# What is an intelligent system?

Martin Molina  Dept. of Artificial Intelligence, Universidad Politécnica de
Madrid

(February 2022)

###### Abstract

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 paper is to give a general description of an
intelligent system, which integrates previous approaches and takes into
account recent advances in artificial intelligence. The paper describes an
intelligent system in a generic way, identifying its main properties and
functional components, and presents some common categories. The presented
description follows a practical approach to be used by system engineers. Its
generality and its use is illustrated with real-world system examples and
related with artificial intelligence methods.

##  1 Introduction

Mankind has made significant progress through the development of increasingly
powerful and sophisticated tools. In the age of the industrial revolution, a
large number of tools were built as machines that automated tasks requiring
physical effort. In the digital age, computer-based tools are being created to
automate tasks that require mental effort. The capabilities of these tools
have been progressively increased to perform tasks that require more and more
intelligence. This evolution has generated a type of tool that we call
intelligent system.

Intelligent systems help us performing specialized tasks in professional
domains such as medical diagnosis (e.g., recognize tumors on x-ray images) or
airport management (e.g., generate a new assignment of airport gates in the
presence of an incident). They can also perform for us tedious tasks (e.g.,
autonomous car driving or house cleaning) or dangerous tasks such as
exploration of unknown areas (e.g., underwater exploration).

The development of such a type of systems is now an engineering discipline of
information technology that requires effective methods and tools. The precise
characterization of an intelligent system is non trivial because it is based
on terms related to cognition, an area that is not fully understood and admits
different interpretations. Some of the used terms can even change with the
proposal of new computational models of intelligence and new scientific
findings related to our understanding of the mind.

The main purpose of this paper is to present a characterization of an
intelligent system that integrates and updates previous conceptions of such
type of system. It follows a pragmatic approach to be useful in an engineering
context to help system engineers conceive, analyze and build intelligent
systems by using concepts and terminology commonly accepted in artificial
intelligence. Therefore, the presented description focuses on functional
characteristics related to the intelligence that machines can exhibit with the
current state of the technology.

The remainder of the paper presents the following contents. Section 2 presents
a definition of intelligent system with the description of its properties and
its main functional components. Section 3 describes aspects in the development
process of an intelligent system such a types of artificial intelligence
methods used to develop the different components of intelligent systems.
Section 4 describes classes of intelligent systems using the presented
characterization, covering both recent achievements and classical approaches
of this technology .

##  2 Definition of intelligent system

Intelligent systems have been characterized in the literature of AI using the
concept of intelligent agent [44] [13] [40]. This concept emphasizes the
ability to act111The essential meaning of the word agent can be expressed as
”one who acts”, based on its derivation from the Latin word agens. on an
environment. Table 1 shows a sample of definitions of intelligent system
considered as an agent. These definitions highlight that the system works in
an environment, has a set of capabilities (perception, learning, etc.), and
makes decisions about how to act.

The word system, instead of agent, is used by some authors [26] and in
academic areas of information technology such as the names of university
courses or academic journals (e.g., IEEE Intelligent Systems and International
Journal of Intelligent and Robotic Systems). In this case, the word system
emphasizes the presence of multiple components that must be adequately
combined to create intelligence. Knowing how to do this combination
efficiently is one of the key aspects in the development of such type of
systems.

Definition 1: Intelligent agents are computational systems that inhabit some
complex dynamic environment, sense and act autonomously in this environment,
and by doing so realize a set of goals or tasks for which they are designed
[24]
---
Definition 2: Intelligent agents continuously perform three functions:
perception of dynamic conditions in the environment; action to affect
conditions in the environment; and reasoning to interpret perceptions, solve
problems, draw inferences, and determine actions [16]
Definition 3: Intelligent agents operate autonomously, perceive their
environment, persist over a prolonged time period, adapt to change, and create
and pursue goals [40]
Definition 4: Intelligent agents are autonomous or semi-autonomous hardware or
software systems that perform tasks in complex, dynamically changing
environments. Agents communicate with their environment and effect changes in
their environment by executing actions [33]
Table 1: Sample of definitions of intelligent agent.

The characterization of an intelligent system used in this paper integrates
parts of the previous agent-based definitions. For example, the identification
of a separate environment where the agent works is important to give an
adequate operational context at a certain degree of abstraction. In our
definition, we also distinguish from the environment other agents that may
interact with the system (e.g., a human user or other artificial systems) in
order to consider the social factor of an intelligent system.

This paper also uses a set of capabilities usually enumerated by agent-based
definitions (e.g., perception, reasoning, learning, etc.). However, we
separate these capabilities into two parts. On the one hand, there are four
primary cognitive abilities: perception, action control, deliberative
reasoning, and language use (to interact with others). On the other hand, we
distinguish the capacity to adapt to changes in the world through learning.
This capacity is applicable to each primary cognitive ability.

Finally, our characterization of an intelligent system takes also into account
that the system operates according to two behavioral principles: acting
rationally and following social norms. The first principle considers that the
system behavior is consistent with the individual goals to be achieved (see
the following sections). The second principle considers that the system has
certain ethical behavior to follow the norms established by the social group
with which it cooperates.

