Towards a Theory of Awareness

Ricardo Sanz

1 a

, Manuel Rodr

ıguez

1 b

, Mart

ın Molina

2 c

, Esther Aguado

1 d

and Virgilio G

omez

1 e

Autonomous Systems Laboratory, Universidad Polit

ecnica de Madrid,

c/ Jos

e Gutierrez. Abascal 2, 28006 Madrid, Spain

Artﬁcial Intelligence Department, Universidad Polit

ecnica de Madrid,

Campus de Montegancedo, 28660 Boadilla del Monte, Madrid, Spain

Keywords:

Awareness, Theory, Autonomy, Cognitive Science, System Architecture, Engineering Methods, Science,

Consciousness, Perception, Attention, Understanding.

Abstract:

Observation of humans and animals shows that awareness is a critical aspect of mental processes for those

agents that operate in changing environments. Responding to potentially dangerous situations and leverag-

ing environmental affordances are essential capabilities for autonomous agents’ ecological viability. Agents

need to be aware of their situations. Artiﬁcial autonomous systems construction depend on using suitable

system architectures and applying proven engineering methods. While current systems display a certain de-

gree of awareness, it is unclear what principles shall be used in their design. We are in a pre-scientiﬁc,

pre-technological situation concering awareness. Unfortunately, the scientiﬁc analysis of the awareness phe-

nomena is quite difﬁcult because its principles cannot be easily isolated in fully functioning human minds. We

need a clean, formal theory of general awareness of universal nature. This theory should be applicable both

to humans and machines, and not exclusively bound to the psychology and neurobiology of living animals. In

this position paper, the authors argue for developing such a theory, state some requirements for it and propose

an initial conceptual seed for a future theory of awareness that orbit around the idea that awareness is the

real-time understanding of sensory ﬂows.

1 INTRODUCTION

The mere observation of humans’ and animals’ be-

haviours shows that awareness is a central aspect of

mentality for those agents that successfully operate

in changing, challenging environments. Perceiving

change, responding to potentially dangerous situa-

tions and leveraging environmental affordances are

essential capabilities for autonomous agents ecolog-

ical viability, i.e. agents that are capable of surviv-

ing the disturbances that a non-controlled environ-

ment throw on them. So should be for robots and

other classes of situated machines. Endowing ma-

chines with “awareness” should improve their auton-

omy proﬁle, making them more reactive to world dy-

namics (Sanz et al., 2007a). However, this is not an

https://orcid.org/0000-0002-2381-933X

https://orcid.org/0000-0003-0929-5477

https://orcid.org/0000-0001-7145-1974

https://orcid.org/0000-0002-7860-9030

https://orcid.org/0000-0001-8538-5111

easy task; especially because we do not have a good,

translatable Theory of Awareness (ToA).

In this domain, authors sometimes use the term

“awareness” and sometimes use the term “conscious-

ness”. The use of the two terms has similarities and

differences that may vary across disciplines, domains

and languages. It is not easy to pinpoint the differ-

ence between “consciousness” and “awareness”, as

Francis Crick acknowledged (Crick and Koch, 1992).

From some perspectives, we can consider both the

same thing, e.g. in the perceptual domain; from oth-

ers, we cannot, e.g. in the ethical domain. We will

follow here Crick’s policy of considering them syn-

onyms; giving preference to “awareness” and using

“consciousness” only when trying to make a speciﬁc

point concerning the term. However, other authors do

follow other policies and use both terms (as the refer-

ences and theories will show).

In principle, it should be feasible to translate most

human mental traits into machine mental traits thanks

to the general multiple realizability of physical sys-

Sanz, R., Rodríguez, M., Molina, M., Aguado, E. and Gómez, V.

Towards a Theory of Awareness.

DOI: 10.5220/0012595100003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 1413-1420

ISBN: 978-989-758-680-4; ISSN: 2184-433X

1413

tems

. To translate awareness from humans to ma-

chines we only need a physical theory of awareness.

