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CS408: Advanced Artificial Intelligence

Unit 1: Intelligent Agents and Problems Of AI   AI is often seen through the autonomous, rational intelligent agents paradigm, which we will emphasize in this unit.  This unit will begin by discussing what software agents are and how agents differ from programs in general.  The unit will then provide a natural taxonomy of autonomous agents and discusses possibilities for further classification before presenting those problems in AI that seem to received the most attention.  The problem of creating intelligence is then broken down into a number of specific sub-problems, which consist of particular traits that should be found in an intelligent system.  Note that different researchers approach the problems of AI from different perspectives, depending on their respective training, fields of expertise, and favored tools.

Unit 1 Time Advisory
This unit should take you 28 hours to complete.

☐    Subunit 1.1: 6 hours

☐    Subunit 1.2: 5 hours

☐    Subunit 1.3: 5 hours

☐    Subunit 1.4: 12 hours

Unit1 Learning Outcomes
Upon successful completion of this unit, students will be able to:

  • List various definitions of the term “intelligent agent.”
  • List and define the major problems that AI addresses.
  • Compare and contrast the terms knowledge, representation, and reasoning.
  • Define the forms of knowledge representation.
  • Describe why planning is difficult.
  • Explain the basics of learning theory.
  • Describe the difference between deduction and induction.
  • Define the notion of probability and Bayes’ rule.
  • List and define the major approaches AI takes in solving problems.

1.1 Is It an Agent, or Just a Program?   - Reading: The University of Memphis: Stan Franklin and Art Graesser's “Is It an Agent, or Just a Program?: A Taxonomy for Autonomous Agents” Link: The University of Memphis: Stan Franklin and Art Graesser’s “Is It an Agent, or Just a Program?” (HTML)
 
Instructions:  This resource covers subsections 1.1.1-1.1.5.  Read the webpage to learn about the advent of software agents.  Memorize the definitions of the AIMA, Maes, KidSim, Hayes-Roth, IBM, SodaBot, Foner, and Brustoloni Agents.  Make sure you know how to define “agency” and work to memorize Franklin's definition of an agent.  Read through the examples of the different taxonomies and classifications of agents. 
 
About the link: Stan Franklin and Art Graesser are researchers of AI, and professors of computer science and cognitive science at the University of Memphis.
 
Terms of Use: Please respect the copyright and terms of use displayed on the web pages above.

1.1.1 What is an agent?   1.1.2 The Essence of Agency   1.1.3 Agent Classifications   1.1.4 A Natural Kinds Taxonomy of Agents   1.1.5 Subagents and Societies of Agents   1.1.6 John Lloyd on Intelligent Agents   - Lecture: videolectures.net: John Lloyd’s “Intelligent Agents: Part 1” Link: videolectures.net: John Lloyd’s“Intelligent Agents: Part 1” (Adobe Flash and Windows Media Player)
 
Instructions: Watch the first part of this three-part video series by John Lloyd.  As he lectures you may wish to work through the slides included on the page.  Throughout the lecture, Professor Lloyd talks about AIMA agents and presents some pertinent examples.  Please compare his thoughts with yours and Franklin's from the previous sections.  This lecture is approximately 50 minutes in length. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
 
About the link: John Lloyd is a professor at Australian National University who shares lectures on videolectures.net.  In the lecture, he introduces the basic ideas of agents and describes some agent architectures.
 
Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (John Lloyd).

1.1.7 Stan Franklin - A Cognitive Theory of Everything   - Lecture: Google Videos: Stan Franklin’s “A Cognitive Theory of Everything” The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

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1.2 Overview of AI General Problems   - Reading: Wikipedia’s “Artificial Intelligence: Problems” Link: Wikipedia’s “Artificial Intelligence: Problems” (PDF)

    
 Instructions: Read this entry on the general problems arising in
the field of AI.  After completing this assignment, you should know
the meaning of terms such as knowledge representation, planning,
learning, natural language processing, motion and manipulation,
perception, social intelligence, creativity, and general
intelligence.  This link covers subsections 1.2.1-1.2.9.  Note that
sections 1.2.2-1.2.4 have additional resources assigned to them (see
below) and require extra attention.  
    
 About the link: The article above is an entry from
en.wikipedia.org, which is a web-based, free-content encyclopedia
project based on an openly editable model.  
    
 Terms of Use: The article above is released under a [Creative
Commons Attribution-Share-Alike License
3.0](http://creativecommons.org/licenses/by-sa/3.0/)(HTML).  This
article is a modified version of an article of the same title
originally found on Wikipedia.  The Saylor Foundation has
reformatted the entry and has omitted several of the original
sections.You can find the original Wikipedia version of this article
[here](http://en.wikipedia.org/wiki/Artificial_intelligence#Problems)(HTML).  

