• EXPERT SYSTEMS apply rules to solve a problem.

– The system uses IF statements and user answers to questions in order to reason just like a human does.

– It takes something the users doesn’t know and applies rules to indicate what to do.

– Expert Systems: ask a series of questions to determine what is “known.” • NEURAL NETWORKS recognize/learn patterns and can apply that learning to the unknown.

– It is either taught by someone or teaches itself. After it is taught to recognize the pattern, it can adjust itself to reflect new learning.

– Neural networks: system is “guessing” based upon examples and patterns found in the data set- trying to figure out what category something fits in.

• GENETIC ALGORITHMS generate several generations of solutions, with each generation resulting in a to the problem.

A GENETIC ALGORITHM

is an artificial intelligence system that mimics the to generate increasingly better solutions to a problem.

Genetic algorithms produce several generations of solutions, choosing the best of the current set for each new generation.

THE CONCEPTS OF EVOLUTION IN GENETIC ALGORITHMS

• • • - or survival of the fittest. The key is to give preference to better outcomes.

- combining portions of good outcomes in the hope of creating an even better outcome.

- randomly trying combinations and evaluating the success (or failure) of the outcome.

Seeking an

Genetic Algorithms Can Generate Lots of Solutions As In

• Deciding which a firm should invest in, given limited investment dollars.

• Generating solutions to – How much cable or track to lay?

– What should your delivery vehicles take?

• Used to production resources) (make the best use of your • Investment companies use them to generate by considering of stocks and bonds .

• Clothing manufacturing: so as to generate the www.coyotegulch.com

: The Traveling Salesman

AN INTELLIGENT AGENT

is a that and then with a certain degree of , and in doing so, employs knowledge or representation of the user’s goals or desires.

The Agent will take your profile and preferences and then go out and work on your behalf.

Characteristics of an intelligent agent A : can act without you telling them what to do A : can and what it does based upon your changing characteristics.

S : can and with other agents that it encounters.

Types of Intelligent Agents

• I Internet or a database) – B s, shopping bots, Google search.

and bring it back to you (from the , Googlebots that scour the Internet locating and indexing sites that ultimately appear in search results when you do a – Information agents for Amazon display lists of books and other products that customers might like, based on past purchases.

• M and Surveillance Agents: constantly – A – Agents that monitor web sites for updated info, such as price changes on desired products.

– Wizards in Microsoft Office and offer suggestions for improvement.

• U : act as a personal assistant by . Examples include sorting and prioritizing email, filling out forms on the Web automatically for you, and automatically storing your information.

• D agents operate in a data warehouse by sifting through the data, trying to discover trends, relationships and patterns through the use of multidimensional statistical analysis.

Monitoring & Surveillance Agents:

constantly observe and report back on what they see.

• Spell Checker • Grammar Checker • Monitoring and surveillance agent in Excel

•

Data-mining agents perform multidimensional analysis in data

Cube

warehouses

– common term for the representation of multi dimensional information (layers, rows, columns)

• EXPERT SYSTEMS apply rules to solve a problem.

– The system uses IF statements and user answers to questions in order to reason just like a human does.

– It takes something the users doesn’t know and applies rules to indicate what to do.

• NEURAL NETWORKS recognize/learn patterns and can apply that learning to the unknown.

– It is either taught by someone or teaches itself. After it is taught to recognize the pattern, it can adjust itself to reflect new learning.

• GENETIC ALGORITHMS generate several generations of solutions, with each generation resulting in a better solution to the problem.

• Expert Systems: ask a series of questions to determine what is “known.” • Neural networks: system is “guessing” based upon examples and patterns found in the data set- trying to figure out what category something fits in.

AI System Problem Type

Expert Systems Neural Networks Diagnostic or prescriptive Identification, classification, prediction

Based On

Strategies of experts The human brain Genetic Algorithms Intelligent Agents

Starting Information

Expert’s know-how Acceptable patterns Optimal solution Biological evolution Set of possible solutions Specific and repetitive tasks One or more AI techniques Your preferences