However, in the case of awareness-related aspects,

there is a strong risk of falling into a very deep rabbit

hole of biologism if we are not careful enough as to

be perfectly clear on what we are talking about and

what class of theory do we need. In this paper we

argue for the development of such a general theory

of awareness, trying to be careful enough as to avoid

the rabbit hole, and delivering a theory applicable to

animals and translatable to machines.

2 AWARENESS IN ROBOTS

The topics of consciousness and awareness have been

quite marginal in the domains of AI and robotics

(Chella and Manzotti, 2007; Chella, 2023).

The efforts to build conscious AIs or the discus-

sion about its very possibility was a matter of a small

group of researchers with some philosophical voca-

tion. Only recently this community has gained some

extra human mass, esp. when some comments by

well-known actors in the current AI landscape hit the

media. These comments talked about the possibility

of current mainstream AI implementations could be

reaching a state of consciousness. These reminded

people about Skynet becoming aware and trying to

kill humanity in Cameron’s classic Terminator. How-

ever, even when this is becoming a wider discussion,

the research program on aware AI is still very ﬂaky;

but it should be not if we are right concerning its im-

portance for autonomous machines.

In the last decades, some researchers have at-

tempted the creation of real implementations of AIs

and robots with consciousness. In most cases, the

approach take was 1) select one of more char-

acteristics associated to human consciousness, and

2) develop a machine that demonstrated this char-

acteristic. For example, Aleksander addressed

anticipation and imagination (Aleksander, 2009),

Tani addressed mirror self recognition (Tani, 2017),

Chella addressed inner speech (Chella et al., 2020),

Hoffmann addressed proprioceptive self-awareness

(Hoffmann, 2021), Hern

andez addressed metacog-

nitive self-awareness (Hern

andez et al., 2009), and,

brave enough, Haikonen addressed qualia (Haikonen,

2013).

The attempts will continue, for example extending

into the very active domains of large, language-based

AIs. However, the current statistical approaches of

machine learning from human-bound data will not

We will here take a strict, non-dualist, physicalist

stance concerning mental aspects like awareness.

achieve the desired end of powering the engineering

capability of creating custom awareness

. The en-

gineering of these systems will only be effective if

grounded in more profound, structural theories that

seem far from what current machine learning capa-

bility can provide. As Dacey says, (Dacey, 2022)

“Statistical inference cannot do all of the work of the-

ory choice.” We have the need of producing a proper

structural deep theory of awareness to do both science

and technology.

3 RATIONALE FOR A THEORY

OF AWARENESS

The rationale for seeking a theory of awareness has

deep grounds in science, philosophy and engineering

(Sanz et al., 2007a).

Understanding awareness needs an approach from

a uniﬁed perspective. For example, in the realm

of philosophy, awareness is related to four essential

branches: epistemology -what the agent gets to know

through the senses, ontology -what the agent is aware

of-, phenomenology -the sensations that the agent

gets-, axiology -the value that such perceptions have

for the agent dwellings. Awareness seems essential

for dwelling in open dynamic worlds.

This need is also very relevant in the world of arti-

ﬁcial systems. Engineers are seeking system design

solutions to deal with the uncertainty of the world

and the uncertainty of the systems themselves. In

many cases, autonomous robots failures are not due

to changes in the world but changes in the robot it-

self (e.g. failures or emergent phenomena in the

robot software subsystems). The streamlined con-

struction and dependable runtime operation of arti-

ﬁcial autonomous systems depend on using suitable

system architectures and applying proven engineering

methods (Aguado et al., 2021). Having solid theo-

ries of world-awareness and self-awareness may help

achieve the desired results.

In this sense, a general theory of awareness is a de-

sirable asset for both scientists and engineers. Unfor-

tunately, the scientiﬁc analysis of the awareness phe-

nomena is quite difﬁcult because it cannot be easily

identiﬁed and isolated in fully functioning biological

minds. We need a single theory of general aware-

ness of universal nature and this universality seems

difﬁcult, especially when we seek a theory applicable

both to humans and robots, and not exclusively bound

Awareness that is designed and scaled to the needs of

the target technical system that need not be similar to a hu-

man.

