1. ISC 4322/6300 – GAM 4322 Artificial Intelligence
University Of Houston- Victoria
Computer Science Department
ISC 4322/6300 – GAM 4322 Artificial Intelligence
Lecture 1
The Foundations of AI
and Intelligent Agents

2. Contacts Instructor: Dr. Alireza Tavakkoli Office: UC 101C
Mon. 5:00 – 6:00 pm
Office hours: Wed. 3:00 – 4:00 pm
or by appointment
URL:
Or Blackboard

3. Class Goals
Introduce state-of-the-art algorithms in artificial intelligence
Show how these algorithms apply to real-world problems
Provide practice in applying algorithms by solving “real” problems (Games)
After this course, you should be able:
Evaluate claims about intelligent systems in an informed way
Have a great algorithmic toolbox to help you design adaptive systems
Build intelligent agents for interesting agents
Contribute to AI research

4. Structure of Course
Part I: Decision-Making in Deterministic Environments
Single agent: Dynamic programming, informed search techniques, randomized search, genetic algorithms, constraint satisfaction, planning
Multi-agent: Game theory, mini-max search and approximations
Part II: Decision-Making in Stochastic Environments
Single agent: Bayesian Networks, Hidden Markov Models, Kalman and Particle Filters, decision and utility theory, Markov Decision Processes
Multi-agent: Expectimax search, stochastic game theory
Part III: Learning in Unknown Environments
Supervised learning: Decision Trees, Support Vector Machines, Neural Networks
Unsupervised learning: reinforcement learning

5. What is Expected Exams Assignments Grading Policy
One mid-term, one Final
Assignments
3-4 Homeworks, 2-3 Programming Assignment
Projects can be complete by pairs of students.
Late assignments: (2n)2% Penalty
Grading Policy
Homework 15% < A
Projects 25% B
Midterm 25% C
Final 30% D
Participation 5% < F

6. What is AI? Humanly vs. Rationally Thinking vs. Acting
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning”
(Bellman,1978)
“The study of mental faculties through
the use of computational models.”
(Winston, 1992)
“AI is concerned with rational action…
and studies the design of rational agents.
A rational agent acts so as to achieve
the best expected outcome.”
(S.R. & P.N., 1995)
“The art of creating machines that
perform functions that require
intelligence when performed by people.”
(Kurzweil, 1990)

7. What is AI? Humanly vs. Rationally Thinking vs. Acting 1
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning”
(Bellman,1978)
“The study of mental faculties through
the use of computational models.”
(Winston, 1992) 1
“AI is concerned with rational action…
and studies the design of rational agents.
A rational agent acts so as to achieve
the best expected outcome.”
(S.R. & P.N., 1995)
“The art of creating machines that
perform functions that require
intelligence when performed by people.”
(Kurzweil, 1990)

8. Acting Humanly Turing Test (1950) Capabilities that the
computer would need
to possess:
Natural language processing
Knowledge representation
Automated reasoning
Machine learning
Alan Turing
( )
The total Turing test requires also computer vision and robotics
The quest for “artificial flight” succeeded when the Wright brothers
and others stopped imitating birds and learned about aerodynamics.

9. What is AI? Humanly vs. Rationally 2 Thinking vs. Acting
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning”
(Bellman,1978)
“The study of mental faculties through
the use of computational models.”
(Winston, 1992)
“AI is concerned with rational action…
and studies the design of rational agents.
A rational agent acts so as to achieve
the best expected outcome.”
(S.R. & P.N., 1995)
“The art of creating machines that
perform functions that require
intelligence when performed by people.”
(Kurzweil, 1990)

10. Thinking Humanly Simon and Newel came up with the
General Problem Solver – GPS (1961).
GPS can solve (in principle) every problem that can be formalized symbolically, e.g. Towers of Hanoi
Herbert Simon
( )
Allen Newel
( )
First system to separate knowledge of problems from its strategy on how to solve them
“Over Christmas, Allen Newell and
I created a thinking machine.”
Herbert Simon, 1961
Main concern by Simon and Newel:
Is the trace of GPS’ reasoning the same as the trace of a human?
But this if the focus of…. Cognitive science:
Requires experimental investigation of actual humans or animals