According to this, the definition that we follow in this paper a basic scheme
to structure the further characterization of an intelligent system is the
following:

Definition. An intelligent system (1) operates in an environment with other
agents, (2) possesses cognitive abilities such as perception, action control,
deliberative reasoning or language use, (3) follows behavioral principles
based on rationality and social norms, and (4) has the capacity to adapt
through learning.

It is important to note that this definition is not intended to be a rigid
characterization to determine whether a system is intelligent or not. Instead,
the definition includes usual characteristics related to intelligence that may
or may not be present in the analysis or development of specific intelligent
systems.

For example, a simple case of intelligent system could be a simple reflex
agent, as it is called by Russel and Norvig [40]. This type of system
possesses perception and control action abilities and it does not have other
characteristics such as learning or deliberative reasoning. A collaborative
autonomous robot is a more complex example of intelligent system. In this
case, the robot interacts with the user and with the environment in which it
operates. The robot has the cognitive abilities of perception, action control,
deliberative reasoning (e.g., for motion planning) and language use (to
communicate with the user). It can also be assumed that the robot has adaptive
capabilities with the help of machine learning techniques.

The following sections describe in more detail the four properties of our
definition of intelligent system, presenting additional concepts and
categories in which the elements of a system may be classified.

###  2.1 Property 1: Working in an environment with other agents

As mentioned in the previous section, one of the properties of an intelligent
system is that it works in an environment together with other agents with
which the system can communicate (e.g., a human user, artificial computer-
based agents, etc.). The system observes features from the environment through
sensors and performs actions using actuators (see Figure 1). The use of
sensors and actuators (real or virtual) separates the body of the intelligent
system from the rest of the environment. This characteristic is called
embodiment.

Figure 1: An intelligent system interacts with the environment and with other
agents.

##### Example.

A thermostat is a simple example that illustrates the concept of this kind of
interaction. The goal of the thermostat is to regulate the temperature in the
environment (e.g., house rooms). The thermostat uses a thermometer to sense
the temperature of the room, and actuates by starting or stopping a heater.
The thermostat also communicates with a human user who starts and stops the
thermostat, or establishes the desired temperature.

####  2.1.1 Environment

Usually, the complexity of the environment is significantly higher than the
information processing capacity of the system. The amount of components of the
environment, their interactions or the anticipated effect of potential actions
in the environment cannot usually be completely represented in the memory of
the intelligent system. For example, in the chess game, it is not possible to
anticipate the exact effect of all potential movements, because the amount of
combinations is too high. The degree of complexity of the environment with
respect to the agent can be described with the following features [43, 40]:

  * •

Static (or dynamic), the environment does not change (or changes) while an
agent is deliberating,

  * •

Discrete (or continuous), the state of the environment, time, percepts or
actions are discrete (or continuous),

  * •

Fully-observable (or partial-observable), sensors detect (or do not detect)
all aspects that are relevant to the choice of action,

  * •

Deterministic (or stochastic), the next state of the environment is (or it is
not) completely determined by the current state and the action,

  * •

Episodic (or sequential), actions do not influence (or they influence) future
actions,

  * •

Known (or unknown), the outcomes for all actions are known (or they are not
known) by the agent in advance.

##### Example.

The environment of a chess player is static, discrete, fully observable,
deterministic, sequential and known. In the case of a self-driving car, the
environment is continuous, partial observable, stochastic, sequential and
known.

####  2.1.2 Other agents

An intelligent system may interact with other agents as part of an
organization. Such an organization can be viewed as a multi-agent system in
which individual agents interact using social coordination mechanisms to
cooperate to achieve common goals or to compete for limited resources.

Figure 2: Comparison between (a) autonomous system and (b) advisor system.

An artificial intelligent system interacts at least with a human user that
acts as the system’s owner. The user starts and stops the execution of the
intelligent system and may adjust specific parameters to modify the the
system’s behavior (for example, the owner of chess player can adjust the level
of the game). Depending on who is acting on the environment (the system or the
user), an artificial system can play two different roles during its
interaction with the user (Figure 2):

  * •

Autonomous. An autonomous system acts in the environment to help the user.
This means that the user delegates to the system a task to be performed and
the system makes its own decisions on how to act in the environment to
autonomously perform that task. An autonomous system can also reject requested
tasks from the user according to certain reasons, based on the current
situation of the environment or its own goals (e.g., safety goals or social
norms). For this purpose, the system can verify the correctness and
feasibility of a requested task before it is performed and explain to the user
the reasons that justify why a requested task is rejected.

  * •

Advisor. An advisor system helps the user act in the environment. In this
case, the user is the one who makes the decisions about what actions to do in
the environment. The role of the system is to give advice by providing useful
information to facilitate such decisions to the user. The system may help the
user in prescriptive tasks such as planning, scheduling or resource
assignment, but also in analytic tasks such as problem detection, problem
diagnosis and temporal projection.

