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 (3 rd 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 knowledge- based” 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