Core AI Concepts

Core AI Concepts

Before building AI agents, you need to understand the foundational concepts that power them.

Search Algorithms

Many AI problems can be framed as search problems — finding a path from an initial state to a goal state.

• Breadth-First Search (BFS): Explores all neighbors before going deeper. Guarantees shortest path.
• Depth-First Search (DFS): Explores as deep as possible before backtracking. Memory efficient.
• A* Search: Uses a heuristic to guide search toward the goal. Optimal and efficient.

# A* search pseudocode
def a_star(start, goal, heuristic):
    open_set = PriorityQueue()
    open_set.put(start, priority=heuristic(start, goal))
    came_from = {}
    g_score = {start: 0}
    
    while not open_set.empty():
        current = open_set.get()
        if current == goal:
            return reconstruct_path(came_from, current)
        
        for neighbor in get_neighbors(current):
            tentative_g = g_score[current] + cost(current, neighbor)
            if tentative_g < g_score.get(neighbor, float('inf')):
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g
                f_score = tentative_g + heuristic(neighbor, goal)
                open_set.put(neighbor, priority=f_score)
    
    return None  # No path found

Knowledge Representation

How agents store and organize information about the world:

• Ontologies: Formal definitions of concepts and relationships
• Semantic networks: Graph-based knowledge structures
• Knowledge graphs: Used by Google, Wikidata, enterprise systems

Logical Reasoning

How agents draw conclusions from known facts:

• Propositional logic: Simple true/false statements
• First-order logic: Variables, quantifiers, predicates
• Inference: Deriving new facts from existing knowledge

Planning

How agents decide on a sequence of actions to achieve a goal:

• Classical planning: Known initial state, deterministic actions
• Conditional planning: Handles uncertainty
• Hierarchical planning: Break complex tasks into subtasks

Key Takeaways

• Search, knowledge representation, reasoning, and planning are the classical AI foundations
• Modern AI agents combine these with machine learning
• Understanding these concepts helps you design better agent architectures