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Citation: Ehresmann, A.C.;
Béjean, M.; Vanbremeersch, J.-P. A
Mathematical Framework for
Enriching Human–Machine
Interactions. Mach. Learn. Knowl. Extr.
2023,5, 597–610. https://doi.org/
10.3390/make5020034
Academic Editor: Andreas Holzinger
Received: 2 February 2023
Revised: 4 May 2023
Accepted: 25 May 2023
Published: 6 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
machine learning &
knowledge extraction
Article
A Mathematical Framework for Enriching
Human–Machine Interactions
Andrée C. Ehresmann 1, *, Mathias Béjean 2and Jean-Paul Vanbremeersch 3
1LAMFA, Universitéde Picardie Jules Verne, 80039 Amiens, France
2IRG, UniversitéParis-Est-Créteil, 94010 Creteil, France
3EHPAD Saint-Joseph, 80330 Cagny, France
*Correspondence: ehres@u-picardie.fr
Abstract:
This paper presents a conceptual mathematical framework for developing rich human–
machine interactions in order to improve decision-making in a social organisation, S. The idea is to
model how S can create a “multi-level artificial cognitive system”, called a data analyser (DA), to
collaborate with humans in collecting and learning how to analyse data, to anticipate situations, and
to develop new responses, thus improving decision-making. In this model, the DA is “processed”
to not only gather data and extend existing knowledge, but also to learn how to act autonomously
with its own specific procedures or even to create new ones. An application is given in cases where
such rich human–machine interactions are expected to allow the DA+S partnership to acquire deep
anticipation capabilities for possible future changes, e.g., to prevent risks or seize opportunities. The
way the social organization S operates over time, including the construction of DA, is described
using the conceptual framework comprising “memory evolutive systems” (MES), a mathematical
theoretical approach introduced by Ehresmann and Vanbremeersch for evolutionary multi-scale,
multi-agent and multi-temporality systems. This leads to the definition of a “data analyser–MES”.
Keywords:
human–machine interactions; memory evolutive systems; artificial intelligence; anticipation
1. Introduction
In most social organizations, human–machine interactions are nowadays commonly con-
sidered as ubiquitous, the classic “machine” going from a simple computer to “a machine which
displays human-like capabilities such as reasoning, learning, planning and creativity” [
1
]. Still,
if the increasing use of AI is supposed to make all of these human–machine interactions more
efficient, the impact of AI will depend on what we mean by AI.
The history of AI is difficult to establish. Some authors date it back to the 1950s
and attribute its paternity to Marvin Minsky—one of the fathers of computer science and
cofounder in 1956 of the Artificial Intelligence Laboratory at MIT. Minsky’s dissertation in
1954 was entitled “A Theory of Neural Analog Reinforcement Systems and Its Application
to the Brain Model Problem” [
2
]. In the late 1960s, Minsky published a number of important
well-known works on similar themes, in particular [3,4] and later [5,6].
Other people date “real” AI much later and consider Newell and Simon to be the
introducers of “symbolic AI” in 1976, writing: “A physical symbol system has the necessary
and sufficient means of general intelligent action.” [
7
]. Historically, symbolism is indeed
the first of the two different paradigmatic approaches to AI. It is based on the definition of
an arbitrary set of symbols and semantic rules that manipulate the symbols.
Many more recent AI-based systems draw on a more bottom-up approach named
“connectionism” which seeks to explain intellectual abilities by using artificial “neural
networks” or “neural nets”. Among the connectionists, there are a large number of authors
dealing with complex neural systems and learning, such as Changeux, Dehaene, and
Toulouse [
8
], who use a physically based approach, or Rumelhart, Hinton, and Williams [
9
].
