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DCP 1172 Introduction to Artificial Intelligence

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Title: DCP 1172 Introduction to Artificial Intelligence


1
DCP 1172 Introduction to Artificial Intelligence
  • Lecture 1
  • Chang-Sheng Chen

2
Today 9/14
  • Administrative stuff
  • What is AI? Why study AI ?
  • Overview of course topics

3
Administrative Stuff
  • Under-graduate course
  • Web page/Class mailing list
  • Reasonable preparation
  • Requirements
  • Computing Facility
  • Programming

4
DCP 1172 Introduction to Artificial Intelligence
  • Instructor Dr. Chang-Sheng Chen,
    cschen_at_mail.nctu.edu.tw
  • Lectures Tue 1540-1630, Fri 1010-1200,
    EC-016
  • Office hours Thu 1030 1130 pm
  • Room 328, Computer Network Center and by
    appointment
  • Course web page
  • http//www.cc.nctu.edu.tw/cschen/courses/2004/dcp
    1172.html
  • Up to date information
  • Lecture notes
  • Relevant dates, links, etc.

5
DCP 1172 Introduction to Artificial
Intelligence
  • Course overview
  • foundations of symbolic intelligent systems.
    agents, search, problem solving, logic,
    representation, reasoning, symbolic programming,
    and robotics, etc.
  • Prerequisites
  • Basic algorithm and data structure analysis
  • Ability to program
  • Some knowledge of Prolog/Lisp for some
    programming assignments.
  • Some exposure to logic
  • Exposure to basic concepts in probability
  • Familiarity with linguistics, psychology, and
    philosophy

6
DCP 1172 Introduction to Artificial
Intelligence
  • Requirements READ the book!
  • AIMA Artificial Intelligence A Modern Approach
  • Russell and Norvig, 2nd Edition, Prentice-Hall
    2003
  • Grading
  • 30 for mandatory homework assignments
  • 15 for term project
  • 25 for midterm
  • 30 for final

7
Important things about this CLASS
  • Homework Late Policy
  • Assignments are due in class, at the beginning of
    class, on the assigned due date.
  • That is unless youve made some arrangement with
    me ahead of time.
  • Programming
  • The programming for this class will be done using
    LISP/Prolog.
  • Free versions are available for UNIX, Windows.
  • Forbidden Things
  • Please dont cheat, copy, plagiarize (??/??) or
    otherwise make my life and yours unpleasant.

8
Computing Facilities/Programming
  • (I suppose) Most of the programming assignments
    could be done using your own PC.
  • However, if in need, we could installed another
    Unix workstation (e.g., using FreeBSD) and you
    could do your programming jobs there.
  • The programming for this class will be done using
    LISP and/or Prolog.
  • Free versions are available for UNIX, Windows.
  • GNU Prolog, etc.
  • GNU Common Lisp, etc.

9
Course Topics
  • Agents
  • State space search
  • Knowledge representation
  • Uncertain reasoning
  • Machine learning
  • Modern AI Applications
  • Rule-based expert system
  • Fuzzy expert system
  • Artificial Neural Network
  • Evolutionary Computation
  • Hybrid Intelligent System

10
A Framework What is AI?
The exciting new effort to make computers thinks
… machine with minds, in the full and literal
sense (Haugeland 1985)
The study of mental faculties through the use of
computational models (Charniak et al. 1985)
The art of creating machines that perform
functions that require intelligence when
performed by people (Kurzweil, 1990)
A field of study that seeks to explain and
emulate intelligent behavior in terms of
computational processes (Schalkol, 1990)
Systems that think like humans
Systems that think rationally
Systems that act like humans
Systems that act rationally
11
Our Framework
  • Getting computers to do the right thing based on
    their circumstances and what they know.
  • No presuppositions about how they should be
    designed to do the right thing
  • I.e. not limited to how people do it
  • Evaluation is based on performance, not on how
    the task is performed

12
Acting Humanly The Turing Test
  • Alan Turing's 1950 article Computing Machinery
    and Intelligence discussed conditions for
    considering a machine to be intelligent
  • Can machines think? ?? Can machines behave
    intelligently?
  • The Turing test (The Imitation Game) Operational
    definition of intelligence.

13
Acting Humanly The Turing Test
  • Computer needs to possess Natural language
    processing, Knowledge representation, Automated
    reasoning, and Machine learning
  • Are there any problems/limitations to the Turing
    Test?

14
Why study AI?
Search engines
Science
Medicine/ Diagnosis
Labor
Appliances
What else?
15
Honda Humanoid Robot
Walk
Turn
http//world.honda.com/robot/
Stairs
16
Sony AIBO
http//www.aibo.com
17
Natural Language Question Answering
http//www.ai.mit.edu/projects/infolab/
http//aimovie.warnerbros.com
18
Robot Teams
USC robotics Lab
19
Applied Areas of AI
  • Game playing
  • Speech and language processing
  • Expert reasoning
  • Planning and scheduling
  • Vision
  • Robotics
  • …

20
What tasks require AI?
  • AI is the science and engineering of making
    intelligent machines which can perform tasks that
    require intelligence when performed by humans …
  • What tasks require AI?

21
What tasks require AI?
  • Tasks that require AI
  • Solving a differential equation
  • Brain surgery
  • Inventing stuff
  • Playing Jeopardy
  • Playing Wheel of Fortune
  • What about walking?
  • What about grabbing stuff?
  • What about pulling your hand away from fire?
  • What about watching TV?
  • What about day dreaming?

