The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system in a generic way, identifying its main properties and functional components.

1. Introduction to
Intelligent Systems
Course: Intelligent Systems
Martin Molina
2020

2. • Specialized tasks in professional domains
– Medical diagnosis (e.g., recognize tumors on x-ray images)
– Airport gate assignment
• Tedious tasks
– Autonomous car driving
– Domestic tasks (e.g., house cleaning)
• Dangerous tasks
– Exploration of unknown areas (e.g., underwater exploration)
What do we mean by intelligent system?
1
Examples of tasks:
We may see an intelligent system as a tool designed to perform tasks for us
that require intelligence

3. 2
• We are not interested in deciding whether a system is intelligent or not
• We are interested in having tools for systems engineers
We are in an engineering context
Engineers who need to
conceive, analyze, design
and program efficiently
intelligent systems
Tools like design metaphors,
architectural patterns,
computational methods and
software tools

4. How can we characterize an intelligent system?
We can distinguish three main properties:
1. Working in a complex world
2. Primary cognitive abilities (e.g., perception, language use, etc.)
3. Complex intelligent behavior (e.g., rationality, learning, etc.)
3
Molina, Martin (2020). What is an intelligent system?. ArXiv preprint arXiv:2009.09083
https://arxiv.org/pdf/2009.09083.pdf

5. How can we characterize an intelligent system?
We can distinguish three main properties:
1. Working in a complex world
2. Primary cognitive abilities (e.g., perception, language use, etc.)
3. Complex intelligent behavior (e.g., rationality, learning, etc.)
4

6. Property 1: Working in a complex world
• An intelligent system operates in an environment and interacts with other agents
(a user or other individuals)
• The system observes features from the environment through sensors and
performs actions using actuators
• The use of sensors and actuators (real or virtual) separates the body of the
intelligent system from the rest of the environment (“embodiment”)
5
Sensors
Actuators
Act
Sense
Environment
Intelligent
system
Sensors
Actuators
Communicate
Other agents

7. Act
[Heat/not heat]
Sense
[Room temperature]
Performance measure
[Energy consumption]
Environment
[House]
Example: Thermostat
Communication
[Manual controller]
Thermostat
6

8. System Environment User
Observable
features
Actions
Performance
measure
Self-driving car
Roads, cars,
pedestrians, …
Passenger
Images from
cameras,
coordinates from
GPS, speed
Steering,
accelerator,
brake, signal,
horn
Safety, travel
time, comfort,
fuel
consumption
Medical
diagnosis
system
Patients Physician
Test results,
medical history,
etc.
Drug
prescriptions,
proposed tests
Health, costs
of tests and
treatment
Chemistry
tutor system
Chemistry
students
Instructor
Answers given by
students
Tests,
proposed
exercises,
proposed
readings
Student’s
score on tests
Other examples
7

9. There are different properties that define the complexity
of the environment with respect to the system
• Static / dynamic
The environment (doesn’t change / changes) while an
agent is deliberating
• Discrete / continuous
The state of the environment, time, percepts or actions
(are discrete / are continuous)
• Fully-observable / partial-observable
Sensors (detect / don’t detect) all aspects that are
relevant to the choice of action
• Deterministic / stochastic
The next state of the environment (is / isn’t) completely
determined by the current state and the action
• Episodic / sequential
Actions (don’t influence / influence) future actions
• Known / unknown
The outcomes for actions (are known / are not
known) by the agent in advance
[Russell, Norvig, 2009]
Act
Sense
Environment
Intelligent
system
8Russell, S., Norvig P. (2009). Artificial Intelligence: A Modern Approach (3rd edition). Pearsons Education Limited.

10. • Static / dynamic
• Discrete / continuous
• Fully-observable / partial-observable
• Deterministic / stochastic
• Episodic / sequential
• Known / unknown
Chess player
Example: Environment of a chess player
9

11. • Static / dynamic
• Discrete / continuous
• Fully-observable / partial-observable
• Deterministic / stochastic
• Episodic / sequential
• Known / unknown
Self-driving car
Example: Environment of a self-driving car
10

12. How can we characterize an intelligent system?
We can distinguish three main properties:
1. Working in a complex world
2. Primary cognitive abilities (e.g., perception, language use, etc.)
3. Complex intelligent behavior (e.g., rationality, learning, etc.)
11

13. 12
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge
University Press.
John B. Carroll
Professor of Psychology
University of Chicago
(1920 -2003)
We can identify multiple cognitive abilities
1993
Cognitive ability:
Ability that requires to process mental information

14. We follow a pragmatic engineering approach
to identify cognitive abilities
• We identify abilities that:
– Are common in computer systems
– Can be implemented with AI methods
• We consider two separated levels:
– Primary abilities (basic abilities)
– Secondary abilities (abilities that use models of other abilities)
13

15. Environment
Other agents
Reasoning about the
world and making
decisions about
what to do
Deliberation
Action
control
Perception
Interaction
Control the
execution of the
own actions
Extraction of relevant
data from the
observed world
Interaction with other
agents (e.g., using
language)
Property 2: Primary cognitive abilities
14

16. Interaction
(Passenger requests destination)
Passenger
Steering
Pedes-
trians
Traffic
signals
Vehicles
Accele-
ration
Braking
Environment
Example: Autonomous car
Deliberation
(Path planning to reach the requested destination)
Perception Action control
Data extraction
from ultrasonic
sensors, radar, lidar,
camera and GPS
Control of
driving
mechanisms
15

