Introduction to Artificial Intelligence

At the end of the course, students should be able to: 1. explain an overview of the field of artificial intelligence, its background, history, fundamental issues, challenges and main directions; 2. interpret and formulate knowledge representations in the form of logic expressions; 3. explain basic concepts, methods and theories for search; 4. account for classical planning of proactive agents; 5. describe methods and theories for reactive agents, architectures based on subsumption, and potential fields; 6. describe the physical structure of robots; 7. account for different degrees of autonomy of robots; 8. explain concepts, methods and theories of embodied cognition and situatedness; 9. explain basic concepts, methods and theories of sensing; and 10. explain basic concepts, methods and theories of neural networks and learning.

This is an introductory course on Artificial Intelligence. The topics include; Overview: foundations, scope, problems, and approaches of AI. Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents; Artificial Intelligence programming techniques. Problem-solving through Search: forward and backward, state-space, blind, heuristic, problem-reduction, A, A*, AO*, minimax, constraint propagation, neural, stochastic, and evolutionary search algorithms, sample applications. Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, Content logics, reasoning with defaults, reasoning about knowledge, sample applications. Planning: planning as search, partial order planning, construction and use of planning graphs. Representing and Reasoning with Uncertain Knowledge: probability, connection to logic, independence, Bayes rule, bayesian networks, probabilistic inference, sample applications. Decision-Making: basics of utility theory, decision theory, sequential decision problems, elementary game theory, sample applications. Machine Learning and Knowledge Acquisition: learning from memorisation, examples, explanation, and exploration. learning nearest neighbor, naive Bayes, and decision tree classifiers, Q-learning for learning action policies, applications. Sample Applications of AI, student project presentations.