Artificial Intelligence (AI) is a branch of computer science that aims to create systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. The foundations of AI lie in disciplines like mathematics, logic, neuroscience, computer engineering, and cognitive science. The history of AI began in the 1950s, evolving through symbolic AI, expert systems, machine learning, and modern deep learning approaches. AI has numerous applications, including speech recognition, autonomous vehicles, healthcare diagnostics, and recommendation systems. Intelligent agents are core to AI, functioning within environments and making rational decisions based on perception, where their behavior is influenced by the environment’s nature and their internal structure.
Solving Problems by Searching is a foundational concept in Artificial Intelligence where intelligent agents find solutions by exploring possible actions. Problem-solving agents are designed to decide what to do by identifying goals and searching for sequences of actions to achieve them. This approach is applied to many example problems such as pathfinding, puzzle-solving, and game playing. The process involves searching for solutions through various strategies, broadly categorized into uninformed search strategies (like BFS and DFS, which have no additional information) and informed search strategies (like A*, which use problem-specific knowledge). Heuristic functions guide informed search by estimating the cost from a given state to the goal, improving efficiency and effectiveness.
Adversarial search is used in game-playing scenarios where multiple agents (players) compete with opposing goals, requiring intelligent decision-making. In such games, optimal decisions aim to maximize a player's advantage while minimizing the opponent's, typically modeled using the Minimax algorithm, which assumes both players play optimally. Alpha-Beta pruning enhances Minimax by eliminating branches that won’t affect the final decision, improving efficiency without compromising accuracy. Constraint Satisfaction Problems (CSPs) involve finding values for variables within specific constraints, such as in puzzles like Sudoku or scheduling problems. CSPs are solved using constraint propagation (to infer variable domains), backtracking search (to explore possible assignments), and by exploiting the problem’s structure (like tree-based or graph-based representations) to improve performance.
Logical agents are intelligent systems that make decisions based on a knowledge base using formal logic. Knowledge-based agents use propositional and first-order logic to represent facts about the world and apply inference rules to derive new knowledge. Propositional logic deals with simple true/false statements, and agents based on it use theorem proving methods to reason. First Order Predicate Logic (FOPL) extends propositional logic by introducing quantifiers and predicates, allowing more expressive knowledge representation. Inference in FOPL includes advanced techniques like unification, forward and backward chaining, and resolution, which enable deeper reasoning compared to propositional logic.
Learning from examples is a core concept in machine learning where systems improve their performance by analyzing data. Forms of learning include supervised, unsupervised, semi-supervised, and reinforcement learning, depending on the availability of labeled data. In supervised learning, the model learns from input-output pairs to make future predictions. One popular supervised learning method is decision trees, which recursively split data based on feature values to classify or predict outcomes. Evaluating and selecting the best hypothesis involves metrics like accuracy, precision, and error rate, while regression and classification with linear models help in predicting continuous values or assigning class labels using linear relationships.