AWAI 2024 - Special Session on AI with Awareness Inside

1414

to the psychology and neurobiology of living animals

(Wilson, 1998). Achieving an universal –applicable

to all classes of systems– and uniﬁed –explaining all

related phenomena— theory is a complex challenge.

Besides the different classes of subjects, there are too

many aspects of consciousness to deal with. Alek-

sander identiﬁed ﬁve aspects of consciousness to be

mapped into machines —perception, imagination, at-

tention, planning, emotion— but, for example, Tani

identiﬁed ten different aspects —based on the phe-

nomenological analysis of Husserl.

Long ago, Aaron Sloman said that “It is not worth

asking how to deﬁne consciousness, how to explain

it, how it evolved, what its function is, etc., because

there’s no one thing for which all the answers would

be the same. Instead, we have many sub-capabilities,

for which the answers are different: e.g. different

kinds of perception, learning, knowledge, attention

control, self-monitoring, self-control, etc.”

Many authors, especially in the biological and hu-

manities domains, argue that machine consciousness

is impossible. However, the possibility of devising a

single mechanism explaining all the aspects of aware-

ness in a system-neutral sense, or at least separat-

ing them into related and unrelated aspect as Sloman

proposed, need not be an impossible dream (Hadley,

2023). As Francis Crick said:

The second assumption is tentative: that

all the different aspects of consciousness, for

example pain and visual awareness, employ a

basic common mechanism or perhaps a few

such mechanisms. If we understand the mech-

anisms for one aspect, we will have gone most

of the way to understanding them all. (Crick

and Koch, 1990)

In this position paper, the authors argue for de-

ploying effort towards developing such a theory, stat-

ing some requirements for it and proposing an initial

seed for a potential future Theory of Awareness.

4 THEORIES OF AWARENESS

There are many theories that offer different perspec-

tives on the nature of awareness, reﬂecting the inter-

disciplinary nature of the ﬁeld. See for example the

article by Seth and Bayne (Seth and Bayne, 2022) for

a more exhaustive review in the domain of biological

consciousness.

In comp.ai.philosophy, 14 Dec. 1994.

4.1 Perspectives on Awareness

Here are some important theories and perspectives re-

lated to awareness:

• In the domain of cognitive psychology:

– Selective Attention: Awareness seems closely

related to selective attention. We are con-

sciously aware of the information we selec-

tively attend to, and other stimuli may not enter

our conscious awareness (Taylor, 2002).

– Levels of Processing: Depth of processing in-

formation affects awareness (Craik and Lock-

hart, 1972). Deeper elaboration leads to better

retention and awareness of information.

• In the domain of neuroscience:

– Global Workspace Theory (GWT): GWT

(Baars, 1997) says that conscious awareness

arises from the global broadcast of information

throughout the brain. Certain neural processes

involve a global workspace that integrates in-

formation and brings it to conscious awareness.

– Neural Correlates of Consciousness (NCC):

Researchers seek to identify speciﬁc neural ac-

tivity patterns associated with conscious aware-

ness (Koch et al., 2016). Understanding these

neural correlates can shed light on how the

brain generates awareness.

• In the domain of Philosophy:

– Phenomenal Consciousness: This perspective

explores the nature of subjective experience —

having “qualia.” It analyses what it is like to

have a particular experience and how subjective

awareness happens (Nagel, 1974).

– Higher-Order Thought (HOT) Theory: This

theory proposes that awareness arises from the

ability to have thoughts about one’s own mental

states (Rosenthal, 2000).

• In the domain of social psychology:

– Social Awareness: How individuals are aware

of and respond to the social environment

(Durkheim, 1893). It includes theories related

to social perception, empathy, and understand-

ing the mental states of others.

• In the domain of general systems:

– Integrated Information Theory (IIT): IIT

(Tononi, 2004) says that a system is conscious

if it has a high degree of integrated information.

This theory has received continuous support

thanks to its formal nature.