Mach. Learn. Knowl. Extr. 2023,5, 597–610. https://doi.org/10.3390/make5020034 https://www.mdpi.com/journal/make
Mach. Learn. Knowl. Extr. 2023,5598
Still, a certain number of authors do not recognize a deep gap between the symbolic
and the connectionist paradigms, with each of them having their own strengths and
weaknesses. This often leads to the idea of finding ways to combine the two paradigms.
For instance, Minsky stated in [
10
]: “Which approach is best to pursue? This question itself
is simply wrong. Each has virtues and deficiencies, and we need integrated systems that
can exploit the advantages of both”.
This general view has been developed further in more recent research works. For
instance, Zhang, Zhu, and Su [
11
] propose three generations of AI, with symbolism as the
first and connectionism as the second. Doing so, they call for a “third generation artificial
intelligence by combining the current paradigms”, for they believe that “AI cannot achieve
human behaviours by relying on only one paradigm”.
We defend this point of view, even if we do not agree with their tentative definition of
the third generation using a vector spaces framework. Instead, the aim of this paper is to
provide a conceptual mathematical framework, based on category theory [
12
], for a future
third generation of AI, and to show that this makes it possible to create multi-level artificial
cognitive systems allowing for richer human–machine interactions.
To this end, we first consider a social organisation S, such as a company, an educative
or a health institution. While preserving a kind of permanence or collective identity, we
assume that such an organisation evolves over time due to changes in the different natures
of its composition (e.g., members who leave S), structure (e.g., reconfiguration of material
resources of S) and functioning (e.g., new ways of working or communicating in S).
Then, we analyse the case of a close collaboration between S and an evolutionary
high-tech machine named a data analyser (DA) that S develops over time. This DA is
not only able to gather and memorize data about the environment, past and current
activities and potential difficulties, but is also able to develop common deep strategies,
for instance allowing the DA+S partnership to generate rich anticipatory assumptions for
risk prevention.
Let us note the particularity of such a DA on an example where S is a healthcare
service. In this case, medical robotics teams can propose robots to streamline workflows
and offer value in many areas by accomplishing specific and more-or-less repetitive tasks.
Our aim for DA is much richer. Our objective is that it should, by itself, be able to
detect some symptoms (after a thorough medical learning) and be trained to autonomously
select appropriate responses to resolve a situation. Another role we assign to DA is to
develop rich interactions with S that allow it to participate in decision-making meetings as
an actor and as an observer capable of detecting and seeking to correct misunderstandings
between participants.
To describe the functioning of S over time, including the construction of a DA, we
draw on the “memory evolutive systems” (MES) concept introduced by Ehresmann and
Vanbremeersch [
13
–
15
], a mathematical model for evolutionary and complex multi-scale,
multi-agent and multi-temporality systems, such as biological, cognitive and social systems.
This leads us to define a particular kind of MES, named DA–MES, by which we study how
DA can improve human–machine decision-making.
As we also wish to consider decision-making about the future, we analyse how a
particular DA–MES may become “Futures Literate” (FL), referring to Riel Miller’s work on
rich anticipatory processes and assumptions [
16
]. This makes it possible to show how, while
relying on collective intelligence knowledge creation processes, one can model the develop-
ment of richer human–machine interactions, including anticipation and risk prevention.
The paper is organised as follows: in Section 2, we recall the general notion of MES. In
Section 3, we introduce a particular kind of MES, called a DA–MES, in which we model the
construction of a “multi-level artificial cognitive system”, called a data analyser (DA), which
we then trained to develop rich human–machine interactions that allow for finer decision
making, including anticipation. In Section 4, we provide ideas for potential applications.
Mach. Learn. Knowl. Extr. 2023,5599
2. Methodological Approach: Recalls on MES
Memory evolutive systems (MES) were introduced by Ehresmann and Vanbremeer-
sch [
13
–
15
]. They consist of a mathematical approach based on category theory [
12
] coupled
with dynamic systems and are used for the study of “complex” evolutionary and adaptive
systems such as biological, social and cognitive systems.