During the interaction, an intelligent system may be proactive instead of
passive222Some authors use the term reactive as the opposite of proactive.
However, in this paper we will use the term reactive as the opposite of
deliberative as it is generally used in agent-based systems.. This means that
the system does not have to wait until a user requests a task, but it takes
the initiative to perform a task based on its own goals and what it perceives
from the environment.

Figure 3: Primary cognitive abilities of an intelligent system.

###  2.2 Property 2: Having primary cognitive abilities

In this paper we distinguish four primary cognitive abilities that are usually
present in intelligent systems:

  * •

Perception. Perception may be understood as the interpretation of sensory
information to form a mental representation of the world [41]. For example, a
self-driving car senses the world with the help of multiple sensors (lidar,
cameras, accelerometers, acoustic sensors, etc.). Perception is distributed in
multiple perceptual components in such a way that each component recognizes a
kind of object (e.g., pedestrians, traffic signals, other vehicles, etc.) and
updates a dynamic representation of the environment. In general, the perceived
world may include parts of the own body that can also be sensed by the
system333This type of perception is called proprioception (perception of self-
movement and body position)..

  * •

Action control. Action control refers to the ability of an agent to control
the execution of its own actions. Action control sends orders to actuators and
uses data from the environment measured with specific sensors operating in a
continuous closed loop. For example, the speed control of an autonomous
vehicle acts on the accelerator by continuously observing data from a sensor
that measures speed. The global control may be divided into a set of behavior
controllers and each one is in charge of a specific control aspect (e.g.,
braking, steering, accelerating, etc.)444The behavior of certain kinds of
intelligent systems may be understood as the combination of multiple behavior
controllers as it has been described by the behavior-based paradigm in
robotics [1] [4]..

  * •

Interaction. The ability to interact with other agents is based on using a
common language. Artificial systems may communicate with users using
simplified mechanisms (e.g., graphical user interfaces, prefixed commands,
etc.). For example, in a self-driving car, the passenger may indicate the
destination to go and the vehicle presents the path to follow together with
information about the environment that helps the passenger monitor the correct
execution of the trip. Besides these mechanisms, interaction with users can
also be based on human language using algorithms on natural language
processing.

  * •

Deliberative reasoning. An intelligent agent may be able to reason in order
to, for example, plan actions (e.g., find a sequence of steps to travel from
one place to another) or diagnose problems (e.g., determine the cause of a
failure). In this paper, we use the term deliberation to refer to this type of
ability, as it is used by different authors [43] [17] [34].

As Figure 3 shows, perception and action control may be divided in multiple
separate components that usually operate in parallel at a high frequency.
However, deliberation and communication usually operate sequentially which is
necessary for coherent reasoning [22] and consistent communication.

####  2.2.1 Reactive behavior versus deliberative reasoning

The global behavior that emerges from individual behavior controllers is
reactive in the sense that it is able to react quickly to events of the
environment. The immediate feedback of the state of the world is done through
sensors555It said said that the agent is situated in an environment when the
agent operates in a close-coupled continuous interaction with environment in a
continuous sequence of sense-act. A system conceived in this way may use a
limited or nonexistent memory about the world.. This reactive behavior is
observed, for example, in reflexes of animals with primitive forms of brains.
It can also be observed in human intuitive thinking, when decisions are based
on intuitions, i.e. based on feelings rather than on facts or evidence.

As an alternative of such behavior, deliberative reasoning uses imaginary
information about the world in order to assess alternative hypotheses. For
example, chess players imagine the consequences of the moves before deciding
on the most appropriate one. Deliberative reasoning uses beliefs, i.e.,
propositions that the the agent considers true (permanently or provisionally).
New beliefs may be inferred by logical deduction or by other forms of
reasoning (induction, analogical reasoning, etc.).

The conclusions achieved by deliberative reasoning may be used to activate and
inhibit certain behavior controllers. Deliberation can manage conflicting
goals and can inhibit reactive behaviors when it is necessary to postpone
immediate answers in order to reach long-term goals with more priority. For
instance, one person may accept deliberately suffering certain pain during a
medical treatment with the expectancy of a future health improvement.

The distinction between deliberative reasoning and reactive behavior has been
identified in human thinking by Daniel Kahneman [19] with the names System 1
and System 2. System 1 (or intuitive thinking) is fast, intuitive, automatic,
unconscious and non-linguistic (difficult to verbalize). On the contrary,
System 2 (or deliberative reasoning) is slow, logical, effortful, conscious,
linguistic (can be described verbally) and makes complex decisions. In the
development of computer-based intelligent systems, the combination of
deliberative reasoning and reactive behavior is a non trivial problem that
requires specific design decisions (e.g., when to favor deliberation over
reaction).

####  2.2.2 Use of symbols

Using symbols has been been pointed out as a relevant feature of intelligence.
For example, the philosopher Ernst Cassirer stated that humans should be
defined as symbol-making animals. Allen Newell and Herbert Simon proposed the
physical symbol hypothesis, i.e., a physical symbol system has the necessary
and sufficient means of general intelligent action [36], which was a dominant
paradigm in artificial intelligence until the 1990s. Gary Marcus argues that
symbolic manipulation seems to be essential for human cognition [25].

