This document discusses different approaches to creating artificial intelligence: symbolic, sub-symbolic, and statistical. It also discusses different types of intelligent agents, including rational agents. The symbolic approach uses logic and rules defined by humans, while the sub-symbolic approach uses neural networks and pattern recognition. The statistical approach uses machine learning techniques to analyze data and make conclusions. Rational agents are defined as those that perceive their environment and act in a way to achieve their goals. Different examples of agents are provided, like a taxi driver and medical diagnostic system. The document also discusses the PEAS (Performance, Environment, Actuators, Sensors) framework for defining agents and their operation.

1. Artificial Intelligence
Nimra Shafiq
Lecturer
nimra.shafiq@cs.uol.edu.pk
The University of Lahore

2. How Does One Make A.I?
• Symbolic
• Sub-Symbolic
• Statistical

3. Symbolic Approach
• Logic-based
• All knowledge is “taught” by humans
• Everything is in a controlled environment
• To mimic human-intellect, it would take millions of trigger
conditions and rules
• This approach was popular in the 60s and 70s

4. Symbolic Example
• Video Game – Decision Trees

5. Sub-Symbolic Approach
• Pattern-based and/or Fuzzy Logic is
applied to determine the result of
decisions
• Uses a bottom-up/inductive approach
• Information is processed in parallel via
neurons
• This mimics the structure of the human
brain
• Improves on the Symbolic Approach
• This approach was popular in the 80s-00s

6. SubSymbolic Examples
• Neural Networks

7. Statistical Approach
• Database-based
• The A.I. is programmed with functions to analyze data and
creates its own conclusion
• Uncontrolled environment, although certain parameters are
provided
• Improves on Sub-Symbolic approach
• This approach began in 00s

8. Statistical Example
81.7% 76.7%
74.8%

9. Agents
• Agent World
• Rational Agents
• Examples
• Agent Types

10. Agent World
Perceive
Reason
Act
[ f: P*  A ]

11. Intelligent Agents
• The agent function maps from percept histories to
actions
• The agent program runs on the physical architecture
to produce actions

12. Rational Agents
• An agent is an entity that perceives and acts
• Every thing to be discussed should be taken in the context of :
RATIONAL AGENTS
• Abstractly, an agent is a function from percept histories to actions:
[f: P*  A]
• For a given class of environments/tasks, Rational Agents sought best
performance

13. Rational Agent
An agent is anything that can be viewed as perceiving its
environment through sensors and acting rationally upon the
environment through effectors
A rational agent is one that does the right thing

14. Examples of Agents
Humans Programs Robots___
senses keyboard, mouse, dataset cameras, pads
body parts monitor, speakers, files motors, limbs

15. PEAS
PEAS
Performance
Environment
Actuators
Sensors
Goals
Utility
Function
Static Vs
Dynamic
Inaccessible
vs
Accessible
Deterministic
Vs
Stochastic
Discrete
Vs
Continous
Episodic
Vs
Sequential
Level of
Autonomy
Level of
Rationality
(omniscient agent – always knows
the rational outcome of an action)

16. PEAS – Taxi Agent
Taxi Driver Example
Performance
Measure
Environment Actuators Sensors
safe, fast,
legal,
comfortable
trip,
maximize
profits
roads, other
traffic,
pedestrians,
customers
steering,
accelerator,
brake,
signal, horn,
display
camera,
sonar,
speedometer,
GPS,
odometer,
engine
sensors,
keyboard,
accelerator
DARPA urban challenge 07:
http://www.youtube.com/watch?v=SQFEmR50HAk

17. PEAS – Medical Diagnostic System
Medical Diagnosis System
Performance
Measure
Environment Actuators Sensors
healthy
patient,
minimize
costs, lawsuits
patient,
hospital, staff
display
questions,
tests,
diagnosis,
treatments,
referrals
keyboard
entry of
symptoms,
findings,
patient’s
answers

18. Mushroom Picking Agent
CS 420: Artificial Intelligence 19
Mushroom-Picking Robot
Performance
Measure
Environment Actuators Sensors
Percentage of
good
mushrooms in
correct bins
Conveyor belt
with
mushrooms,
bins
Jointed arm
and hand
camera, joint
angle sensors

19. Rhino

20. Map generation
and localization

22. • Is this intelligent Agent ? If YES, where is the BRAIN – Thinking Process
• It has been encoded as a PLAN inside the machine
• It acts rationally most of the time, so it is intelligent agent.

24. PEAS
PEAS
Performance
Environment
Actuators
Sensors

25. PEAS
PEAS
Performance
Goals
Utility
Function
Goal: get from one exhibit to
another
Knowledge about the goal:
whereabouts it is
Need this to guide its actions
(movements)
RHINO searched for paths from its
current location to an exhibit, using
the distance from the exhibit as a
utility function.
However, this was complicated by
visitors getting in the way.

26. PEAS
PEAS
Environment Static Vs
Dynamic
Inaccessible
vs
Accessible
Deterministic
Vs
Stochastic
Discrete
Vs
Continuous
Episodic
Vs
Sequential
RHINO had to deal with "invisible" objects. These included glass cases
and bars at various heights which could not be detected by the robotic
sensors. These are clearly inaccessible aspects of the environment,
RHINO had to take an informed guess/plan accordingly.
The environment was
dynamic, people could
bring chairs in front of the
robot. Fast decision making
(planning route)
RHINO's data came from 3d
space, hence was considered
continuous. Had vision
system in place
RHINO's world was non-deterministic because people
moved around, and they move objects such as chairs
around. In fact, visitors often tried to trick the robot by
setting up roadblocks with chairs. This was another
reason why RHINO's plan was constantly updated.
Getting to and from exhibits, the individual trips between exhibits can be seen as episodes. If
goal of giving a guided tour is considered, however, RHINO must at least remember the
exhibits it had already visited, at the top level, its actions were not episodic.

27. So Rhino is an
Agent
• How is an agent designed?
• What are the ingredients?
• How many types are there ?

28. Slides Credit
Stanford Lectures
Artificial Intelligence: A modern Approach