# Artificial Intelligence For most of its history, artificial intelligence meant **search**, **logic**, and **reasoning** rather than learning from data — writing down what an agent knows and letting it deduce, plan, and decide what to do. _Figure 001 — Search: expand the frontier node by node until a path to the goal appears. _ The organizing idea is the **rational agent**: something that perceives its environment and acts to maximize its expected performance. Everything else is machinery for choosing the action — searching a space of possibilities, proving a logical consequence, or computing an expected utility under uncertainty. _Figure 002 — Minimax: leaf values back up as min and max to pick the best move. _ Search comes first: cast a problem as states and moves, and algorithms like A* find a path to the goal, guided by a heuristic that estimates the distance still to go. Add an adversary and the same idea becomes the minimax search that plays chess. _Figure 003 — A constraint problem: color the map so no two neighbors match. _ When the world is uncertain, logic gives way to _probability_. A Bayesian network compresses a joint distribution over many variables into a graph of local dependencies, and the same expected-utility calculus, run over time, becomes the Markov decision process that underlies planning and reinforcement learning. _Figure 004 — A Bayesian network: evidence enters and belief flows down the edges. _ These are the ideas that ran from the Logic Theorist to Deep Blue to the probabilistic robotics of self-driving cars — the foundation the modern, learning-driven era of AI was built on top of. _Figure 005 — Resolution: clauses combine and cancel down to the empty clause — a proof. _ --- ## Contents ### 1. Foundations 1. [What Is Artificial Intelligence?](/artificial-intelligence/foundations/what-is-ai) 2. [The Foundations of AI](/artificial-intelligence/foundations/foundations-of-ai) 3. [Intelligent Agents](/artificial-intelligence/foundations/intelligent-agents) 4. [Agent Architectures](/artificial-intelligence/foundations/agent-architectures) ### 2. Search 1. [Uninformed Search](/artificial-intelligence/search/uninformed-search) 2. [Search Strategies Compared](/artificial-intelligence/search/search-strategies-compared) 3. [Informed Search and A*](/artificial-intelligence/search/informed-search) 4. [Heuristic Functions and Memory-Bounded Search](/artificial-intelligence/search/heuristic-functions) 5. [Local Search and Optimization](/artificial-intelligence/search/local-search) 6. [Population and Continuous Search](/artificial-intelligence/search/population-and-continuous-search) 7. [Adversarial Search and Games](/artificial-intelligence/search/adversarial-search) 8. [Games of Chance and Imperfect Information](/artificial-intelligence/search/games-of-chance-and-imperfect-information) 9. [Constraint Satisfaction Problems](/artificial-intelligence/search/constraint-satisfaction) 10. [CSP Search and Structure](/artificial-intelligence/search/csp-search-and-structure) 11. [Search Under Uncertainty](/artificial-intelligence/search/search-under-uncertainty) 12. [Belief-State and Online Search](/artificial-intelligence/search/belief-state-and-online-search) ### 3. Logic and Planning 1. [Logical Agents and Propositional Logic](/artificial-intelligence/logic-and-planning/propositional-logic) 2. [Propositional Inference and Logical Agents](/artificial-intelligence/logic-and-planning/propositional-inference) 3. [First-Order Logic](/artificial-intelligence/logic-and-planning/first-order-logic) 4. [First-Order Logic in Use](/artificial-intelligence/logic-and-planning/first-order-logic-in-use) 5. [Inference in First-Order Logic](/artificial-intelligence/logic-and-planning/inference-and-resolution) 6. [First-Order Resolution](/artificial-intelligence/logic-and-planning/first-order-resolution) 7. [Classical Planning](/artificial-intelligence/logic-and-planning/classical-planning) 8. [Planning Heuristics and GraphPlan](/artificial-intelligence/logic-and-planning/planning-graphs-and-graphplan) 9. [Planning and Acting in the Real World](/artificial-intelligence/logic-and-planning/planning-in-the-real-world) 10. [Planning Under Uncertainty](/artificial-intelligence/logic-and-planning/planning-under-uncertainty) 11. [Knowledge Representation](/artificial-intelligence/logic-and-planning/knowledge-representation) 12. [Reasoning Systems and Default Logic](/artificial-intelligence/logic-and-planning/reasoning-systems-and-defaults) ### 4. Uncertainty 1. [Quantifying Uncertainty](/artificial-intelligence/uncertainty/probability-and-bayes) 2. [Bayes' Rule and Naive Bayes](/artificial-intelligence/uncertainty/bayes-rule-and-naive-bayes) 3. [Bayesian Networks](/artificial-intelligence/uncertainty/bayesian-networks) 4. [Bayesian Networks: Inference and Relational Models](/artificial-intelligence/uncertainty/inference-in-bayesian-networks) 5. [Probabilistic Reasoning over Time](/artificial-intelligence/uncertainty/reasoning-over-time) 6. [Reasoning over Time: Tracking and Data Association](/artificial-intelligence/uncertainty/tracking-and-data-association) 7. [Making Decisions: Utility Theory](/artificial-intelligence/uncertainty/making-decisions) 8. [Markov Decision Processes](/artificial-intelligence/uncertainty/markov-decision-processes) 9. [Decision Analysis: Multi-Attribute Utility and Decision Networks](/artificial-intelligence/uncertainty/decision-networks-and-game-theory) 10. [Game Theory and Mechanism Design](/artificial-intelligence/uncertainty/game-theory-and-mechanism-design) ### 5. Learning 1. [Learning from Examples](/artificial-intelligence/learning/learning-from-examples) 2. [The Theory of Learning and Model Families](/artificial-intelligence/learning/theory-and-model-families) 3. [Learning Probabilistic Models](/artificial-intelligence/learning/probabilistic-learning) 4. [Learning with Hidden Variables: The EM Algorithm](/artificial-intelligence/learning/expectation-maximization) 5. [Reinforcement Learning](/artificial-intelligence/learning/reinforcement-learning) 6. [Reinforcement Learning: Generalization and Policy Search](/artificial-intelligence/learning/generalization-and-policy-search) 7. [Knowledge in Learning](/artificial-intelligence/learning/knowledge-in-learning) 8. [Knowledge-Based Learning: EBL, Relevance, and ILP](/artificial-intelligence/learning/knowledge-based-learning-methods) ### 6. Frontiers 1. [Vision and Perception](/artificial-intelligence/frontiers/vision-and-perception) 2. [Vision: Reconstructing the 3D World](/artificial-intelligence/frontiers/reconstructing-the-3d-world) 3. [Robotics](/artificial-intelligence/frontiers/robotics) 4. [Robotics: Planning and Control](/artificial-intelligence/frontiers/robot-planning-and-control) 5. [Natural Language for AI Agents](/artificial-intelligence/frontiers/natural-language-in-ai) 6. [Language for AI Agents: Grammar, Translation, and Speech](/artificial-intelligence/frontiers/nlp-grammar-translation-and-speech) 7. [Philosophy, Ethics, and the Future of AI](/artificial-intelligence/frontiers/philosophy-and-future) 8. [The Ethics and Future of AI](/artificial-intelligence/frontiers/ai-ethics-and-future)