Such systems have:
(i)
a tangled hierarchy of complexity levels with multifaceted components;
(ii)
a multi-agent, multi-temporal self-organisation with a network of local co-regulators,
each operating at its own rhythm with the help of;
(iii)
a flexible memory allowing for self-repair and adaptation to changes.
MES do not describe the invariant structure of the system but give a “dynamic model”
that evaluates the system in its becoming, with the variation of its components and their
interrelations over time, and with some of these interrelations disappearing while new
ones appear.
2.1. MES H Associated to a System S
This MES H has interacting components of different natures:
(i)
individuals and a hierarchy of groups of interacting individuals;
(ii)
material components such as artefacts, computers, machines, (in a DA–MES, this will
include the data analyser (DA) to be implemented);
(iii)
memory components such as contextual data, conceptual and procedural knowledge,
algorithms, various memories, and also unconscious or implicit knowledge such as
pure practices, heuristics, values of different kinds, and even affects and emotions.
The components and the links through which they can communicate are dynamic
entities whose successive states during their “life” can depend on some physical attributes
(e.g., activity at a given time, propagation delay and strength of a link, . . . ).
The configuration of H at a time t consists of the states at t of the components and links
between them that exist at that time. In the MES H, this is represented by a category Ht
which has for objects the states of the components existing at t, and for morphisms the states
of the links between them. Over time, there are two kinds of change for configurations:
(i)
dynamic changes of the state of the components and links existing at t, for instance
imposed by energetic constraints;
(ii)
structural changes leading to the possible loss or addition of some components
or links.
In H, the change of configuration, or ‘transition’ from t to t
0
> t is modelled by a partial
functor from Ht to Ht
0
, defined on the components and links at t which still exist at t
0
,
which maps their state at t on their new state at t
0
. As shown by Figure 1, the different
categories Ht and the transition functors connecting them form an evolutive system [
13
],
consisting in a functor H: T -> ParCat from the time category T to the category ParCat of
partial functors between (small) categories.
2.2. The Hierarchical Structure of H
The MES H has a compositional hierarchy. As illustrated by Figure 2, this means
that its components are distributed into a finite number of complexity levels, verifying the
condition: at a time t, an object C of the configuration category at t of level n + 1 ‘combines’
a pattern P of interacting components of levels
≤
n so that C alone has the same operational
role as P acting collectively.
In categorical terms, this means that C is the colimit (or inductive limit, Kan [
17
]) of
P. Formally, a pattern (or diagram) P in a category consists of a family of objects Pi and
some morphisms between them. A cone (modelling a collective link) from P to C is a family
of morphisms si from Pi to C well correlated by the morphisms distinguished in P. The
pattern P admits a colimit cP if there is a cone from P to cP through which any cone from P
Mach. Learn. Knowl. Extr. 2023,5600
to C
0
factors uniquely. A hierarchical evolutive system is an evolutive system in which the
configuration categories are hierarchical categories.
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 4
Figure 1. An evolutive system. This is defined by a functor from the category defining the order on
the timeline to the category of partial functors between small categories.
2.2. The Hierarchical Structure of H
The MES H has a compositional hierarchy. As illustrated by Figure 2, this means that
its components are distributed into a finite number of complexity levels, verifying the con-
dition: at a time t, an object C of the configuration category at t of level n + 1 ‘combines’ a
paern P of interacting components of levels ≤ n so that C alone has the same operational
role as P acting collectively.
In categorical terms, this means that C is the colimit (or inductive limit, Kan [17]) of
P. Formally, a paern (or diagram) P in a category consists of a family of objects Pi and
some morphisms between them. A cone (modelling a collective link) from P to C is a fam-
ily of morphisms si from Pi to C well correlated by the morphisms distinguished in P. The
paern P admits a colimit cP if there is a cone from P to cP through which any cone from
P to C′ factors uniquely. A hierarchical evolutive system is an evolutive system in which
the configuration categories are hierarchical categories.
