CIS 530: Artificial Intelligence

Course Focus
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Algorithms and methods in classical artificial intelligence

Course motivation
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This course is motivated by the need to provide computer science students with a thorough background in classical artificial intelligence. Classical artificial intelligence techniques such as efficient brute force search, heuristic search, constraint satisfaction, intelligent game design, logical reasoning and automatic planning play a critical role in the design and development of intelligent computer systems. This is a key course for students who want to work in artificial and computational intelligence.

Target Audience:
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The target audience for this course is graduate students and researchers in computer science.

Pre-Requisites:
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1. Undergraduate level data structures and algorithms course
2. Programming in C++ or any high level language

Course Objectives
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At the end of this course, successful candidates will:
- Know about the state of the art algorithms and theory in Artificial Intelligence
- Understand ‘agent models’ of Artificial Intelligence
- Be able to apply concepts of Artificial Intelligence in real life development projects

Course Schedule
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3 Credit Hours, 16 weeks

Course Design and Contents:
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- Basics (1 week): Introduction to AI, its scope and focus. Intelligent agent model of AI.
- Uninformed Search Techniques (1 week): Search problems in Computer Science, Uninformed Search (Breadth First Search, Depth First Search etc.,), Refinements and advancements in uninformed search.
- Heuristic Search (1 week) : Heuristics, Greedy Best First Search, A* search with Memory bounded heuristic search
- Local Search Techniques (1 week): Hill climbing, simulated annealing
- Constrained Satisfaction Problems (1 week): Introduction to constrained satisfaction problems (CSPs) in computer science, solving CSPs through search
- Intelligent Game Programming (1 weeks): Decision making in games, MIN-MAX, Alpha beta pruning, intelligent game design
- Logical Agents and Propositional Logic (2 weeks): Introduction to logic agents, propositional logic, reasoning with proposition logic and inference
- First Order Logic (2 weeks): First Order Logic, Inference in First Order Logic, Use of Prolog for reasoning
- Probabilistic Reasoning (2 weeks)
- Automatic Planning and Scheduling (1 weeks): Robotic Motion planning and scheduling
- Introduction to learning from data (1 week)
- Reinforcement Learning (2 weeks)

Course Books/References:
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• Artificial Intelligence: A modern approach, 3rd Edition, Stuart Russell and Peter Norvig, Prentice Hall 2010.

• PROLOG programming for Artificial Intelligence, 3rd Edition, Ivan Bratko, Pearson Education Press.

• Latest research papers