Typically offered only during during the fall semester.

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 or equivalent and MTH 231.

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)

Graduate students:

Students enrolled in CSE 586 will be given additional readings and/or assignments.

Learning Outcomes:

1: Describe the history of AI

1.1: Identify the disciplines that support AI

1.2: Describe the important historical events in AI history

2: Describe the architecture of intelligent agents and their environments

2.1: Distinguish between the various kinds of agents: simple reflex, goal-based, utility-based

2.2: Describe the properties of an agent’s environment

3: Describe, apply, and implement uninformed and informed search techniques to solve search problems.

3.1: Apply and implement breadth-first, depth-first, uniform-cost, iterative deepening search techniques to search problems

3.2: Describe and implement A* search

3.3: Describe the basic theoretical properties of heuristic functions

3.4: Describe the process of developing heuristics based on relaxing domain constraints

3.5: Apply and implement common techniques for solving constraint satisfaction problems

4: Implement software capable of playing a competitive game

4.1: Describe and implement mini-max and alpha-beta search

4.2: Devise an evaluation function for assessing the game state of a fully observable, deterministic game.

4.3: 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

5.1: Describe and evaluate logic operations

5.2: Determine if knowledge base (KB) entails a sentence S.

5.3: Apply inference rules to a KB to create new rules

5.4: Describe forward and backward chaining algorithms

5.5: Convert a set of logic sentences into normal form

5.6: Complete resolutions proofs

5.7: Use first order logic to describe some domain

5.8: State the time complexity of the various inferencing algorithms

6: Describe, apply, and implement basic machine learning techniques

6.1: Describe the differences in supervised learning, unsupervised learning, and reinforcement learning.

6.2: Describe, apply, and implement the decision tree learning algorithm

6.3: Describe an ensemble learning method such as AdaBoost.

6.4: 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.