Figure 2. A hierarchical category. A category is hierarchical if the class of its objects is partitioned
into a finite number of complexity levels, so that each object C of level n + 1 is the colimit of at least
one paern P with each Pi of level < n + 1; such a P is called a lower levels decomposition of C.
Figure 1.
An evolutive system. This is defined by a functor from the category defining the order on
the timeline to the category of partial functors between small categories.
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 4
Figure 1. An evolutive system. This is defined by a functor from the category defining the order on
the timeline to the category of partial functors between small categories.
2.2. The Hierarchical Structure of H
The MES H has a compositional hierarchy. As illustrated by Figure 2, this means that
its components are distributed into a finite number of complexity levels, verifying the con-
dition: at a time t, an object C of the configuration category at t of level n + 1 ‘combines’ a
paern P of interacting components of levels ≤ n so that C alone has the same operational
role as P acting collectively.
In categorical terms, this means that C is the colimit (or inductive limit, Kan [17]) of
P. Formally, a paern (or diagram) P in a category consists of a family of objects Pi and
some morphisms between them. A cone (modelling a collective link) from P to C is a fam-
ily of morphisms si from Pi to C well correlated by the morphisms distinguished in P. The
paern P admits a colimit cP if there is a cone from P to cP through which any cone from
P to C′ factors uniquely. A hierarchical evolutive system is an evolutive system in which
the configuration categories are hierarchical categories.
Figure 2. A hierarchical category. A category is hierarchical if the class of its objects is partitioned
into a finite number of complexity levels, so that each object C of level n + 1 is the colimit of at least
one paern P with each Pi of level < n + 1; such a P is called a lower levels decomposition of C.
Figure 2.
A hierarchical category. A category is hierarchical if the class of its objects is partitioned
into a finite number of complexity levels, so that each object C of level n + 1 is the colimit of at least
one pattern P with each Pi of level < n + 1; such a P is called a lower levels decomposition of C.
Over time, the decomposition P of C may vary progressively and eventually com-
pletely disappear while C persists. C acts as a “Janus”: it is “simple” vs. higher levels, and
“complex” vs. lower levels. Successive decompositions of C down to level 0 are named the
ramifications of C. The order of complexity of C at t is the smallest length of a ramification
of C; it is less or equal to the level of C. This definition is inspired by the Kolmogorov [18]
and Chaitin [19] complexity of a string.
2.3. The Multiplicity Principle at the Basis of Flexibility
An important property of the MES H is the ‘flexible redundancy’ which generalizes the
degeneracy property of biological systems (Edelman [
20
], Edelman and Gally [
21
]). This
Mach. Learn. Knowl. Extr. 2023,5601
asserts the existence of the components C which are multifaceted, in the sense that they can
operate through (formally, are the colimit of) several structurally different non-connected
lower-level decompositions and can switch between them; over time, they take their own
individuation, independently of their lower-level constituents. In H, this property is called
the multiplicity principle (MP). As shown by Figure 3, it allows for the existence, beside the
simple links which bind clusters (Beurier [
22
]) of lower-level links, of complex links which
are composites formed when the simple links bind non-adjacent clusters. In Chalmer’s [
23
]
terminology, these complex links are weakly emergent at their level with respect to the
lower levels. The emergence of complex links is at the root of “change in the conditions of
change” in Karl Popper’s sense [24].
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 5
Over time, the decomposition P of C may vary progressively and eventually com-
pletely disappear while C persists. C acts as a “Janus”: it is “simple” vs. higher levels, and
“complex” vs. lower levels. Successive decompositions of C down to level 0 are named
the ramifications of C. The order of complexity of C at t is the smallest length of a ramifi-
cation of C; it is less or equal to the level of C. This definition is inspired by the Kolmogo-
rov [18] and Chaitin [19] complexity of a string.
