Download What is AI?

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What is AI?
AI is the field of science and engineering which attempts to build
intelligent systems.
But what are intelligent systems?
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What is AI (cont’d)
Definitions found in AI textbooks tend to fall into the following
categories:
• AI is the field of science and engineering which attempts to
build systems that act like humans.
• ... that think like humans.
• ... that think rationally.
• ... that act rationally.
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Acting Like Humans: The Turing Test Approach
To pass the Turing test a computer should have the following
capabilities:
• natural language processing
• knowledge representation
• automated reasoning
• machine learning
• computer vision
• robotics
Within AI, there has not been a big effort to pass the Turing test.
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Thinking Like Humans:
The Cognitive Modelling Approach
How do humans think?
There two ways to find out:
• Introspection
• Psychological experiments
Example: The GPS program by Newell and Simon
In this tradition, psychology and cognitive science are very
relevant.
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Thinking Rationally: The Laws of Thought Approach
What are the laws of thought? This question goes back to the
syllogisms of the Greek philosopher Aristotle.
The logicist tradition in AI has followed this approach.
Example: Early work on theorem proving
The emphasis on this tradition is correct inference. As a result related
work from philosophy and logic is very important.
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Acting Rationally: The Rational Agent Approach
In this approach the design of rational agents is the main
problem.
What is an agent?
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Agents
An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon that environment
through effectors or actuators.
sensors
percepts
?
environment
actions
agent
effectors
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Examples of Agents
• Human agents
• Robotic agents
• Software agents (or software robots or softbots).
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Rational Agents
A rational agent is one that acts so as to achieve the best
outcome or, when there is uncertainty, the best expected outcome.
Other properties of agents:
• autonomy
• social ability
• situatedness
• adaptivity
• ...
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Acting Rationally (cont’d)
The study of AI as rational agent design is
• more general than the laws of thought approach
• “easier” than approaches based on human thought or human
behaviour
This is the approach that we will take in this course. We will
concentrate on general principles of rational agents and on
components for constructing them.
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Foundations of AI
The following disciplines have contributed ideas, viewpoints and
techniques to AI.
• Philosophy
• Mathematics
• Economics
• Neuroscience
• Psychology and cognitive science
• Computer science and engineering
• Control theory and cybernetics
• Linguistics
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History of AI
• Gestation (1943-1955)
Models of artificial neurons (McCulloch and Pitts, 1943).
Hebbian learning (Hebb, 1949).
The article “Computing Machinery and Intelligence” by Alan
Turing (1950).
Snarc: The first neural network computer (Minsky and
Edmonds, 1951).
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History of AI (cont’d)
• Birth (1956)
The Dartmouth workshop in the summer of 1956 (McCarthy,
Minsky, Newell, Simon).
The term “artificial intelligence” was adopted after a
suggestion by McCarthy.
Presentation of the Logic Theorist (Newell and Simon). Soon
after the workshop, the Logic Theorist program was able to
prove most of theorems in Chapter 2 of the book Principia
Mathematica (a famous book by Alfred North Whitehead and
Bertrand Russell on logic and mathematics).
See http://www.dartmouth.edu/~ai50/homepage.html for a
conference celebrating the 50th anniversary of the Dartmouth
workshop.
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History of AI (cont’d)
• Early enthusiasm, great expectations (1952-1969)
Logic Theorist, General Problem Solver, Geometry Theorem
Prover, game playing, Lisp, theorem proving, Shakey the robot,
micro-worlds, adalines, perceptrons.
• A dose of reality (1966-1973)
Programs with no domain knowledge, intractability problems.
Cancellation of big projects on machine translation (US),
Lighthill report (UK).
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History of AI (cont’d)
• Knowledge-based systems (1969-1979)
The role of domain specific knowledge, expert systems.
Representation and reasoning languages (e.g., Prolog and frame-based
languages).
• AI becomes industry (1980-present)
The first successful expert system: R1 (McDermott, DEC).
The Japanese 5th generation project (1981) and its emphasis on logic
programming.
Microelectronics and Computer Technology Corporation (MCC) in the
U.S.
Alvey report in the U.K.
• The return of neural networks (1986-present)
Connectionism.
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History of AI (cont’d)
• AI becomes science (1987-present)
Neats vs. scruffies.
Knowledge representation, speech recognition, neural networks
and data mining, Bayesian networks, robotics, computer vision.
• Intelligent agents (1995-present)
See the conference AAMAS
(http://www.aamas-conference.org/)
• Semantic Web (1998-present)
See the site http://www.semanticweb.org/.
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State of the Art
• Autonomous planning and scheduling
See NASA’s Remote Agent
(http://ic.arc.nasa.gov/projects/remote-agent/).
• Game Playing
See IBM’s Deep Blue
(http://www.research.ibm.com/deepblue/).
• Autonomous control
See CMU’s NavLab computer controlled minivan
(http://www.ri.cmu.edu/labs/lab_28.html).
See DARPA’s grand challenge in autonomous ground vehicles
(http://www.darpa.mil/grandchallenge/index.asp).
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State of the Art (cont’d)
• Diagnosis (e.g., medical diagnosis problems consulted by
expert physicians).
• Logistics Planning
The Dynamic Analysis and Replanning Tool, DART was used
by the US military in the Persian Gulf crisis of 1991 to do
logistics planning and scheduling for transportation.
• Constraint solving software
See solvers by ILOG (http://www.ilog.com).
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State of the Art (cont’d)
• Robotics
As an example, RoboCup (http://www.robocup.org/) is an
international competition that has the following goal: “By the
year 2050, develop a team of fully autonomous humanoid robots
that can win against the human world soccer champion team.”
• Language understanding and problem solving
Proverb is a program that solves crossword puzzles better
than most humans, using constraints on possible world fillers, a
large database of past puzzles, and a variety of information
sources (e.g., dictionaries, online databases etc.).
• Computer and video games
See the book Artificial Intelligence for Computer Games by
John Funge (http://ai4games.sourceforge.net/).
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Readings
Chapter 1 and 2 (not in depth) of AIMA.
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