delivery.
• Learned heuristics – Reducing heuristic design time via ML while making them customizable across problems.
• Meta-heuristics – Having reusable general heuristic models adaptable to new domains via transfer learning and few-shot techniques.
• Memory-based methods – Caching past results allows reusing priors during search rather than recomputing heuristics, as in Monte Carlo Tree Search.
• Holistic hybrid search – Rather than isolated informed and uninformed phases, having a more integrated blend tailored adaptively based on search stage and problem structure.
• Multi-agent search – Effective heuristics and protocols for cooperative informed search by large teams of distributed AI agents.

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