2.3. The Multiplicity Principle at the Basis of Flexibility
An important property of the MES H is the ‘flexible redundancy’ which generalizes
the degeneracy property of biological systems (Edelman [20], Edelman and Gally [21]).
This asserts the existence of the components C which are multifaceted, in the sense that
they can operate through (formally, are the colimit of) several structurally different non-
connected lower-level decompositions and can switch between them; over time, they take
their own individuation, independently of their lower-level constituents. In H, this prop-
erty is called the multiplicity principle (MP). As shown by Figure 3, it allows for the exist-
ence, beside the simple links which bind clusters (Beurier [22]) of lower-level links, of
complex links which are composites formed when the simple links bind non-adjacent
clusters. In Chalmer’s [23] terminology, these complex links are weakly emergent at their
level with respect to the lower levels. The emergence of complex links is at the root of
“change in the conditions of change” in Karl Popper’s sense [24].
Figure 3. Simple and complex links. C is a multifaceted object colimit of both Q and P. The link g:
cQ′ -> cQ is a simple link binding a cluster G from Q′ to Q, and idem for f binding a cluster from P
to P′. Their composite is a complex link which is only weakly emergent at its level.
Theorem 1. Complexity Theorem. MP is a necessary condition for the existence of components of
complexity order > 1. Otherwise, we have pure reductionism. With MP we can speak of emergentist
reductionism (in the sense of Mario Bunge [25]).
MP gives flexibility to the system, in particular to the development of robustness
though flexible memory (cf. Section 2.5) in which a component (named record) can be
recalled through any of its different ramifications, providing plasticity over time to adapt
to environmental changes.
2.4. The De/Complexification Process Leading to Emergence
As stated in Section 2.1, the change of configuration from t to t′ is both due to dynam-
ical changes of states and to structural changes. The structural changes correspond to the
four “standard transformations” singled out by Thom [26]: Birth, Death, Scission, Conflu-
ence. In H, these correspond to the introduction of a new component (e.g., recruitment of
a new employee), rejection of an existing one, and decomposition or formation of an in-
teractive group of components. As illustrated by Figure 4, in the configuration category
Figure 3.
Simple and complex links. C is a multifaceted object colimit of both Q and P. The link
g: cQ
0
-> cQ is a simple link binding a cluster G from Q
0
to Q, and idem for f binding a cluster from P
to P0. Their composite is a complex link which is only weakly emergent at its level.
Theorem 1.
Complexity Theorem. MP is a necessary condition for the existence of components of
complexity order > 1. Otherwise, we have pure reductionism. With MP we can speak of emergentist
reductionism (in the sense of Mario Bunge [25]).
MP gives flexibility to the system, in particular to the development of robustness
though flexible memory (cf. Section 2.5) in which a component (named record) can be
recalled through any of its different ramifications, providing plasticity over time to adapt
to environmental changes.
2.4. The De/Complexification Process Leading to Emergence
As stated in Section 2.1, the change of configuration from t to t
0
is both due to dy-
namical changes of states and to structural changes. The structural changes correspond
to the four “standard transformations” singled out by Thom [
26
]: Birth, Death, Scission,
Confluence. In H, these correspond to the introduction of a new component (e.g., recruit-
ment of a new employee), rejection of an existing one, and decomposition or formation
of an interactive group of components. As illustrated by Figure 4, in the configuration
category Ht, they correspond to the following operations: ’adding’ external elements,
’suppressing’ or ’decomposing’ some components, and ‘combining’ a given pattern P into a
new emerging component (due to become the colimit of P).
Given a procedure Pr with objectives of the above kinds on the category H, the
de/complexification process for Pr consists in constructing a category H
0
in which these
objectives are optimally satisfied (meaning H
0
is the solution of a universal problem). In [
13
],
H
0
has been explicitly constructed and its ‘categorical’ construction gives conditions on Pr
for the validity of the following:
Mach. Learn. Knowl. Extr. 2023,5602
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 6
Ht, they correspond to the following operations: ’adding’ external elements, ’suppressing’
or ’decomposing’ some components, and ‘combining’ a given paern P into a new emerg-
ing component (due to become the colimit of P).