Symbols are present in the language used for agent interaction. For example,
human language is characterized by the use of symbols in the form of words or
other representations (e.g., mathematical notations). Symbols represent
classes of physical objects or abstract concepts. Agents agree in advance a
shared meaning of the symbols in order to understand the language used in the
communication process.

An agent may establish the meaning of symbols through perception (to associate
sensory information to symbols) and control action (to translate symbolic
commands into actions). This is related to the symbol grounding problem [15],
i.e., how to ground the meaning of a symbol in anything different than other
symbols. This problem has been addressed in robotics, for example, with the
idea of perceptual anchoring [7] under which a symbol (e.g., the concept tree)
and sensor data (e.g., the image of a particular tree observed through a
camera) are related using data structures (called anchors) together with
specialized handling procedures.

An agent can represent the beliefs used during reasoning with the help of
symbols understandable to other agents. This is useful to explain to others
one’s own reasoning and the knowledge used to reach the conclusions, showing
awareness of own mental states666In psychology, self-awareness is understood
as the ability to become the object of one’s attention [8]. Self-awareness can
also understood following a view of processing information about the self [32]
which may be appropriate to simulate this ability with computer-based systems.
(as humans perform via introspection). This property (sometimes called
explainability) is a desirable characteristic for an intelligent system to be
trusted.

###  2.3 Property 3: Following principles about rationality and social norms

As presented above, an intelligent system is expected to follow two basic
principles of behavior. The first principle is acting rationally and affects
the individual behavior of the system with respect to its goals and the
environment. The second principle is about following social norms and affects
the behavior of the system with respect to other agents. The following
sections describe these principles in more detail.

####  2.3.1 Acting rationally

An agent acts rationally if the decisions it makes about its actions seek to
maximize a measure of performance. This measure quantifies the degree of
achievement of the goals pursued by the agent. For example, in the game of
chess, players try to win their opponents. A financial analyst makes decisions
about investments to maximize the economic profit obtained. The decisions made
by these agents are oriented to maximize a performance measure (number of
opponents beaten or economic benefit obtained). This type of behavior has been
described in artificial intelligence as the principle of rationality [35] and
it has also been analyzed in other disciplines such as philosophy or economy
[9].

Acting rational is closely related to the notion of intelligence. Comparing
performance measures in achieving goals is one way to compare degrees of
intelligence. We say that a chess player A (or investment advisor A) is more
intelligent than another player B (or another investment advisor B) if A wins
more games (or makes more profit). This goal-based view is one of the
approaches followed by different authors to define intelligence [21] [14]
[23]. For example Kurzweil defines intelligence as "the ability to use
optimally limited resources to achieve goals".

Note that this type of definition provides a method for testing intelligence
based on the evaluation of a system in maximizing an objective function. This
approach is flexible because it does not create an artificial rigid separation
between intelligent and non-intelligent systems. In addition, it takes a
practical approach for building intelligent machines, avoiding open scientific
questions related to intelligence (e.g., phenomenal consciousness).

####  2.3.2 Social norms

The behavior of an artificial intelligent system with respect other agents is
usually constrained by social norms, especially when it cooperates with human
users. This is important to ensure that the system operates in accordance with
ethical values such as ensuring fairness (avoiding group biases) or limiting
harmful use.

For instance, an intelligent system trained using biased data may be not able
to ensure a fair operation. This situation happened with Tay, an experimental
conversational chatbot developed by Microsoft in 2016. This chatbot was
trained with uncontrolled public data based on the interaction with people
which generated a racist bias in the system. The ethical behavior of
autonomous vehicles has been analyzed by Bonnefon et al. [3] considering
extreme situations in which the vehicles have to choose between running over
pedestrians or sacrificing their passenger to save the pedestrians.

In this context, Russell argues that artificial intelligence systems should
operate as beneficial machines that maximize the realization of human
preferences in order to cope with the AI control problem777The AI control
problem is the problem of how to build AI systems that aid rather than harm
their creators. [39]. According to Russell, the actions of intelligent
machines are expected to achieve human objectives and, since these objectives
are uncertain, machines will defer to humans asking permission, accepting
correction and allowing to be switched off. In addition, the behavior of these
machines should be constrained by rules and prohibitions just as the actions
of humans are constrained by laws and social norms.

This type of behavior has also been analyzed, for example, using the concept
of benevolent agents in social organizations like multi-agent systems [6].
Benevolence may be understood as a kind of goal adoption. An agent is
benevolent if the agent adopts other’s goals and the adoption of such goals
does not help to achieve its own goals.

###  2.4 Property 4: Capacity to adapt

The capacity for adaptation allows an intelligent system to operate
effectively in complex dynamic environments. In general, a system is said to
be self-adaptive if it is able to modify the way it performs a certain task in
response to changes in the environment so that the system can perform such
task more efficiently.

