CSCI 561 Artificial Intelligence Fall 2003

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Course Description : CSCI-561 Artificial Intelligence - Fall 2003 Number Details 1 Introduction. [AIMA Ch 1] Course Schedule. Homeworks, exams and grading. Course material, TAs and office hours. Why study AI? What is AI? The Turing test. Rationality. Branches of AI. Research disciplines connected to and at the foundation of AI. Brief history of AI. Challenges for the future. Overview of class syllabus. 2 Intelligent Agents. [AIMA Ch 2] What is an intelligent agent? Examples. Doing the right thing (rational action). Performance measure. Autonomy. Environment and agent design. Structure of agents. Agent types. Reflex agents. Reactive agents. Reflex agents with state. Goal-based agents. Utility-based agents. Mobile agents. Information agents. 3 Problem solving and search 1. [AIMA Ch 3] Example: measuring problem. Types of problems. More example problems. Basic idea behind search algorithms. 4 Problem solving and search 2. [AIMA Ch 3] Complexity. Combinatorial explosion and NP completeness. Polynomial hierarchy. 5 Uninformed search. [AIMA Ch 3] Depth-first. Breadth-first. Uniform-cost. Depth-limited. Iterative deepening. Examples. Properties. 6 Informed search 1. [AIMA Ch 4] Best-first. A* search. Heuristics. Hill climbing. Problem of local extrema. Simulated annealing. 7 Informed search 2. [AIMA Ch 4] Best-first. A* search. Heuristics. Hill climbing. Problem of local extrema. Simulated annealing. 8 Game playing 1. [AIMA Ch 5] The minimax algorithm. Resource limitations. Aplha-beta pruning. 9 Game playing 2. [AIMA Ch 5] Elements of chance and nondeterministic games. 10 Agents that reason logically 1. [AIMA Ch 6] Knowledge-based agents. Logic and representation. Propositional (boolean) logic. 11 Agents that reason logically 2. [AIMA Ch 6] Inference in propositional logic. Syntax. Semantics. Examples. 12 First-order logic 1. [AIMA Ch 7] Syntax. Semantics. Atomic sentences. Complex sentences. Quantifiers. Examples. FOL knowledge base. Situation calculus. 13 First-order logic 2. [AIMA Ch 7] Describing actions. Planning. Action sequences. 14 Building a knowledge base. [AIMA Ch 8] Knowledge bases. Vocabulary and rules. Ontologies. Organizing knowledge. 15 Building a knowledge base. [AIMA Ch 8] Knowledge bases. Vocabulary and rules. Ontologies. Organizing knowledge. 16 Inference in first-order logic 1. [AIMA Ch 9] Proofs. Unification. Generalized modus ponens. Forward and backward chaining. 17 Inference in first-order logic 2. [AIMA Ch 9] Proofs. Unification. Generalized modus ponens. Forward and backward chaining. 18 Inference in first-order logic 3. [AIMA Ch 9] Proofs. Unification. Generalized modus ponens. Forward and backward chaining. 19 Logical reasoning systems. [AIMA Ch 10] Indexing, retrieval and unification. The Prolog language. Theorem provers. Frame systems and semantic networks. 20 Planning. [AIMA Ch 11] Definition and goals. Basic representations for planning. Situation space and plan space. Examples. 21 Semantic Web and the Resource Description Framework (RDF). [Slides and Semantic Web Article] Introduction to the Semantic Web concept. Detailed look into RDF--a language for building ontologies for the Internet. 22 Fuzzy logic. [Handout] Introduction to fuzzy logic. Linguistic hedges. Fuzzy inference. Examples. 23 Fuzzy logic. [Handout] Knowledge-based agents. Logic and representation. Propositional (boolean) logic. 24 Neural Networks. [Handout] Perceptrons, Hopfield networks, self-organizing feature maps. How to size a network? What can neural network achieve? 25 Neural Networks. [Handout] Perceptrons, Hopfield networks, self-organizing feature maps. How to size a network? What can neural network achieve? 26 Genetic Algorithms. [Handout] Overview of genetic algorithms and their use in optimization problame. 27 Towards intelligent machines. [AIMA Ch 25] The challenge of robots: with what we have learned, what hard problems remain to be solved? Different types of robots. Tasks that robots are for. Parts of robots. Architectures. Configuration spaces. Navigation and motion planning. Towards highly-capable robots. 28 Overview and summary. [all of the above] What have we learned. Where do we go from here?