Figure 4. De/complexification process. We consider the procedure Pr on H with objectives: to add
the set A, to suppress the cone E, and to add colimits cP′ and cQ′ to the diagrams P′ and Q′. The
de/complexification H′ ‘optimally’ adds the colimit cones of bases Q′ and P′, the simple links cG and
cG′ binding the clusters G and G′ and the complex link c: cQ′ -> cP′ which is their composite.
Given a procedure Pr with objectives of the above kinds on the category H, the
de/complexification process for Pr consists in constructing a category H′ in which these
objectives are optimally satisfied (meaning H′ is the solution of a universal problem). In
[13], H′ has been explicitly constructed and its ‘categorical’ construction gives conditions
on Pr for the validity of the following:
Theorem 2. Emergence Theorem. For specific procedures, the de/complexification process leads to
the emergence of (multifaceted) components of increasing complexity orders and of complex links,
which render unpredictable the result of iterated de/complexifications.
Warning. The ‘categorical’ construction of H′ does not explicitly take into account the dynamic
aributes of the components and links. For H′ to become the effective configuration of the system
at a later time, it must be compatible with the different dynamic and physical constraints imposed
by these aributes.
2.5. The Memory
The social organization S develops a flexible long-term memory. In the MES H asso-
ciated with S, the memory is modelled by a hierarchical evolutive subsystem whose com-
ponents are named records. A multifaceted record takes its own individuation over time
and can be recalled through its different ramifications. This memory develops over time
through successive de/complexifications, and it therefore acquires records of increasing
complexity orders (cf. emergence theorem which implies that iterated de/complexification
processes give a categorical approach to “Deep Learning”).
In the memory, we distinguish different types of records, among them:
(i) A procedural memory whose records memorize some kind of action allowing certain
objectives to be achieved; such a procedural record Pr is connected by ‘command’
links to a paern of effectors through which it can be realized; for instance, a proce-
dure for modifying the dynamic aributes of a component can be realized by an al-
gorithm that computes the changes to be undertaken. If Pr has formerly been applied
with success in a specific situation, it can exist an activator link from the record of the
situation to Pr;
Figure 4.
De/complexification process. We consider the procedure Pr on H with objectives: to add
the set A, to suppress the cone E, and to add colimits cP
0
and cQ
0
to the diagrams P
0
and Q
0
. The
de/complexification H
0
‘optimally’ adds the colimit cones of bases Q
0
and P
0
, the simple links cG and
cG0binding the clusters G and G0and the complex link c: cQ0-> cP0which is their composite.
Theorem 2.
Emergence Theorem. For specific procedures, the de/complexification process leads to
the emergence of (multifaceted) components of increasing complexity orders and of complex links,
which render unpredictable the result of iterated de/complexifications.
Warning.
The ‘categorical’ construction of H
0
does not explicitly take into account the dynamic
attributes of the components and links. For H
0
to become the effective configuration of the system at
a later time, it must be compatible with the different dynamic and physical constraints imposed by
these attributes.
2.5. The Memory
The social organization S develops a flexible long-term memory. In the MES H associ-
ated with S, the memory is modelled by a hierarchical evolutive subsystem whose com-
ponents are named records. A multifaceted record takes its own individuation over time
and can be recalled through its different ramifications. This memory develops over time
through successive de/complexifications, and it therefore acquires records of increasing
complexity orders (cf. emergence theorem which implies that iterated de/complexification
processes give a categorical approach to “Deep Learning”).