A first approach to achieve self-adaptation is by a dynamic re-combination of
behaviors [16] [37] [30]. In this case, the agent uses an adaptation logic
with information about the effectiveness of its own behavior methods. In a
given situation, the agent uses the adaptation logic to decide which
combination of methods is the most appropriate to perform a given task,
according to the current state of the environment.

A second approach for adaptation is through learning. The following sections
describe in more detail two forms of adaptation (individual and social) that
are based respectively on learning from experience and learning from others.

####  2.4.1 Learning from experience

A system with the capacity of learning is able to improve its performance in
the course of multiple interactions with the environment. For example, a chess
player can learn more effective strategies after playing multiple games. This
approach to learning has been used in the context of machine learning by Tom
Mitchell [27], who formulated the following definition: "a computer program is
said to learn from experience E with respect to some class of tasks T and
performance measure P, if its performance at tasks in T, as measured by P,
improves with experience E".

An intelligent system that is able to learn needs a value judgement method to
assess its behavior. The result of such assessment is used by the intelligent
system to change its behavior in order to improve the assessment result in the
future. The value judgement method can be an external agent who works as a
instructor (or trainer or supervisor) observing and assessing the behavior of
the intelligent system to provide feedback (e.g., with positive or negative
rewards). The intelligent system can also use an internal method that judges
its own actions to generate positive or negative rewards using, for example,
simulated feelings (like pain or pleasure) or other more complex mechanisms
based on self-observation,

As it is argued by Silver et al. [42], systems that learn through trial and
error experience to maximize reward could help to acquire multiple abilities
related to intelligence. Thus, learning should be understood as a
distinguished capacity of intelligent systems that affect primary cognitive
abilities (perception, action control, reasoning or interaction). For example,
an aerial robot can improve action control by learning to land more precisely
on a marked surface after multiple trials.

####  2.4.2 Learning from other agents

In addition to the approach described above, we can identify another form
learning based on knowledge transfer between individuals. With this scheme, an
agent A learns new beliefs from another agent B on the basis that agent B is
able to describe its own knowledge with a symbolic language whose meaning
agent A is able to understand. This form of learning, which allows groups of
agents to acquire knowledge directly from other agents, accelerates social
adaptation to the environment because some agents can save the effort of
having to learn individually from their own experience.

For example, consider the belief expressed as "zebras are horse-shaped and
have striped skin". An agent who has never seen a zebra might be able to
recognize this animal using the symbolic description. This form of learning
has been called zero shot learning [45], which consists of learning to
recognize new objects without any examples, only having their symbolic
description.

##  3 Building intelligent systems

This section summarizes two important aspects in the construction of an
intelligent system: (1) the selection of artificial intelligence methods and
(2) the acquisition of the knowledge used by the system to perform the desired
tasks (which can be done manually or with the help of automatic methods).

###  3.1 Artificial intelligence methods

The development of an intelligent system can require combining several
artificial intelligence methods. Table 2 shows functionalities related to
cognitive abilities together with a sample of common methods used for building
intelligent systems.

These methods can be divided according to two distinguished approaches. One
approach corresponds to methods using connectionist representations (e.g.,
convolutional neural networks, recurrent neural networks or deep reinforcement
learning). The other approach uses symbolic methods following formal semantics
based on logic (e.g., automated logic deduction, rule-based systems and
constraint satisfactions algorithms), based on probability (e.g., bayesian
networks) or hybrid approaches (e.g., fuzzy logic).

Symbolic and connectionist methods correspond to alternative approaches that
have complementary strengths and weaknesses [12]. In fact, there is a lack of
understanding of how information processing performed by symbolic approaches
can be mapped onto computations performed by connectionist methods. The term
computational explanatory gap [38] has been used to express that it is unclear
how the two approaches are related.

Cognitive abilities that are consciously accessible (such as processes related
to deliberation or interaction) may be implemented using symbolic methods.
These methods can describe the symbols that represent knowledge to justify how
to reach a certain conclusion.

On the other hand, perception and action control are usually carried out in an
unconscious way and they are better implemented with non-symbolic approaches
such as connectionist methods. In practice, it is interesting to note that the
implementation of unconscious sensorimotor skills require much more
computation compared to deliberative reasoning. This has been stated as the
Moravec’s paradox [31].

###  3.2 Knowledge acquisition

Intelligent systems may use knowledge that is difficult to be formalized using
procedural representations of computer languages such as Java, Python or C++.
For example, consider a medical expert system that uses knowledge that relates
symptoms and diseases. This knowledge may be modeled using symbolic
declarative representations (e.g., rules) that are stored in a knowledge base.
An inference algorithm processes such rules to diagnose the disease of a given
patient. The inference algorithm is general and, therefore, it may be
implemented using generic software tools. However, the rules of the knowledge
model are specific and must be written for the particular expert system.