17. Interaction
Other agents
A1P1 P2 Pm A2 An
Environment
Perception and action control are usually
divided in multiple components
Deliberation
Perception Action control
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Parallel
Serial

18. Gap
Interaction
P1 P2 Pm
Environment
Deliberation
Perception Action control
Attention
mechanisms
Symbol
grounding
Execution
control
Cognizant
failure
The gap between deliberation and
perception-actuation requires specific abilities
Other agents
17
A1 A2 An

19. Environment
Other agents
Deliberation
Action
control
Perception
Interaction
18
Reactive behavior
Generation of instantaneous actions in response to a
stimulus (e.g., animal reflexes or in decisions based on
intuitions).
Advantage:
• Efficient reaction to dynamic events in a dynamic
environment (it uses limited memory about the world)
Deliberation
Making decisions about what to do based on justifiable
reasons
Advantages:
• Reactive behaviors can be inhibited to reach more useful
long-term goals
• Decisions are consistent with own knowledge
“Action control” provides “reactive behavior”

20. Advisor system
• Helps the user to act in the environment
• The user makes decisions about what to do
Autonomous system
• Acts in the environment to help the user
• The system makes decisions about what to do
Help me
perform
task T
Perform
task T
for me
We can distinguish two types of systems
according to who acts in the environment
Act
Sense
SuggestionCompletion Environ-
ment
Intelligent
advisor
system
User
Act
Sense
Environ-
ment
Intelligent
autonomous
system
User
19
(a) (b)

21. An intelligent system may interact with other system
Request
Answer
Act
Sense
Environ-
ment 1
Intelligent
system
1
20
Act
Sense
Environ-
ment 2
Intelligent
system
2

22. Intelligent systems can be part of
multiagent systems creating complex organizations
Partial
environment
Partial
environment
Partial
environment
Partial
environment
Partial
environment
Global environment
Agent Agent Agent Agent Agent
21
Sense
and act
Sense
and act
Sense
and act
Sense
and act
Sense
and act
Communicate
Communicate Communicate Communicate Communicate
Communicate

23. How can we characterize an intelligent system?
We can distinguish three main properties:
1. Working in a complex world
2. Primary cognitive abilities (e.g., perception, language use, etc.)
3. Complex intelligent behavior (e.g., rationality, learning, etc.)
22

24. Environment
Other agents
Deliberation
Action
control
Perception
Interaction
Property 3: Complex intelligent behavior
3.1. Acting rationally
3.2. Adaptation through learning
3.3. Introspection
23

25. A system acts rationally if it makes decisions to obtain the maximum
performance measure
Examples:
• A chess player selects the movement that maximizes the expectation of
winning the game
• A self-driving car selects the best route to reach a destination considering
possible traffic jams
Implementation:
• The expected performance measure of actions is usually uncertain
• Rational behavior can be explicitly programmed using algorithms from
decision theory (with a probabilistic representation)
24
Sub-property 3.1: Acting rationally

26. 25
Rational decisions affect different cognitive abilities
Environment
Other agents
Deliberation
Action
control
Perception
Interaction
What is the next
question to ask the
user?, …
What part of the
environment requires
more attention?, …
What is the right
action to do?, what is
the right method to
perform a task?, …
What is the right
method to control an
action?, …

27. The system is capable of improving its performance over multiple interactions
with the world
Examples:
• A chess player improves its capacity to win by learning from game
experience
• A self-driving car reduces the time to reach destination in a city by learning
from the experience of urban trips
26
Sub-property 3.2: Adaptation through learning

28. 27
Adaptation through learning can affect
different cognitive abilities
Environment
Other agents
Deliberation
Action
control
Perception
Interaction
Learning user
preferences, …
Learning
relevance of
features, …
Learning by
deduction using a
world model, …
Improving action
control by
learning (object
manipulation, …)

29. • Capacity to analyze one's cognitive abilities
– The system uses an observable model of its own abilities
– This model is used to simulate self-awareness processes
Practical utility:
• Allows the system to judge its own actions
– This provides feedback to be able to learn
(this feedback can also be done by simulating reactive feelings)
• Allows the system to generate explanations
– E.g., the system is able to justify recommended decisions to the user
28
Sub-property 3.3: Introspection

30. Summary of properties of an intelligent system
1. Working in a complex world
– Environment
– Other agents (e.g., user)
2. Primary cognitive abilities
– Perception
– Action control
– Deliberation
– Interaction
3. Complex intelligent behavior
– Acting rationally
– Adaptation through learning
– Introspection
29
Environment
Other agents
Deliberation
Action
control
Perception
Interaction
• Acting rationally
• Adaptation through learning
• Introspection
Intelligent
system

31. Scientific journals
• IEEE intelligent systems (IEEE)
• Knowlegde-based systems (Elsevier)
• Expert systems with applications (Elsevier)
• Engineering applications of artificial intelligence (Elsevier)
• International journal on artificial intelligence tools (World Scientific)
Associations
• AAAI: http://aaai.org
• ECCAI: http://www.eccai.org
There are multiple sources of
information about intelligent systems
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32. Lecture slides of master course “Intelligent Systems”.
© 2020 Martin Molina
This work is licensed under Creative Commons license CC BY-SA 4.0:
https://creativecommons.org/licenses/by-sa/4.0/legalcode
Suggested work citation:
Molina, M. (2020): “Intelligent Systems”. Master course (lecture slides). Department of
Artificial Intelligence. Universidad Politécnica de Madrid.
31