Lecture 1 – AI Background
Dr. Muhammad Adnan Hashmi
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Profile:
 Name: Dr. Muhammad Adnan Hashmi
 2005: BSc (Hons.) in CS – University of the
Punjab, Lahore, Pakistan
 2007: MS in Multi-Agent Systems– University
Paris 5, Paris, France
 2012: PhD in Artificial Intelligence – University
Paris 6, Paris, France.
Coordinates:
 Email: adnan.hashmi@ciitlahore.edu.pk
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Primary Book:
 Artificial Intelligence: A Modern Approach (AIMA)
 Authors: Stuart Russell and Peter Norvig (3rd Ed.)
 Advisable that each student should purchase a
copy of this book
Reference Book:
1. Artificial Intelligence (Fourth Edition) by George F
Luger
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1.
Provide a concrete grasp of the fundamentals of
various techniques and branches that currently
constitute the field of Artificial Intelligence, e.g.,
1. Search
2. Knowledge Representation
3. Autonomous planning
4. Machine learning
5. Robotics etc.
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Course overview
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What is AI?
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A brief history of AI
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The state of the art of AI
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Introduction and Agents (Chapters 1,2)
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Search (Chapters 3,4,5,6)
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Logic (Chapters 7,8,9)
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Planning (Chapters 11,12)
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Learning (Chapters 18,20)
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Views of AI fall into four categories:
 Systems that act like humans
 Systems that think like humans
 Systems that act rationally
 Systems that think rationally
In this course, we are going to focus on systems
that act rationally, i.e., the creation, design and
implementation of rational agents.
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Turing (1950) "Computing machinery and
intelligence": "Can machines think?"  "Can
machines behave intelligently?"
Operational test for intelligent behavior: the
Imitation Game
Predicted that by 2000, a machine might have a
30% chance of fooling a lay person for 5 minutes
Anticipated all major arguments against AI in
following 50 years
Suggested major components of AI: knowledge,
reasoning, language understanding, learning.
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1960s “Cognitive Revolution": Informationprocessing psychology replaced prevailing
orthodoxy of behaviorism
Requires scientific theories of internal activities of
the brain. How to validate?
 Cognitive Science: Predicting and testing
behavior of human subjects
 Cognitive Neuroscience: Direct identification from
neurological data
Both approaches are now distinct from AI, and
share with AI the following characteristic:
 The available theories do not explain (or
engender) anything resembling human-level
general intelligence.
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Normative rather than descriptive
Aristotle: What are correct thought processes?
Several Greek schools developed various forms of logic:
 Notation and rules of derivation for thoughts (this
may or may not have proceeded to the idea of
mechanization)
Direct line through mathematics and philosophy to
modern AI
Problems:
 Not all intelligent behavior is mediated by logical
deliberation
 What is the purpose of thinking? What thoughts
should I have out of all the thoughts (logical or
otherwise) that I could have?
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Rational behavior: doing the right thing
The right thing: the optimal (best) thing that is
expected to maximize the chances of achieving
a set of goals, in a given situation
Doesn't necessarily involve thinking, but a
rational agent should be able to demonstrate it
artificially, in moving towards its goal
Aristotle (Nicomachean Ethics):
 Every art and every inquiry, and similarly
every action and pursuit, is thought to aim at
some good.
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An agent is an entity that perceives and acts
This course is about designing rational/intelligent
agents
Abstractly, an agent is a function from percept
histories to actions:
 f : P* -> A
For any given class of environments and tasks, we
seek the agent (or class of agents) with the
optimal (best) performance
Caveat: computational limitations make perfect
rationality unachievable
 So we attempt to design the best (most
intelligent) program, under the given resources.
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Philosophy: Logic, methods of reasoning, mind as
physical system, foundations of learning, language,
rationality
Mathematics: Formal representation and proof,
Algorithms, Computation, (un)decidability,
(in)tractability, probability
Psychology: Adaptation, phenomena of perception and
motor control, experimental techniques (with animals,
etc.)
Economics: Formal theory of rational decisions
Linguistics: Knowledge representation, grammar
Neuroscience: Plastic physical substrate for mental
activity
Control theory: Homeostatic systems, Stability, Simple
optimal agent designs.
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1943
1950
1956
1952-69
1950s
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
Dartmouth: "Artificial Intelligence“ adopted
Look, Ma, no hands!
Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
1965
Robinson's algo for logical reasoning
1966-73 AI discovers computational complexity
Neural network research almost disappears
1969-79 Early development of knowledge-based systems
1980-- AI becomes an industry
1986-- Neural networks return to popularity
1987-- AI becomes a science
1995-- The emergence of intelligent agents.
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Deep Blue defeated the reigning world chess
champion Garry Kasparov in 1997
No hands across America (driving autonomously
98% of the time from Pittsburgh to San Diego)
During the 1991 Gulf War, US forces deployed
an AI logistics planning and scheduling program
that involved up to 50,000 vehicles, cargo, and
people
NASA's on-board autonomous planning program
controlled the scheduling of operations for a
spacecraft
Proverb solves crossword puzzles better than
most humans.
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Speech technologies
 Automatic speech recognition (ASR)
 Text-to-speech synthesis (TTS)
 Dialog systems
Language Processing Technologies
 Machine Translation
 Information Extraction
 Informtation Retrieval
 Text classification, Spam filtering.
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Computer Vision:
 Object and Character Recognition
 Image Classification
 Scenario Reconstruction etc.
Game-Playing
 Strategy/FPS games, Deep Blue etc.
Logic-based programs
 Proving theorems
 Reasoning etc.
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