 

1.2.1 Deduction, Reasoning, Problem Solving   1.2.2 Knowledge Representation   - Lecture: videolectures.net: Maurice Pagnucco’s “Knowledge Representation and Reasoning: Part 1” Link: videolectures.net: Maurice Pagnucco’s “Knowledge Representation and Reasoning: Part 1” (Adobe Flash and Windows Media Player)
 
Instructions: Watch the first part of this three-part video series by Maurice Pagnucco.  After viewing the lecture, you should be able to define the terms knowledge, representation, and reasoning; realize the advantages of this approach; and define the forms of knowledge representation.  This lecture is approximately 1 hour long. 
 
About the link: Maurice Pagnucco is a professor at the School of Computer Science and Engineering at University of New South Wales. 
 
Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Maurice Pagnucco).

1.2.3 Planning   - Lecture: videolectures.net: Jussi Rintanen’s “Planning: Part 1” Link: videolectures.net: Jussi Rintanen’s“Planning: Part 1” (Adobe Flash and Windows Media Player)
 
Instructions: Watch the first part of the video by Jussi Rintanen.  You may wish to work through the slides provided on the right-hand side of the screen as Professor Rintanen lectures.  After viewing the lecture, you should understand why planning can be difficult and be able to define the term “transition systems.”  This video is about an hour long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
 
About the link: Jussi Rintanen is a researcher and an associate professor at NICTA Canberra Research Laboratory and The Australian National University. 
 
Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Jussi Rintanen).

1.2.4 Learning   - Lecture: videolectures.net: Olivier Bousquet’s “Introduction to Learning Theory: Part 1” Link: videolectures.net: Olivier Bousquet’s “Introduction to Learning Theory; part 1” (Adobe Flash and Windows Media Player)
 
Instructions: Watch Olivier Bousquet’s “Part 1,” working through the provided on the right-hand side of the screen as you listen to his lecture.  After viewing the lecture, you should have a general understanding of “learning theory,” be able to differentiate between deduction and induction, and describe, in general terms, the concept of probability and Bayes' rule.  This lecture is about 1 hour long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
 
About the link: Olivier Bousquet works at the Max Planck Institute for Biological Cybernetics. 
 
Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Oliver Bousquet).

1.2.5 Natural Language Processing   1.2.6 Motion and Manipulation   1.2.7 Perception   1.2.8 Social Intelligence   1.2.9 General Intelligence   1.3 Approaches to AI   - Reading: Wikipedia’s “Artificial Intelligence: Approaches” Link: Wikipedia’s“Artificial Inelligence: Approaches” (HTML)
 
Instructions: Read this entry on the different paradigms that guide AI research and make sure you know the differences between them.  This link covers subsections 1.3.1-1.3.4.
 
About the link: The article above is an entry from en.wikipedia.org, which is a web-based, free-content encyclopedia project based on an openly editable model.
 
Terms of Use: The article above is released under a Creative Commons Attribution-Share-Alike License 3.0(HTML).  This article is a modified version of an article of the same title originally found on Wikipedia.  The Saylor Foundation has reformatted the entry and has omitted several of the original sections.You can find the original Wikipedia version of this article here(HTML).

 

1.3.1 Cybernetics and Brain Simulation   1.3.2 Symbolic AI   1.3.3 Sub-symbolic AI   1.3.4 Statistical   1.3.5 Systems with General Intelligence   - Lecture: videolectures.net: Michael Thielscher’s "Systems with General Intelligence" Link: videolectures.net: Michael Thielscher’s "Systems with General Intelligence" (Adobe Flash and Windows Media Player)
 
Instructions: Watch this video about general problems in AI, working through slides provided on the right-hand side of the screen as Thielscher lectures.  After watching the video, you should be familiar with the chess-as-an-intelligent-system example, understand what general game playing is about, and identify the major questions with which general AI is concerned.  Do not let yourself get bogged down by the details; work for a general understanding of AI.  This lecture is 53 minutes long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
 
About the link: In this video, Michael Thielscher of the School of Computer Science and Engineering, University of New South Wales,  talks about general intelligence and AI problems, approaches, and history.
 
Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Michael Thielscher).

1.4 Agents in Code   - Assignment: National Taiwan Normal University: Department of Computer Science and Information Engineering: Tsung-Che Chiang’s “Vacuum Cleaner World”  Link: National Taiwan Normal University: Department of Computer Science and Information Engineering: Tsung-Che Chiang’s “Vacuum Cleaner World” (HTML)
 
 Instructions: Please read through the webpage and follow the instructions to complete the activity.
 
 Terms of Use: Please respect the copyright and terms of use displayed on the web page above.