Introduction to Artificial Intelligence

As a major branch of computer science, Artificial Intelligence (AI) is dedicated to the theory and development of systems that can perform tasks normally requiring human intelligence. This foundational area explores the creation of intelligent agents—machines that can perceive their environment, reason, learn from data, and take actions to achieve specific goals. Core topics include problem-solving through search algorithms, knowledge representation, the principles of machine learning, natural language processing, and an introduction to the ethical considerations and societal impact of building intelligent systems.

1. Foundations of Artificial Intelligence
   1. Defining Artificial Intelligence
      1. Historical Definitions and Evolution
      2. Scope and Boundaries of AI
      3. AI vs. Human Intelligence
      4. AI vs. Traditional Computing
   2. The Turing Test and Intelligence Assessment
      1. Alan Turing's Imitation Game
      2. Test Procedure and Criteria
      3. Historical Context and Significance
      4. Criticisms and Limitations
      5. Alternative Tests
         1. Chinese Room Argument
         2. Lovelace Test
         3. Winograd Schema Challenge
   3. Approaches to AI
      1. Acting Humanly
         1. Behavioral Approach
         2. Cognitive Modeling
         3. Human-Computer Interaction
      2. Thinking Humanly
         1. Cognitive Science Approach
         2. Introspection and Protocol Analysis
         3. Computational Psychology
      3. Acting Rationally
         1. Rational Agent Approach
         2. Decision Theory
         3. Game Theory Applications
      4. Thinking Rationally
         1. Logic-Based Approach
         2. Symbolic Reasoning
         3. Mathematical Foundations
   4. History of Artificial Intelligence
      1. Pre-AI Foundations (Before 1943)
         1. Philosophical Roots
         2. Mathematical Logic
         3. Early Computing Machines
      2. Gestation Period (1943-1955)
         1. McCulloch-Pitts Neuron Model
         2. Turing's Computing Machinery and Intelligence
         3. Early Cybernetics
         4. Shannon's Information Theory
      3. Birth and Early Enthusiasm (1956-1974)
         1. Dartmouth Conference
         2. Logic Theorist and General Problem Solver
         3. Early Neural Networks
         4. LISP Programming Language
         5. Initial Funding and Optimism
      4. First AI Winter (1974-1980)
         1. Lighthill Report
         2. Computational Limitations
         3. Funding Cuts
         4. Reduced Research Activity
      5. Expert Systems Era (1980-1987)
         1. Knowledge-Based Systems
         2. MYCIN and DENDRAL
         3. Commercial Applications
         4. Fifth Generation Computer Project
      6. Second AI Winter (1987-1993)
         1. Expert System Limitations
         2. Market Collapse
         3. Reduced Commercial Interest
      7. Statistical Renaissance (1993-2012)
         1. Machine Learning Focus
         2. Statistical Methods
         3. Internet and Data Availability
         4. Practical Applications
      8. Deep Learning Revolution (2012-Present)
         1. Neural Network Breakthroughs
         2. Big Data and GPU Computing
         3. Modern AI Applications
         4. Current State and Future Directions
   5. Types and Classifications of AI
      1. By Capability Level
         1. Narrow AI (Weak AI)
            1. Definition and Characteristics
            2. Current Examples
            3. Limitations and Scope
         2. General AI (Strong AI)
            1. Theoretical Framework
            2. Research Challenges
            3. Timeline Predictions
         3. Artificial Superintelligence
            1. Hypothetical Scenarios
            2. Potential Benefits
            3. Existential Risks
      2. By Functionality
         1. Reactive Machines
            1. Characteristics
            2. Examples and Applications
         2. Limited Memory Systems
            1. State Representation
            2. Modern AI Applications
         3. Theory of Mind AI
            1. Conceptual Requirements
            2. Current Research
         4. Self-Aware AI
            1. Philosophical Implications
            2. Future Possibilities
      3. By Learning Approach
         1. Symbolic AI
         2. Connectionist AI
         3. Evolutionary AI
         4. Hybrid Systems
   6. Intelligent Agents Framework
      1. Agent Concepts
         1. Agent Definition
         2. Agent vs. Program
         3. Autonomy and Intelligence
      2. Environment Types
         1. Observable vs. Partially Observable
         2. Deterministic vs. Stochastic
         3. Episodic vs. Sequential
         4. Static vs. Dynamic
         5. Discrete vs. Continuous
         6. Single-Agent vs. Multi-Agent
      3. Rationality and Performance
         1. Rational Behavior
         2. Performance Measures
         3. Bounded Rationality
         4. Satisficing vs. Optimizing
      4. PEAS Framework
         1. Performance Measures
         2. Environment Description
         3. Actuators
         4. Sensors
         5. PEAS Examples
      5. Agent Architectures
         1. Simple Reflex Agents
            1. Condition-Action Rules
            2. Limitations
         2. Model-Based Reflex Agents
            1. Internal State
            2. World Model
         3. Goal-Based Agents
            1. Goal Formulation
            2. Planning and Search
         4. Utility-Based Agents
            1. Utility Functions
            2. Decision Theory
         5. Learning Agents
            1. Learning Element
            2. Performance Element
            3. Critic
            4. Problem Generator