Cognitive ability |  Example function |  Examples of detailed
functions |  Examples of computational
methods
---|---|---|---
Perception |  Feature
extraction |  Image recognition, information extraction, signal processing, attention mechanisms |  Neural networks (e.g., convolutional neural networks), pattern recognition methods
|  Data
interpretation |  Data abstraction, data fusion, symbol grounding |  Statistical data analysis, extended Kalman filters, perceptual anchoring
Deliberation |  Descriptive
reasoning |  Classification, diagnosis, temporal projection (prediction), belief revision |  Automated logic deduction, bayesian networks, rule-based systems, semantic networks, truth maintenance systems
|  Prescriptive reasoning |  Planning, scheduling, configuration, assignment, goal reasoning |  Automated planning, constraint satisfaction algorithms, BDI models
Interaction |  Language recognition and generation |  Natural language understanding, speech recognition, natural language generation |  Natural language processing methods (e.g., recurrent neural networks, transformers)
|  Dialog
management |  Dialog coordination, discourse planning |  Communicative act theory, interaction protocols, automated planning
Action control |  Action
selection |  Goal-driven or event-driven action selection, action conflict management |  Finite-state machines, behavior trees, Petri nets
|  Action
execution
control |  Motion control, manipulation control |  Control theory, fuzzy control, deep reinforcement learning
Table 2: Functionalities related to cognitive abilities together with examples
of computational methods used for building intelligent systems.

The construction of a knowledge base by developers is called knowledge
acquisition. Many of the knowledge bases of classical expert systems
represented heuristic knowledge that was acquired manually from domain experts
and formalized using declarative representations (e.g., rules or first order
logic). The creation and maintenance of such knowledge bases usually requires
significant effort. Therefore, manual acquisition is limited for the
development of knowledge bases that are not too large. This problem has been
called knowledge acquisition bottleneck.

To facilitate this task, the field of ontology engineering in artificial
intelligence has proposed technical solutions to formulate common
representations, understood as ontologies, that allow sharing and reusing
contents of knowledge bases across different intelligent systems.

###  3.3 Automatic methods for building intelligent systems

In contrast to manual knowledge acquisition, it is possible to apply automatic
methods for building intelligent systems, which has gained popularity in the
last decades due to the increasing availability of large amounts of data and
higher computational power. These methods can use information sources with
different formats such as images (photographs or videos), structured data
(e.g., data bases with alphanumeric data or temporal series) or non-structured
text written in natural language.

Machine learning methods can be used to develop components that implement
specific cognitive abilities that will be part of an intelligent system. For
instance, a neural network can be trained using thousands of images of tumors
labelled as positive and negative examples. The trained neural network can be
used as part of an intelligent system that helps physicians recognize the
presence of tumors. To build an aerial robot able to land on a moving surface,
it would also be possible to apply a method based on deep reinforcement
learning and a simulator of the robot operating in a virtual environment to
pre-train a neural network able control the robot motion during landing on the
surface. In order to build a rule-based expert system for medical diagnosis, a
rule induction method could be applied to a data base with examples of
diagnoses relating diseases and symptoms to generate candidate rules for the
knowledge base.

Natural language processing methods can also be used as an automatic method to
assist in the construction of intelligent systems. These methods can be used
to extract knowledge from text documents written in natural language. For
example, a large corpus of text documents corresponding to a professional
domain can be analyzed using natural language processing techniques to
generate instances of classes and and relations to be used by a
question/answer system.

Note that these two methods, machine learning and natural language processing,
can play different roles in the development of intelligent systems. On the one
hand, they can be used offline as it is described in this section, playing a
role of tools used by a developer to create automatically parts of the system.
On the other hand, they can be used online, i.e., the final intelligent system
includes components using such methods to implement the capacity to adapt
(using machine learning methods) or the ability to interact in natural
language (using natural language processing methods).

##  4 Examples of intelligent systems

This section presents illustrative examples of real-world intelligent systems
using the characterization presented in this paper. In order to provide a
global view, Figure 4 shows diverse intelligent systems organized in a
hierarchy of categories. This hierarchy is not intended to be accurate and
exhaustive but illustrative of the kind of existing systems. The following
sections describe in more detail some of these categories.

Figure 4: Examples of intelligent systems.

###  4.1 Autonomous robots

An autonomous robot may be considered as a representative case of intelligent
system. An example of this system is an aerial robot for airplane inspection
developed as a demonstrative prototype in 2019 for the company Airbus [2].
This system performs the following functions:

  * •

Perception. The aerial robot is equipped with multiple sensors such as (1)
inertial measurement unit (IMU) to provide information about orientation,
angular velocities, and linear accelerations, (2) cameras to calculate
velocity using visual odometry, (3) a lidar sensor that generates a 3D point
cloud, and (4) a high resolution frontal camera. The perception system
performs data fusion from multiple sensors for a precise localization. The
high resolution camera is used to detect anomalies on the surface of the
airplane using computer vision.

  * •

Deliberation. In this system, the main deliberative function is path planning.
The system may generate an inspection plan to cover the surface of the
airplane. In addition, the system stores images to be analyzed later.

