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Course Outline

Overview of Artificial Intelligence

  • Intelligent Agents

Problem-Solving Methodologies

  • Search-Based Problem Solving
  • Advanced Search Techniques
  • Adversarial Search Strategies
  • Constraint Satisfaction Approaches

Knowledge Representation and Reasoning

  • Logical Agent Architectures
  • First-Order Logic Fundamentals
  • Inference Mechanisms in First-Order Logic
  • Classical Planning Frameworks
  • Planning and Execution in Dynamic Environments
  • Knowledge Representation Structures

Reasoning Under Uncertainty

  • Quantification of Uncertainty
  • Probabilistic Inference
  • Temporal Probabilistic Reasoning
  • Decision-Making Processes: Simple Cases
  • Decision-Making Processes: Complex Cases

Machine Learning Systems

  • Example-Based Learning
  • Integrating Knowledge into Learning Models
  • Probabilistic Model Learning
  • Reinforcement Learning Algorithms

Communication, Perception, and Action

  • Natural Language Processing
  • Natural Language for Interagency and Public Communication
  • Perceptual Systems
  • Robotics Applications

Conclusions

  • Philosophical Underpinnings
  • The Current State and Future Trajectory of AI for Government and Society

Requirements

A foundational understanding of computing principles, biology, mathematics, and physics is required for government positions.
 7 Hours

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