In the memory, we distinguish different types of records, among them:
(i)
A procedural memory whose records memorize some kind of action allowing certain
objectives to be achieved; such a procedural record Pr is connected by ‘command’ links
to a pattern of effectors through which it can be realized; for instance, a procedure for
modifying the dynamic attributes of a component can be realized by an algorithm that
computes the changes to be undertaken. If Pr has formerly been applied with success in
a specific situation, it can exist an activator link from the record of the situation to Pr;
(ii)
A semantic memory which gradually develops through the classification of records
into invariance classes represented by a specific concept [13].
2.6. The Local and Global Dynamics
H acts as a multi-agent self-organized system whose dynamics are modulated by
the cooperation and/or competition between its processing agents. These agents, named
co-regulators, can be simple individuals or formal groups of interacting people and/or
machines (for instance, the data analyser of a DA–MES will act as a co-regulator). The
overall dynamics weave the different internal local dynamics of the co-regulators. In the
MES H, a co-regulator is modelled by an evolutive subsystem.
Mach. Learn. Knowl. Extr. 2023,5603
Each co-regulator CR has its own function, its differential access to the memory, in
particular in recalling the procedures related to its function, and it acts stepwise at its own
rhythm; the rhythms of the co-regulators can be very different.
A step of a co-regulator CR from t to t
0
is divided into three or more or less
overlapping phases
:
(i) Formation of the landscape of CR at t, which collects the partial information of the sys-
tem and its environment that is obtained via the active links arriving to objects of CR
during the step from other parts of the system, e.g., the memory or other co-regulators.
This information is analysed in order to make sense of it; (In H, the landscape is
modelled by an evolutive system that has these active links for components.)
(ii)
Using the memory, a procedure Pr is selected through the landscape. It is not realized
on the landscape but by activation of its commands to the effectors of Pr;
(iii)
This starts a dynamical process (eventually leading to differential equations) whose
result will be evaluated at the beginning of the next step. If the objectives of Pr are not
attained, in particular if Pr is not compatible with dynamic and temporal constraints,
there is a fracture for CR.
At a given time, the various co-regulators may send conflicting commands to effectors.
The global dynamic results form an ‘interplay’ among them, and this may cause a fracture
to some of them. While the local dynamics can be computable, the interplay between
co-regulators renders the global dynamic unpredictable.
2.7. MENS and Multi-Level Artificial Cognitive Systems
In Entropy, the paper entitled “MENS” [
27
] details the application of MES to the
modelling of neuro-cognitive systems. The so-called model MENS is an MES whose level
0 of its hierarchy, NEUR, models the neural system whose configuration at a time t is the
category of synaptic paths between neurons existing at t. The construction of its higher
levels relies on two properties of the neural system (Edelman [20]):
(i)
The Hebb rule [
28
]: a mental object corresponds to the formation and reinforcement
of a synchronous pattern of neurons;
(ii)
The degeneracy of the neural code, which will imply that MENS satisfies the “Multi-
plicity Principle” (MP, cf. Section 2.3).
Its successive levels, whose components are called cat-neurons (for category neurons),
are constructed by successive de/complexifications of the level 0.
More precisely: as shown by Figure 5, a cat-neuron of level 1 models a mental object O
which activates a synchronous pattern P of neurons as the “binding” (or colimit) cP of P
added via a de/complexification of the category of neurons at t.
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 8
Figure 5. A cat-neuron of level 2. This is obtained via a double de/complexification of Neur and it
gives a dynamic model of a mental object O by becoming the colimit of the different neural paerns
activated by O.
Cat-neurons of higher levels are obtained by successive de/complexifications of lower
levels so that a cat-neuron of level n + 1 is obtained by iterative binding of paerns of
lower-level cat-neurons, which model flexible mental objects or processes of increasing
complexity. Due to (ii) such a cat-neuron gives a “dynamic” model of a mental object O
by becoming the colimit cP = cP′ in MENS of the various paerns P and P′ of the (cat-
)neurons of lower levels able to activate O.