• In the domain of quantum mechanics:

Towards a Theory of Awareness

1415

– Orchestrated Objective Reduction (Orch-OR):

Orch-OR (Hameroff and Penrose, 2014) sug-

gests that quantum processes in microtubules

within brain cells play a role in consciousness.

Besides the effort put and the raising interest in the

topic, the study of awareness is still in a pre-scientiﬁc

stage. There are lots of ongoing research and debate,

but no single theory has gained unanimous acceptance

(Jylkk

a and Railo, 2019). See, for example, the re-

cent polemics about IIT being considered as pseudo-

science by some authors, esp. in the neuropsychology

domain (Fleming, 2023).

4.2 Awareness vs Consciousness

As said at the beginning, the terms “awareness”

and “consciousness” are often used interchangeably.

However the two terms are sometimes used differ-

ently in various contexts, and there isn’t a universally

agreed-upon distinction.

Different perspectives —GWT, IIT, Orch-OR,

etc.— attempt to differentiate awareness from con-

sciousness but these distinctions are not universally

accepted. Different theories and disciplines may use

the terms in varying ways and explain them using con-

ceptualizations that are far from beign harmonised.

The ﬁeld remains dynamic, and our understanding

of consciousness and awareness is likely to evolve

with ongoing research and interdisciplinary explo-

ration and the elaboration of proper, transversal the-

ories.

A distinction that may be somewhat distilled from

the previous list is that awareness is related to infor-

mation –i.e. to the epistemic aspect– and conscious-

ness to sentience –i.e. to the phenomenic aspect. The

work that we are doing points into the epistemic direc-

tion, leaving the sentience aspect to ulterior scientiﬁc

efforts.

5 THE CONTENT OF A THEORY

The ToA shall be a theory that is both scientiﬁc and

operationalisable. As a scientiﬁc theory, the ToA shall

be a well-substantiated explanation of some aspect of

the natural world that is based on a body of evidence,

observations, and experiments. This body of evidence

comes from the cognitive operation of animals and

also from the cognitive operation of machines, esp. in

the uncertain domains of the open robot world. The

“aspect of the natural world” that we are interested in

is the phenomenon of “awareness”.

We want the ToA to be a solid theory —a robust

and well-established scientiﬁc explanation— to serve

as the framework upon which scientists could build

their understanding of the phenomenon of awareness

and pile-up solid research results, and engineers use

this understanding in its operationalisation as applied

science in building better cognitive robots.

5.1 Characteristics of a Scientiﬁc

Theory

This is a list of key components and characteristics of

a scientiﬁc theory and to what extent the ToA shall

address them:

Empirical Basis: A scientiﬁc theory is grounded in

empirical evidence. It should be supported by

a substantial body of observations, experiments,

and data collected through systematic and repeat-

able methods. This empirical support is crucial in

distinguishing a theory from a mere hypothesis or

conjecture. The source of evidence is deployed

human cognition and the situations where aware-

ness plays a central role. For example, the META-

TOOL project

explores evidence concerning the

role of awareness in ancient tool making. Obvi-

ously we are dealing with cognitively problematic

situations (Norman, 1980) and hence, a proper or-

ganization of evidence will be critical.

Consistency: A scientiﬁc theory must be internally

consistent, meaning its various components and

principles should not contradict one another. It

should provide a logical and coherent framework

for explaining observed phenomena. It shall

also be consistent with other accepted scientiﬁc

theories. The use of formal methods –as IIT

attempted– and the model-based methods of en-

gineering may provide the necessary support to

guarantee this consistence.

Testability: Scientiﬁc theories are falsiﬁable (Pop-

per, 1959). They can be subjected to experimen-

tation and observation, and there should be clear

criteria that, if not met, would disprove the the-

ory. The ability to test and potentially disprove

a theory is a fundamental aspect of the scientiﬁc

method. The level of robot performance in real

settings may provide this necessary evidence.

