CSC 4631 - Artificial Intelligence

• Describe Turing test and the "Chinese Room" thought experiment
• Describe difference between the sense/think/act model and embodied or modeless frameworks
• Create well-reasoned, logical arguments of the biases and limitations of AI techniques
• Explore agent and state-based frameworks to various problems such as game playing. These frameworks may include state machines, decision trees, informed and uninformed search, Q-Learning, reinforcement learning, minimax, cellular automata, etc.
• Identify problems that can be modeled as a search space problem with appropriate operators
• Describe the role of heuristics and describe the trade-offs among completeness, optimality, time complexity, and space complexity
• Apply propositional and first-order logic to planning problems including working with Bayesian decision networks
• Explore embodied and nature-inspired approaches including Artificial Neural Networks, Genetic Algorithms, and Reinforcement Learning
• Evolve controllers for simulated and real robots (Evolutionary Robotics)
• Describe the problem of combinatorial explosion of search space and its consequences

• Understand and apply complex data structures and algorithms
• Use appropriate algorithms (and associated data structures) to design and build working software systems
• An ability to construct Python solutions to programming problems
• Understand and apply mathematical functions, relations, and sets as well as the associated operations
• Ability to use and understand symbolic propositional logic