11. What is AI? Humanly vs. Rationally Thinking 3 vs. Acting
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning”
(Bellman,1978)
“The study of mental faculties through
the use of computational models.”
(Winston, 1992)
“AI is concerned with rational action…
and studies the design of rational agents.
A rational agent acts so as to achieve
the best expected outcome.”
(S.R. & P.N., 1995)
“The art of creating machines that
perform functions that require
intelligence when performed by people.”
(Kurzweil, 1990)

12. Thinking Rationally “Socrates is a man; all men are mortal;
therefore, Socrates is mortal.”
Logic-based AI
Logic provides:
A syntax for describing the world and relations among objects
A way to achieve logical/correct inferences
Create intelligent systems that
reason using the syntax and the laws of logic
By 1965 there were programs that could in principle, solve any solvable problem described in logical notation.
Aristotle
( BC)
But…
How to represent knowledge in logical notation when we are less than 100% certain?
Computational explosion: A few hundred of facts exhaust the computational resources of any computer when it attempts logical inference without some sort of guidance

13. What is AI? Humanly vs. Rationally Thinking vs. Acting 4
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning”
(Bellman,1978)
“The study of mental faculties through
the use of computational models.”
(Winston, 1992) 4
“The art of creating machines that
perform functions that require
intelligence when performed by people.”
(Kurzweil, 1990)
“AI is concerned with rational action…
and studies the design of rational agents.
A rational agent acts so as to achieve
the best expected outcome.”
(Russell and Norvig, 1995)

14. Acting Rationally
Logical inference is part of intelligence. It does not cover everything:
e.g., might be no provably correct thing to do, but still something must be done
e.g., reflex actions can be more successful than slower, carefully deliberated ones
What is a rational action?
One that achieves the best outcome
(or in the case of uncertainty… the best expected outcome)
Stuart
Russell
It is this objective that requires:
Natural language processing
Knowledge representation
Automated reasoning
Machine Learning
Computer Vision
Robotics
“Human behavior is well adapted for
one specific environment and is the
product, in part, of a complicated and
largely unknown evolutionary process
that is still far from producing perfection.”
Peter
Norvig

15. The Foundations of AI
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
Economics utility, decision theory
Neuroscience physical substrate for mental activity
Psychology phenomena of perception and motor control, experimental techniques
Computer building fast computers engineering
Control theory design systems that maximize an objective function over time
Linguistics knowledge representation, grammar

16. AI History – Modern Era
McCulloch & Pitts: Boolean circuit model of brain
Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted
1952—69 Look, Ma, no hands!
1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine
1965 Robinson's complete algorithm for logical reasoning
1966—73 AI discovers computational complexity Neural network research almost disappears
1969—79 Early development of knowledge-based systems
AI becomes an industry
Neural networks return to popularity
AI becomes a science
The emergence of intelligent agents

17. State of the Art
Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
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

18. Where are we now?
SKICAT: automatically classifies data from space telescopes and indentifies interesting objects in the sky. 94% accuracy, way better than human (decision trees)
Deep Blue: the first computer program to defeat human champion Garry Kasparov (minimax search + alpha-beta-pruning + optimizations)
Pegasus, Jupiter, etc.: speech recognition systems (Hidden Markov Models)
HipNav: a robot hip-replacement surgeon (planning algorithms)
DARPA Grand/Urban Challenge: autonomous driving 98% of the time from Pittsburgh to San Diego (filtering and planning algorithms)
Deep Space 1: NASA spacecraft that did an autonomous flyby an asteroid (logic- based AI)
Credit card fraud detection and loan approval (decision trees and neural networks)
Chinook: the world checker’s champion (game theory)
Spam Assassin and other spam detectors (naïve Bayes learning)
Soccer playing Aibo robots (reinforcement learning)

19. Where are we now?

21. Intelligent Agents Agent ? How to fully describe an AI problem:
Percepts
Environment
Sensors
How to fully describe an AI problem:
Performance measure
Environment
Actuators
Sensors ?
Actions
Actuators
Intelligent (rational) agent
For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge of the environment the agent has
Definition encompasses both physical and virtual/software agents

22. Vacuum-Cleaner World Performance Measure Environment Actions Percepts
Award One Point per Clean Cell Over 1000 Time Step
Environment
Two Cell World
Actions
Left, Right, Suck, NoOp
Percepts
Location and Contents, e.g., [A,Dirty]
Is this a rational agent?

