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COMP-6600: Artificial Intelligence (Overview) • A tentative overview of the course is as follows: 1. Introduction to Artificial Intelligence 2. Evolutionary Computation 3. Machine Learning Overview (cont.) • This course will consist of: – homework assignments (25%) – a final exam (25%) – a final project (50%) • a final project presentation (10%) [Must have a topic by week 5] • a final project report (40%) Brief Introduction to Artificial Intelligence • One of the first questions we must ask ourselves concerning AI is, “What does it mean to be intelligent?’’ • According to Webster’s New World Pocket Dictionary (3rd Edition), Intelligence is defined as, “The ability to learn, or solve problems”. • Fogel in (Fogel, D. B., Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, 2000) defines Intelligence, “as the capability of a system to adapt its behavior to meet its goal in a range of environments.” • According to our textbook there are 4 camps based on thinking/acting humanly/rationally. – – – – Thinking Humanly: Cognitive Modeling Thinking Rationally: Logic Acting Humanly: Turing Test Acting Rationally: Intelligent Agents Brief Introduction to Artificial Intelligence (cont.) • In my opinion, Intelligence is the ability to create unique artifacts (ideas, or concepts) that previously did not exist. – Genesis 2:19,20 NIV – Jeremiah 32:35 NIV • Is it possible to reliably classify an entity as intelligent by merely observing or interacting with it? – – – – Sphex Wasp (Fogel, 2000,p. 13; Russell & Norvig, 2003, p. 37) Dung Beetle (Russell & Norvig, 2003, p. 37) Eliza (Weizenbaum) Parry COMP-4640: Symbolic AI • Based on Newell & Simons Physical Symbol System Hypothesis • Uses logical operations that are applied to declarative knowledge bases (FOPL) • Commonly referred to as “Classical AI” • Represents knowledge about a problem as a set of declarative sentences in FOPL • Then logical reasoning methods are used to deduce consequences • Another name for this type of approach is called “the knowledgebased” approach • The Symbol Processing Approach uses “top-down” design of intelligent behavior. COMP-6600: Sub-symbolic Approach • Based on the Physical Grounding Hypothesis • “bottom-up” style • Starting at the lowest layers and working upward. • In the sub-symbolic approach signals are generally used rather than symbols • Proponents believe that the development of machine intelligence must follow many of the same evolutionary steps. • Sub-symbolic approaches rely primarily on interaction between machine and environment. This interaction produces and emergent behavior (evolutionary robotics, Nordin, Lund) • Some other sub-symbolic approaches are: Evolutionary Computation, Artificial Immune Systems, and Neural Networks