  * •

Action control. Action control corresponds to plan execution and motion
control to complete the navigation plan. Action control also includes a
reactive mechanism to avoid unexpected obstacles detected with the help of the
lidar sensor.

  * •

Interaction. The operator provides the geometry of the airplane and initiates
the mission. The operator can stop the mission execution if an emergency
happens.

In general, an intelligent robotic system is one of the most complex category
of intelligent system because it usually includes the majority of the
characteristics considered in this paper and, in addition, it operates in
real-world environments with demanding physical constraints. In this category,
there are multiple kinds of systems with different kinds of complexity such as
industrial collaborative robots (e.g., Baxter), autonomous vehicles (e.g.,
self driving cars, planet explorer Curiosity), assistive robots, domestic
robots (e.g., house cleaning robot Roomba), entertaining robots (e.g., Sony
Aibo, Furby), etc.

###  4.2 Expert systems

The development of expert systems contributed to the practical success of AI
in the 1980s with many commercial systems in multiple domains. They were
applied to perform automatically tasks such as the following:

  * •

Classification: Find the category of an object using observations (e.g.,
determine the presence of a mineral, recommend a type of investment, detect
abnormal behaviors).

  * •

Diagnosis: Find the causes of symptoms (e.g., medical diagnosis, mechanical
diagnosis).

  * •

Temporal projection: Estimate the future behavior of a dynamic system (e.g.,
economic market prediction).

  * •

Assignment/Scheduling: Assign a set of resources to a set of needs (e.g.,
assign airplanes to gates in a airport).

  * •

Configuration: Create a design that satisfy some requirements (e.g., design
the machinery of an elevator).

  * •

Planning: Find a set of actions to reach a desired state (e.g., emergency
evacuation planner)

####  4.2.1 First generation expert systems

The first generation of expert systems used mainly heuristic reasoning with
symbolic knowledge representations such as rules. For example, Mycin was one
of the first expert systems that was used sucessfully as a model in other
types of problems. Mycin was developed at the Stanford university in the 1970s
[5] and it was able to diagnose infectious diseases and propose therapies.
This system has the following two main functional components:

  * •

Interaction. The information about patients is obtained through the
communication with the user. Dialogue with the user is done in a question-
answering process with prefixed questions and answers. Mycin asks questions
about the patient and cultures. The questions are adapted to the previous
answers (questions have context). The user may answer with uncertainty and may
ask for explanations. Mycin presents diagnosis and therapy recommendation

  * •

Deliberation. Mycin diagnoses the causes of the symptoms finding the possible
infectious organisms. Mycin uses a heuristic classification method with a
knowledge base with 450 rules and backward chaining. Mycin includes an
original method for approximate reasoning (certainty factors as values in [-1,
+1]). In addition, Mycin recommends therapies using a method based on a
generate and test procedure.

The methods used for building expert systems demonstrated that they are valid
in many problems, but they present limitations to be used in certain cases.
One of the most important limitation is the problem mentioned above called
knowledge acquisition bottleneck that restricts this approach for the
development of knowledge bases that are not too large.

Expert systems may be able to solve difficult problems, but they operate in
narrow specialized domains, without general knowledge about the rest of the
world . Therefore, compared to human experts, who have also common sense
knowledge, expert systems are brittle and unable to react correctly in
unexpected situations.

Another problem is related to the way these systems obtain information about
the environment. For example, Mycin gets information about the patient using a
prefixed set of questions and answers. This communication mechanism may be too
narrow and rigid to be used in situations when the format of available data is
more diverse (e.g., unstructured texts). This limitation makes difficult to
integrate the expert system into day-to-day operations.

####  4.2.2 Expert systems for the management of sensorized environments

The behavior of dynamic environments can be monitored with the help of sensor
networks and information systems. This is the case, for example, of modern
information infrastructures that have been developed in the last decades
applied for example to strategic areas such a smart cities (transportation,
climate, pollution surveillance) or industry 4.0 (with sensorized industrial
installations).

In this context, it emerged the need of using intelligent systems that advise
operators to make decisions for the management of such environments. There are
multiple examples of expert systems that belong to this category. For
instance, there are systems for public transportation management [28],
scheduling and coordination at an airport [18], emergency decision support in
floods [29] or electric power operation [11].

The first example corresponds to an expert system that detects incidents and
recommends actuation in a urban bus transportation network. This system was
included in 2002 as part of the fleet management system of the bus control
center of the city of Vitoria in Spain. This system has the following main
abilities:

  * •

Perception. The system collects data from buses using GPS information and
localizes the position of each bus in a map of transportation lines.

  * •

Deliberation. The system detects the presence of incidents (e.g., detection of
delays based on current position and planned position) and predicts future
behavior (e.g., a model is used about historic demand for transportation). The
system determines actions to be done to manage incidents (an automated planner
based on hierarchical task network (HTN) planning is used to generate action
plans).