23. Rational Agents
Rationality is distinct from omniscience (all-knowing with infinite knowledge)
Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)
An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

24. PEAS PEAS: Performance measure, Environment, Actuators, Sensors
Must first specify the setting for intelligent agent design
Consider, e.g., the task of designing an automated taxi driver
Agent Type
Performance Measure
Environment
Actuators
Sensors
Taxi Driver
Safe
Fast
Legal
Comfortable
Profitable
Roads
Other Traffic
Pedestrians
Customers
Steering
Accelerator
Brake
Signal
Horn
Display
Cameras
Sonar
Speedometer
GPS
Odometer
Engine Sensors
Keyboard

25. Properties of Environments
Fully observable vs. partially observable:
If the agent has always access to the complete state of the environment, then the environment is fully observable
Partial observability due to: noisy, inaccurate sensors or sensor limitations
Deterministic vs. stochastic:
If the next state of the world is completely determined by the current state and the agent’s action, then the environment is deterministic.
If the environment is deterministic except from the actions of other agents, then it is called strategic.
Episodic vs. sequential:
In an episodic environment the agent’s experience does not depend on the actions taken in previous episodes.
In sequential environments, the current decision could affect all future decisions.

26. Properties of Environments
Static vs. dynamic:
If the environment changes while the agent is not acting, then the environment is dynamic.
Discrete vs. continuous:
Continuous values can appear in:
The state of the environment, time, the percepts or actions of the agent.
Single agent vs. multiagent:
When are the other entities in the world considered agents?
Competitive environments:
When they maximize an performance measure that depends on the agent’s actions
Cooperative environments:
When they communicate or follow common rules so as to satisfy a common performance measure

27. Agent Functions and Programs
How does the inside of a rational agent work?
An agent is completely specified by the agent function mapping percept sequences to actions
Agent = Architecture + Program
Agent Program vs. Agent Function
Program takes the percept and returns the action
Function takes the entire percept history
One agent function (or a small equivalence class) is rational
Aim: find a way to implement the rational agent function concisely

28. A Table Driven Agent Drawbacks: Huge table
A Table Driven Agent Program
Function TABLE-DRIVEN-AGENT (percept) returns action
static percepts, a sequence, initially empty
table, a table of actions, initially fully specified
append percept to the end of percepts
action Lookup (percepts, table)
return action
Drawbacks:
Huge table
Take a long time to build the table
No autonomy
Even with learning, needs a long time to learn the table entries

29. Reflex Vacuum Agent Program
Function REFLEX-VACUUM-AGENT ([location, status]) returns action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
Sensing input:
[Cell Id, Dirty or Not Dirty]
Actions:
[Clean, move Left, move Right, NoOp]
Advantages
Small Table
Cutting Down the Table Size by Eliminating Percept History
Action Does Not Depend on the Location

30. How do agents work? ? Agent Percepts Environment Actions Sensors
Actuators

31. Reflex Agents Agent Percepts Environment Actions Sensors Actuators
What the world is like now?
Condition-action
(if-then)
rules
What action should I do now?
Actions
Actuators

32. Model-based Reflex Agents
Environment
Sensors
Percepts
State
What the world is like now?
How the world evolves?
What my actions do?
Condition-action
(if-then)
rules
What action should I do now?
Actions
Actuators

33. Goal-based Agents Agent Percepts Environment Actions Search & Planning
Sensors
Percepts
State
What the world is like now?
How the world evolves?
What my actions do?
What it will be if I do action A?
What action should I do now?
Goals
Actions
Actuators
Search & Planning

34. Utility-based Agents Agent Percepts Environment Actions
Sensors
Percepts
State
What the world is like now?
How the world evolves?
What it will be if I do action A?
What my actions do?
How happy I will be in the state?
Utility
What action should I do now?
Actions
Actuators
Utility Function: X (state space)  

35. Learning Agents Agent Performance standard Percepts Environment
State
Sensors
Percepts
feedback
changes
Learning
element
Performance
Element
knowledge
learning
goals
Problem
generator
Actions
Actuators

36. Intelligent Agents

37. Types of Agents

38. Question?
AI: A Modern Approach :
Chapters 1 & 2