¾Ã¾ÃÈÈÊÓƵ

Catalog description:

Basic concepts of artificial intelligence (AI) including problem solving, search knowledge representation, and rule-based systems covered with symbolic AI language such as PROLOG or LISP. Application areas (natural language understanding, pattern recognition, learning and expert systems) are explored.

Prerequisite(s):

CSE 274 and (MTH 231 or MTH 331)

Required topics (approximate weeks allocated):

• Artificial intelligence overview (1)
  • AI definition
  • history
  • application areas
  • characterization of task environments
• Problem solving using uninformed search (1.5)
  • review of graphs
  • breadth-first, depth-first and iterative deepening search
  • avoiding repeated states
• Informed search (1.5)
  • priority queues
  • heuristics
  • A* search
  • hill climbing techniques
• Constraint satisfaction problems (1)
  • backtracking search techniques
  • heuristics for efficient backtracking
• Adversarial search (1.5)
  • minimax algorithm
  • alpha-beta pruning
  • probabilistic games
  • case studies
• Knowledge and reasoning (2)
  
  • propositional logic and its semantics
  • first-order logic
  • inference, soundness and completeness
  • unification
• Uncertainty (1.5)
  • Acting under uncertainty
  • Axioms of probability
  • Bayes' rule for combining evidence
  • Bayesian networks
• Machine learning (1.5)
  • learning from observations
  • decision trees
  • ensemble learning
  • training and testing
• Additional topics (2.5)
  • knowledge representation
  • AI programming languages
  • evolutionary computation
  • neural networks
  • reinforcement learning
  • robotics
• Exams/Review (1)

Learning Outcomes:

1. Describe the history of AI
   • Identify the disciplines that support AI
   • Describe the important historical events in AI history
2. Describe the architecture of intelligent agents and their environments
   • Distinguish between the various kinds of agents: simple reflex, goal-based, utility-based
   • Describe the properties of an agent's environment
3. Describe, apply, and implement uninformed and informed search techniques to solve search problems
   • Apply and implement breadth-first, depth-first, uniform-cost, iterative deepening search techniques to search problems
   • Describe and implement A* search
   • Describe the basic theoretical properties of heuristic functions
   • Describe the process of developing heuristics based on relaxing domain constraints
   • Apply and implement common techniques for solving constraint satisfaction problems
4. Implement software capable of playing a competitive game
   • Describe and implement mini-max and alpha-beta search
   • Devise an evaluation function for assessing the game state of a fully observable, deterministic game
   • Identify and apply techniques from other areas of computer science to improve a game-player's performance
5. Describe and use propositional logic and first-order logic
   • Describe and evaluate logic operations
   • Determine if knowledge base (KB) entails a sentence S
   • Apply inference rules to a KB to create new rules
   • Describe forward and backward chaining algorithms
   • Convert a set of logic sentences into normal form
   • Complete resolutions proofs
   • Use first order logic to describe some domain
   • State the time complexity of the various inferencing algorithms
6. Describe, apply, and implement basic machine learning techniques
   • Describe the differences in supervised learning, unsupervised learning, and reinforcement learning
   • Describe, apply, and implement the decision tree learning algorithm
   • Describe an ensemble learning method such as AdaBoost
   • Describe the operation of other elementary classifiers such as Bayesian classifier, perceptron, and the nearest neighbor classifier
7. Describe and apply AI techniques in areas such as: evolutionary computation, neural networks, reinforcement learning, semantic web, fuzzy set theory, uncertainty, and robotics
8. Independently investigate an AI technique and describe, apply, and implement that technique
9. (Graduate ¾Ã¾ÃÈÈÊÓƵs only) Analyze the extensions of standard search algorithms such as the A* search algorithm
10. (Graduate ¾Ã¾ÃÈÈÊÓƵs only) Apply conditional independence for probabilistic inference