Artificial Intelligence John Ross Yuki Yabushita Sharon Pieloch Steven Smith

8 Types of AI  Robotics  Fuzzy Logic  Expert Systems  Intelligent Agents  Natural Language Processing  Artificial Vision  Neural Networks  Genetic Algorithms

Expert Systems  “An Expert System is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise”.  In the early 1960’s, the General-purpose Problem Solver was introduced (GPS). This machine helped solve some basic problems.  Source: (

Geologic Expert System Model Part 1 Source:

Geologic Expert System Model Part 2 Source:

Geologic Expert System Model Part 3 Source:

Source: Over the past 10 years, expert systems have replaced human workers in fields such as industrial, science and research, and even for recreational purposes such as mowing your lawn. ES’s will eventually be able to solve complex solutions such as the traveler who wants the fastest route for 10 cities. The Future of Expert Systems The i-Mow from Toro

Natural Language Processing NLP has become very popular over the last decade. Speech recognition can make work a bit easier if implemented in the right places. One of the drawbacks has been the difference in languages and syllables. Computers must be trained to adapt to individual voice patterns which takes time. “It is clear that for applications that are eye-busy, hand-busy, a trainer that incorporates NLP technology is very useful” (

Fig. 1: Examples of typical documents used: a) Chinese, b) English, c) Greek, d) Korean, e) Malayalam, f) Persian, g) Russian. Natural Language Processing: The Multiple Languages Barrier Source:

Breaking the Language Barrier  To incorporate ES units that can be translated in most common languages.  To bypass syllable irregularities between different languages (such as Japanese characters that use two symbols).  NLP systems that can comprehend the intended use of certain words. Fuzzy logic can give computers protocols of how certain words are used in certain cultures.

Integrating Global NLP  One future challenge is to create cells (nodes) that will comprehend all major languages.  In the future, robots will recognize multiple voice patterns in multiple languages.

Fuzzy logic  Similar processes to those of neural networks.  It is designed with specific formulas or rules.  Gives a clear output by having a calculation.  Usually used for “fuzzy” things (unclear estimations).

Fuzzy logic General idea/rules  If service is poor, good, excellent..?  If food is rancid, or delicious..? We can combine these two categories to to one list.  If service is poor or the food is rancid..?  If service is excellent or food is delicious…? The MathWorks, elp/toolbox/fuzzy/fuzzy.shtml

Fuzzy logic

Neural Network  Inputs: all facts  Hidden layer: finding the possible ways to predict  Outputs: final decision of prediction

Neural Network  Different paradigm for computing.  Operates similar to our human brain.  Takes inputted data, and predict some actions based on these data.  Learns new ways of solving by adding some more facts.

Neural Network Business Application  Finance; credit risk decision support, predicting bad debtors, preventing application fraud  Marketing; customer attrition, analyzing consumer-spending patterns  Forecasting sea surface temperature

Fuzzy logic Business Application  Cruise control for car  PDA  Subway system  Prediction system for early recognition of earthquakes  Air conditioning system

Robotics Definition: A programmable, self controlled device consisting of electronic, electrical, or mechanical units It is a machine that functions in place of a living agent