  * •

Interaction. The system presents to the operator detected incidents (e.g., bus
delays). The operator can notify other events to the system (e. g., a broken
bus, a blocked street, etc.). The system recommends actions (e.g., using
additional buses, sending repairing truck, etc.). The system justifies actions
(e.g., why some lines are prioritized based on expected demand).

In general, this type of systems include a perception component to observe and
interpret characteristics of the environment behavior using a sensor network
or an information system. In addition, the system includes a deliberation
component to derive conclusions from data, such as:

  * •

what happens in the environment (detection),

  * •

why it happens (diagnosis),

  * •

what may happen in the future (temporal projection, what-if analysis), and

  * •

what should be done (planning).

The system also includes a interaction component to present this information
to the human operator. This type of intelligent system does not have an
actuation component to modify the environment. The operator is responsible of
making management decisions and implementing the actions in the environment.
Therefore, it is important that the interaction component presents the system
suggestions with sufficient justification in such a way that the operator can
take responsibility for the decisions.

###  4.3 Question-answering systems based on cognitive computing

The company IBM developed a system called Watson [10] that marked a
significant milestone in the history of artificial intelligence when it won
the first prize of the quiz TV show "Jeopardy” in 2011 competing against human
opponents. The system was able to respond better than humans to open domain
questions written in natural language about general culture.

In the quiz game, Watson had to guess an answer based on a given clue provided
by the user. For example, Watson receives a clue such as “this drug has been
shown to relieve the symptoms of ADD with relatively few side effects”. In
less than 5 seconds, Watson had to generate the answer expressed as: "what is
Ritalin?" (according to the rules of the game, the answer “Ritalin” is
presented as a question).

Watson used a selected large corpus of text documents of about 2 million pages
as a source of information (e.g., news, encyclopedias and literary works).
Watson searches candidate answers in such a corpus that can be possible
responses to questions formulated by the end user in natural language. Watson
uses a sophisticated structure of algorithms to (1) select relevant search
keywords that may be used to find out (partial) candidate partial answers and
(2) rank and integrate partial answers to generate the final answer in natural
language.

The general characteristics of Watson can be described using to the components
presented in this paper. In our characterization, we may consider the corpus
of documents as the environment in which the system operates to perform its
searching task. According to this, the components of Watson are the following:

  * •

Interaction. The interaction component of Watson uses natural language
processing to extract the linguistic structure of the given clue. The
interaction component also generates the final answer in natural language.

  * •

Deliberation. Deliberation in Watson included two parts. A first part
generates a number of search keys from the linguistic structure of the given
clue. Each search key is generated to be used later (by the perception
component) to retrieve information from text documents. The second part of
deliberation ranks and combines retrieved candidate answers. For this purpose,
Watson was is able to merge answers [20] doing temporal reasoning, geospatial
reasoning and taxonomic reasoning (subsumption, disjointness, etc.) using
ontologies and databases (e.g., Yago, DBPedia, Freebase). In order to score
answers, Watson uses models generated by machine learning.

  * •

Perception. To obtain candidate answers from the corpus of documents, Watson
used dozens of specialized algorithms for information extraction using natural
language processing.

Watson is a successful representative case of a question-answering system that
uses a computing approach called cognitive computing. The idea of this
approach is that the system uses as a source of information large amounts of
structured and non-structured data (text, images, etc.). The system performs
automatically cognitive tasks that include feature extraction (e.g., using
natural language processing or computer vision), answer aggregation (reasoning
with ontologies or knowledge bases), natural language communication (using
natural language understanding and generation) and evidence assignment of
hypotheses using models learned by machine learning.

##  5 Conclusions

This paper has presented a characterization of an intelligent system that has
been formulated with the purpose of being useful for systems engineers to
analyze and design this type of systems. The paper defines an intelligent
system using certain terminology with commonly accepted meanings in artificial
intelligence. The description focuses on functional characteristics related to
intelligence that machines are now capable to exhibit with the current state
of technology.

In contrast to other work, the description presented in this paper is oriented
to provide a global view as a whole, with usual features that may or may not
be present in a particular system. This whole view creates a convenient
context to describe more precisely concepts and identify their relations. For
example, our characterization extends other definitions in order to consider
explicitly the social factor of an intelligent system. This includes the
interaction with other agents using language and following social norms.
Language is related to the use of symbols, which is a significant feature of
human intelligence, and social norms are important, especially when the
intelligent system should cooperate with human users in accordance to ethical
values. In addition, the capacity to adapt is presented in a broad sense, in
relation to different forms of learning (from experience and from others).

The characterization provides a unified view of what is meant by an
intelligent system that integrates different conceptions of such systems to
cover classical systems (e.g., rule-based systems), behavior-based systems, or
more recent conceptions for autonomous AI-based systems. The functional
components used in the definition of an intelligent system are related to
common AI methods (based on both connectionist and symbolic approaches). The
paper also demonstrates how the presented characterization may be used to
describe the functions of various real systems (e.g., expert systems and
autonomous robots).

As future work, we hope to adapt and extend this general characterization to
take into account results derived from new research on new computational
models of intelligence.

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