Predictive Power: A strong scientiﬁc theory can

make predictions about future observations or ex-

periments. These predictions should be based on

the theory’s principles and should be veriﬁable

through empirical testing. The theory’s ability to

make accurate predictions lends further credibility

to it. This is an essential aspect for a theory that is

http://metatool-project.eu

AWAI 2024 - Special Session on AI with Awareness Inside

1416

used as a design asset in an engineering endeav-

our. Engineers will use designs based on this the-

ory to guarantee effectiveness in future systems.

Scope and Explanatory Power: A scientiﬁc theory

should have a broad scope, meaning it can explain

a wide range of related phenomena. The more

phenomena it can explain, the more powerful and

inﬂuential the theory is. This sits at the very core

of the scientist ambition: we target a theory that

not only addresses robotic awareness but general

cognitive systems awareness (see Figure 1).

Figure 1: Target a general cognitive theories of awareness.

Simplicity: If two or more theories explain the same

phenomena equally well, the simpler one is to be

preferred (Ockam’s razor). Simplicity makes the-

ories more elegant and easier to work with. So far,

there is no real competition in theories of aware-

ness (i.e. in the terms stated here). We shall try to

make the ToA simple using compact and effective

abstractions. The CORESENSE project

tries to

use category theory for these abstractions in part

motivated by this search for simplicity.

Reproducibility: The experiments should be repro-

ducible by other scientists using the same meth-

ods and conditions. This is a cornerstone of the

scientiﬁc method and ensures the reliability of the

theory. This has always been a problem in cog-

nitive robotics and in cognitive systems in gen-

eral. To this end, benchmarking has been used

in robotics to enhance this reproducibility. For

example, the RoboCup@Home challenge used in

robotics speciﬁcally addresses this problem.

Peer Review: Experts in the ﬁeld assess the theory’s

validity and the quality of the evidence support-

ing it. Peer review is an important process for

maintaining the rigor and reliability of scientiﬁc

theories. The Open Science approach of modern

research speciﬁcally addresses this need.

http://coresense.eu

Consensus: While scientiﬁc consensus can evolve

and change, a well-established theory is gener-

ally widely accepted within the scientiﬁc commu-

nity (achieving the status of “normal science” in

Kuhn terms or becoming a “framework of shared

commitments” (Eckardt, 1995)). In the domain of

psychology this is not easy. Psychological con-

structs suffer from the “toothbrush” problem: no

self-respecting psychologist wants to use anyone

else’s. But in rigorous science, we are sometimes

forced to do so by the force of the facts. If our the-

ory is solid, well documented and effective, con-

sensus will eventually emerge.

Understandability: As quantum mechanics demon-

strate, understandability is not a necessary char-

acteristic of scientiﬁc theory. However, we would

like our theory to be understandable by a broad

community of scientists and engineers. This may

require from us the expression of the theory in dif-

ferent ways that can reach these people.

In science, alive theories are not absolute truths

but are just our best current explanations based on the

available evidence. The process of forming and reﬁn-

ing scientiﬁc theories is the essential ongoing and dy-

namic aspect of the scientiﬁc endeavour. In a sense,

this ﬁltering-out by evidence can only happen when

the abstract concepts are strictly mapped into more

concrete realities. Most of the discussion on aware-

ness is pre-scientiﬁc in this sense: deals with abstrac-

tions disconnected from the evidence.

5.2 Operationalisation of a Scientiﬁc

Theory

The term “operationalizable theory” is not a standard

concept in scientiﬁc terminology. It is a combina-

tion of two important concepts: “theory” and “opera-

tionalization.”

Theory: As described before, a theory is a well-

substantiated explanation of some aspect of the

natural world that is based on empirical evidence

and can be used to make predictions and under-

stand phenomena.

Operationalization: Operationalization is a process

in research where abstract concepts or variables

are deﬁned and measured in a concrete and ob-

servable way. It involves specifying how a the-

oretical concept will be measured or observed in

practice. This is a crucial step in turning abstract

theories into testable hypotheses and conducting

empirical research.

