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 capability of a system to adapt its behavior to meet its goal in a range of environments.” ) defines Intelligence, “as the • 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