22
The Architectural Components of AI Systems
  • State-space search
  • Knowledge representation
  • Logical reasoning
  • Reasoning under uncertainty
  • Learning

23
Outlook
  • AI is a very exciting area right now.
  • This course will teach you the foundations.

24
Course Overview
  • General Introduction
  • 01-Introduction. AIMA Ch 1 Course Schedule.
    Homeworks, exams and grading. Course material,
    TAs and office hours. Why study AI? What is AI?
    The Turing test. Rationality. Branches of AI.
    Research disciplines connected to and at the
    foundation of AI. Brief history of AI. Challenges
    for the future. Overview of class syllabus.
  • 02-Intelligent Agents. AIMA Ch 2 What is
  • an intelligent agent? Examples. Doing the right
  • thing (rational action). Performance measure.
  • Autonomy. Environment and agent design.
  • Structure of agents. Agent types. Reflex agents.
  • Reactive agents. Reflex agents with state.
  • Goal-based agents. Utility-based agents. Mobile
  • agents. Information agents.

25
Course Overview (cont.)
How can we solve complex problems?
  • 03/04-Problem solving and search. AIMA Ch 3
    Example measuring problem. Types of problems.
    More example problems. Basic idea behind search
    algorithms. Complexity. Combinatorial explosion
    and NP completeness. Polynomial hierarchy.
  • 05-Uninformed search. AIMA Ch 3 Depth-first.
    Breadth-first. Uniform-cost. Depth-limited.
    Iterative deepening. Examples. Properties.
  • 06/07-Informed search. AIMA Ch 4 Best-first. A
    search. Heuristics. Hill climbing. Problem of
    local extrema. Simulated annealing.

26
Course Overview (cont.)
  • Practical applications of search.
  • 08/09-Game playing. AIMA Ch 5 The minimax
    algorithm. Resource limitations. Aplha-beta
    pruning. Elements of
  • chance and non-
  • deterministic games.

tic-tac-toe
27
Course Overview (cont.)
  • 10-Agents that reason logically 1. AIMA Ch 6
    Knowledge-based agents. Logic and representation.
    Propositional (boolean) logic.
  • 11-Agents that reason logically 2. AIMA Ch 6
    Inference in propositional logic. Syntax.
    Semantics. Examples.

Towards intelligent agents
wumpus world
28
Course Overview (cont.)
  • Building knowledge-based agents 1st Order Logic
  • 12-First-order logic 1. AIMA Ch 7 Syntax.
    Semantics. Atomic sentences. Complex sentences.
    Quantifiers. Examples. FOL knowledge base.
    Situation calculus.
  • 13-First-order logic 2.
  • AIMA Ch 7 Describing actions.
  • Planning. Action sequences.

29
Course Overview (cont.)
  • Representing and Organizing Knowledge
  • 14/15-Building a knowledge base. AIMA Ch 8
    Knowledge bases. Vocabulary and rules.
    Ontologies. Organizing knowledge.

An ontology for the sports domain
Kahn Mcleod, 2000
30
Course Overview (cont.)
  • Reasoning Logically
  • 16/17/18-Inference in first-order logic. AIMA Ch
    9 Proofs. Unification. Generalized modus ponens.
    Forward and backward chaining.

Example of backward chaining
31
Course Overview (cont.)
  • Examples of Logical Reasoning Systems
  • 19-Logical reasoning systems.
  • AIMA Ch 10 Indexing, retrieval
  • and unification. The Prolog language.
  • Theorem provers. Frame systems
  • and semantic networks.

Semantic network used in an insight generator
(Duke university)
32
Course Overview (cont.)
  • Systems that can Plan Future Behavior
  • 20-Planning. AIMA Ch 11 Definition and goals.
    Basic representations for planning. Situation
    space and plan space. Examples.

33
Course Overview (cont.)
  • Expert Systems
  • 21-Introduction to CLIPS. handout
  • Overview of modern rule-based
  • expert systems. Introduction to
  • CLIPS (C Language Integrated
  • Production System). Rules.
  • Wildcards. Pattern matching.
  • Pattern network. Join network.

CLIPS expert system shell
34
Course Overview (cont.)
  • Logical Reasoning in the Presence of Uncertainty
  • 22/23-Fuzzy logic.
  • Handout Introduction to
  • fuzzy logic. Linguistic
  • Hedges. Fuzzy inference.
  • Examples.

35
Course Overview (cont.)
  • AI with Neural networks
  • 24/25-Neural Networks.
  • Handout Introduction to perceptrons, Hopfield
    networks, self-organizing feature maps. How to
    size a network? What can neural networks achieve?

36
Course Overview (cont.)
  • Evolving Intelligent Systems
  • 26-Genetic Algorithms.
  • Handout Introduction
  • to genetic algorithms
  • and their use in
  • optimization
  • problems.

37
Course Overview (cont.)
  • What challenges remain?
  • 27-Towards intelligent machines. AIMA Ch 25 The
    challenge of robots with what we have learned,
    what hard problems remain to be solved? Different
    types of robots. Tasks that robots are for. Parts
    of robots. Architectures. Configuration spaces.
    Navigation and motion planning. Towards
    highly-capable robots.
  • 28-Overview and summary. all of the above What
    have we learned. Where do we go from here?

robotics_at_USC
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