The term “operationalizable theory” describes a

theory that has been sufﬁciently developed and de-

Towards a Theory of Awareness

1417

ﬁned so that its key concepts and variables can be op-

erationalized for empirical experimentation. In this

context, an operationalizable theory would be one that

can be translated into speciﬁc, measurable variables

or constructs that researchers can work with to con-

duct experiments, gather data, and test hypotheses

(e.g. building AI-driven robots and deploying them in

open worlds). This operationalization is a crucial step

in the scientiﬁc method when examining the applica-

bility and validity of a theory in real-world scenarios.

6 INITIAL STEPS TO A THEORY

OF AWARENESS

In the CORESENSE and METATOOL projects we

have the speciﬁc task of developing a ToA. Any the-

ory is a complex construct that consists of several

key elements. These elements shall work together to

provide a comprehensive and well-substantiated view

of some aspect of the worlds. In some sense this

view is explanatory —e.g. when applied to humans

or animals— and in another sense this view is oper-

ational —as when it enables the construction of arte-

facts.

6.1 Essential Elements for a Theory

Table 1 describes some speciﬁc elements and aspects

that a theory typically has. It is too early in the de-

velopment of our ToA to detail how the theory ad-

dresses all of them. In this paper we will just address

the domain and the initial concepts. The domain is,

obviously, “awareness”. The initial concepts are sum-

marily described in Section 6.2.

The elements and structure of a theory may vary

depending on the ﬁeld of science and the nature of

the phenomenon being studied. This may imply some

speciﬁcs of the ToA when applied to certain classes

of systems. However, these elements collectively

contribute to the development of a robust and well-

supported scientiﬁc theory.

6.2 Initial Concepts for a Theory of

Awareness

When considering the domain of the theory, we shall

be aware that both consciousness and awareness are

mongrel concepts when applied to humans: They are

used in many senses, referring to different classes of

phenomena, generating confusion and long irrelevant

discussions.

An analytical effort is necessary to separate the

different mental aspects they refer to and a termino-

logical effort will be necessary to suitably label all

those aspects. In some cases, the terminological effort

is addressed by using noun phrases like “visual aware-

ness” or “synthetic awareness”, but this usually im-

plies a subclassing from a general “awareness” class.

Another approach is the use of preﬁxes to create new

terms when the intention is to create terms for unre-

lated classes of phenomena. Examples of this are P-

consciousness and A-consciousness for phenomenal

consciousness and access consciousness.

In this paper the terms are used in a very speciﬁc

sense: we are interested in the functional aspects of

awareness that let an autonomous system act prop-

erly when it is aware of the situation it is immersed

in. In our case, the situational awareness proposed by

Endsley (Endsley, 1995) address not only the system

environment but extends to 1) the system itself and 2)

its relation to the environment when pursuing an ex-

ternally imposed mission. The fundamental domain

entities in this scenario are: the autonomous system is

the subject of awareness, generating a mental model

of some objects situated in the environment, the part

of the world that is causally connected with both sub-

ject and objects.

In this base scenario of a subject being aware of an

object, we investigate the essential character of aware-

ness processes and how they are related to perception

and understanding. We are interested in the processes

that underlie the capability of the subject to be aware

and understand the changes in the object to be more

effective in completing its mission.

As part of these initial steps, these are some fun-

damental concepts under elaboration in this research:

Sensing: Getting information –sense-data– bound to

an object in the environment.

Perceiving: Integration of the sensory information

into a model of the object by means of a modelet.

Modelet: A partial model related to a target system.

A information structure that sustains a modelling

relation (Rosen, 2012).

Model: Integrated actionable representation; an inte-

grated set of modelets.

Engine: Set of operations over a model (e.g., integra-

tion of a modelet, exertion, compaction, pruning,

intensiﬁcation, chunking, etc).

Inference: Derive conclusions from the model.

Valid inference: A inference whose result matches

the phenomenon at the modelled object.

Exert a model: Perform valid inferences from the

model.

AWAI 2024 - Special Session on AI with Awareness Inside

1418

Table 1: Essential Elements and Aspects of a Theory.

Element

Content of the element

Phenomenon

or domain

A theory explains a speciﬁc natural phenomenon or a particular domain of inquiry. It de-

ﬁnes the scope of what it seeks to explain. In our case: “awareness”.

