Introduction to AI and Intelligent Agents
Definition and Scope of AI▼
Artificial Intelligence (AI) focuses on creating machines capable of tasks that require human-like intelligence: reasoning, learning, perception, and autonomous action. The aim is to develop intelligent agents that can adapt and evolve across domains such as problem-solving, natural language understanding and creative tasks.
History and Applications of AI▼
Modern AI began in the mid-20th century with milestones like the 1956 Dartmouth workshop. Since then AI experienced waves of enthusiasm and slower periods.
Today AI is embedded across industries:
- Healthcare: diagnostics from images, outcome prediction, drug discovery.
- Finance: fraud detection, algorithmic trading, personalized advice.
- Transportation: self-driving perception and planning.
- Entertainment: recommendation systems.
- Creativity: art, music and text generation.
Characteristics of Intelligent Agents▼
- Autonomy: operate without constant human oversight.
- Perception: gather information via sensors.
- Rationality: choose actions likely to achieve goals.
- Adaptation: learn and improve over time.
Types of Agents and Environments▼
Agent types include simple reflex, model-based reflex, goal-based and utility-based agents. Environments are classified by observability, determinism, episodic nature, dynamics, discreteness, and whether they are single- or multi-agent.
Agent Architecture▼
An agent's architecture houses sensors, effectors and the agent program (the mapping from perceptions to actions). This can be physical (robot) or purely computational (software agent).
Problem Solving & Search Strategies
Problem-Solving Agents▼
Problem-solving agents remain central to classical AI because they demonstrate how intelligent behavior can be achieved through structured reasoning and systematic search. They form the basis of algorithms for puzzles, navigation systems, robotics, and planning tasks. Furthermore, their design principles continue to influence modern AI systems, bridging traditional search-based reasoning with learning-based approaches. Problem-solving agents are goal-based, rely on systematic search, and serve as the foundation of classical AI. Their architecture, types, and objectives highlight their enduring role in solving structured problems and laying the groundwork for advanced intelligent systems.
Example Problems and Approaches▼
Examples: 8-puzzle, 8-queens. A problem is defined by an initial state, goal state, available actions and a path cost. The objective is to find a lowest-cost path.
Uninformed Search Strategies▼
- BFS: level-by-level, complete and optimal for unweighted graphs.
- DFS: deep exploration, memory-efficient but not optimal and may loop.
- Iterative Deepening: repeated depth-limited DFS; combines BFS completeness with DFS memory efficiency.
Informed Search Strategies▼
Heuristic-guided search uses an evaluation function to estimate progress toward the goal.
- Greedy Best-First: expands nodes closest to the goal per heuristic; fast but not guaranteed optimal.
- A* Search: uses f(n)=g(n)+h(n); optimal and complete when heuristic is admissible and consistent.
- Heuristics: Good heuristics trade computation for guidance and dramatically improve performance.
Hill Climbing & Simulated Annealing▼
Local search moves from the current state to neighboring states that improve the objective. Hill climbing is greedy and can get trapped; simulated annealing probabilistically accepts worse moves to escape local optima.
Constraint Satisfaction Problems (CSPs)▼
CSPs (e.g., Sudoku) search for variable assignments satisfying constraints. Backtracking with heuristics (most constrained variable, least constraining value) is commonly used.
Game Playing▼
- Minimax: recursive adversarial search for two-player zero-sum games.
- Alpha-Beta Pruning: eliminates branches that cannot change the outcome.
- Stochastic/Partially Observable Games: require methods handling chance and hidden information.
Knowledge Representation & Reasoning
Propositional & First-Order Logic▼
Propositional Logic handles true/false sentences; First-Order Logic adds objects, predicates and quantifiers for richer representation.
Syntax, Semantics & Inference▼
Syntax defines well-formed sentences; semantics define meaning/truth; inference derives new facts (e.g., Modus Ponens).
Knowledge-based Agents▼
Knowledge-based agents maintain a KB and use reasoning to decide actions. The Wumpus World exemplifies logical deduction in action.
Logic Programming using Prolog▼
Prolog is declarative: programmers state facts and rules; the engine uses inference to answer queries (e.g., family relations).
Forward & Backward Chaining▼
Forward chaining is data-driven; backward chaining is goal-driven (used by Prolog).
Resolution & Ontological Engineering▼
Resolution forms the basis of automated theorem proving. Ontological engineering builds domain ontologies to structure large-scale KBs.
Applications of AI & Multi-Agent Systems
Coming Soon
Natural Language Processing (NLP)▼
NLP includes machine translation, information retrieval and extraction, enabling machines to process human language.
Robotics▼
Robotics uses AI for perception, planning and motion control in physical systems.
Speech Recognition▼
Speech recognition converts spoken language into machine-readable text (e.g., Siri, Alexa).
Software Agents & Multi-Agent Systems▼
MAS study interactions among agents: architecture, communication, trust, reputation and negotiation.
Explainable AI (XAI)▼
XAI focuses on making black-box models interpretable. Techniques like LIME and SHAP provide explanations to build trust and enable debugging.
Uncertainty & Learning Techniques
Coming Soon
Introduction to Uncertainty & Probabilistic Reasoning▼
Agents use probabilities when the world is uncertain, expressing beliefs instead of binary truth values.
Bayes' Rule & Bayesian Networks▼
Bayes' Rule updates hypothesis probabilities given evidence. Bayesian networks encode dependencies between random variables for diagnosis and prediction.
Fuzzy Logic▼
Fuzzy logic allows degrees of truth (0–1) to model imprecise concepts like "tall" or "hot".
Neural Networks (Basics)▼
Neural networks are interconnected neuron layers. The perceptron is the simplest model; backpropagation trains multi-layer networks by propagating errors and updating weights.
Fundamentals of Machine Learning▼
ML enables models to learn from data: supervised (classification/regression) and unsupervised (clustering/dimensionality reduction).