Concepts The theory contains a set of well-deﬁned concepts that provide the vocabulary and frame-

work for discussing and understanding the phenomenon.

Hypotheses The theory often generates speciﬁc hypotheses, i.e. testable predictions about how certain

variables or factors are related within the deﬁned domain, and guide empirical research.

Laws or Prin-

ciples

A theory may incorporate laws or principles typically derived from empirical data and ob-

servations, that describe relationships or patterns observed within the phenomenon.

Relationships The theory speciﬁes causal relationships between the concepts and variables involved.

Explanatory

Power

A theory should have a high degree of explanatory power, meaning it can account for a wide

range of observations and data within its domain.

Predictive

Power

A strong theory can make accurate predictions about future observations or experiments, i.e.

should be veriﬁable through empirical testing.

Models In some scientiﬁc theories, especially in the physical sciences and engineering, formal mod-

els may be used to describe and predict the behaviour of the phenomenon.

Empirical

Support

A theory is grounded in empirical evidence. It should be supported by a substantial body of

observations, experiments, and data collected through systematic and repeatable methods.

Evolution Scientiﬁc theories are subject to revision as new evidence and understanding emerge.

Consistency A scientiﬁc theory must be internally consistent, meaning its various components and prin-

ciples should not contradict each other.

Reviews Scientiﬁc theories are typically subjected to peer review, where experts in the ﬁeld assess

the theory’s validity and the quality of the evidence supporting it.

Understanding: Achieving exertability of a modelet;

e.g. by mental model integration of a modelet and

activation of associated engines.

Speciﬁc understanding: Understanding concerning

a speciﬁc set of exertions (can be extensive or in-

tensive).

Structure understanding: Understanding the struc-

ture of the object implies achieving exertability

concerning the system structure.

Behaviour understanding: Understanding the be-

haviour of the object (achieving exertability con-

cerning the system behaviour).

Mission understanding: Understanding mission-

bound exertions (i.e. achieving derivability of

valid results from the model that can be used by

the agent to fulﬁll the mission).

Awareness: Real-time understanding of sensory

ﬂows.

Awareness of : Object-bound awareness.

Self-awareness: Subject-bound awareness. Aware-

ness concerning inner perceptive ﬂows.

The current project work is related to the devel-

opment of the formal model of the theory of aware-

ness in the form of 1) a formal ontology in higher-

order logic and 2) an architecture expressed in formal

MBSE

languages.

7 CONCLUSIONS

The elaboration of formal theories of awareness is ac-

tive (Bringsjord and Sundar, 2020) but still lost in

the variety of phenomena associated to conscious-

ness. An analytical approach at clarifying the many

aspects of mentality and awareness is necessary. This

means properly setting the boundaries of the domain

of explanation and being more precise on the class of

phenomena that the theory is addressing.

In this paper we have described the overall ex-

pected content of a Theory of Awareness to be of

applicability in the construction of broad classes of

autonomous systems. The elaboration of the causal

principles and laws will enable the development of ar-

chitectural patterns that will enable the design of new

systems (Sanz et al., 2007b). Besides architectural

patterns, this theory could also provide many other

resources in autonomous systems engineering to an-

alyze existing systems, to guide new designs, or to

build speciﬁc programs, etc. (examples of speciﬁc re-

MBSE is an acronym of Model-Based Systems Engi-

neering, a way of performing systems engineering where

models are the central assets.

Towards a Theory of Awareness

1419

sources are design idioms, complete reference archi-

tectures, common terminologies formalized through

ontologies, domain speciﬁc languages, reusable com-

ponents in software libraries, etc.).

See more about these developments at the CORE-

SENSE

and METATOOL

project websites and the

Awareness Inside

EIC Pathﬁnder Challenge.

ACKNOWLEDGMENTS

The authors acknowledge the support of the European

Commission through the Horizon Europe project

#101070254 CORESENSE and the European Inno-

vation Council project #101070940 METATOOL.

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