The number of levels increases over time, allowing for the formation of a robust and
flexible memory with a higher level, called an archetypal core, driving the formation of
conscious processes [13].
“Multi-level artificial cognitive systems”, also defined in [27] (see the last section),
are similarly defined as an MES obtained by successive de/complexifications of a category
of paths of a graph satisfying the analogue of the Hebb rule and of the multiplicity prin-
ciple. An important result obtained in [29] is that MENS supports both symbolism and
connectionism (and even an “iterated connectionism” which defines a connectionism for
each level). Due to the similarity of the constructions of the artificial cognitive systems, the
same result is valid for each of them.
Now, if we accept the classification of AI in [11], it follows that such systems are of
the third generation AI. If such a result seems more easily obtained here than in [11], it is
because we use stronger mathematical tools based on category theory. For instance, the
complexification process, in presence of the multiplicity principle, leads to the formation
of multifaceted objects and complex links between them. Such an approach provides a
way of combining both the connectionist and symbolist views, while the use of the vector
spaces framework in [11] raises issues at each step.
3. Results: Constructing a DA–MES for Enriching Human–Machine Interactions
3.1. Definitions
Here, we define the concept of a DA–MES to model a continuous situation in which
a social organization S collectively develops an internal multi-level artificial cognitive sys-
tem (cf. Section 2.7), called a “data analyser” (DA). This DA should be able to collect and
memorize a large number of different data and knowledge and, through rich human–ma-
chine interactions, act as a partner in collaborative decision-making.
As illustrated by Figure 6, the DA is conceptually equipped with four main units:
(i) A “receptors unit” with different kinds of ‘receptors’ (e.g., sensors, user interfaces,
etc.) and ‘effectors’ to communicate both ways between the system and its environ-
ment. Let us note that, unlike in [30], we do not seek to describe a specific implemen-
tation of this unit but only to indicate its role;
(ii) A central “processing unit” to analyse and treat information, for instance by con-
structing de/complexifications;
(iii) A “multi-level memory” which develops over time;
Figure 5.
A cat-neuron of level 2. This is obtained via a double de/complexification of Neur and it
gives a dynamic model of a mental object O by becoming the colimit of the different neural patterns
activated by O.
Mach. Learn. Knowl. Extr. 2023,5604
Cat-neurons of higher levels are obtained by successive de/complexifications of lower
levels so that a cat-neuron of level n + 1 is obtained by iterative binding of patterns of
lower-level cat-neurons, which model flexible mental objects or processes of increasing
complexity. Due to (ii) such a cat-neuron gives a “dynamic” model of a mental object O by
becoming the colimit cP = cP
0
in MENS of the various patterns P and P
0
of the (cat-)neurons
of lower levels able to activate O.
The number of levels increases over time, allowing for the formation of a robust and
flexible memory with a higher level, called an archetypal core, driving the formation of
conscious processes [13].
“
Multi-level artificial cognitive systems
”, also defined in [
27
] (see the last section),
are similarly defined as an MES obtained by successive de/complexifications of a category
of paths of a graph satisfying the analogue of the Hebb rule and of the multiplicity prin-
ciple. An important result obtained in [
29
] is that MENS supports both symbolism and
connectionism (and even an “iterated connectionism” which defines a connectionism for
each level). Due to the similarity of the constructions of the artificial cognitive systems, the
same result is valid for each of them.
Now, if we accept the classification of AI in [
11
], it follows that such systems are of
the third generation AI. If such a result seems more easily obtained here than in [
11
], it is
because we use stronger mathematical tools based on category theory. For instance, the
complexification process, in presence of the multiplicity principle, leads to the formation of
multifaceted objects and complex links between them. Such an approach provides a way of
combining both the connectionist and symbolist views, while the use of the vector spaces
framework in [11] raises issues at each step.
3. Results: Constructing a DA–MES for Enriching Human–Machine Interactions
3.1. Definitions
Here, we define the concept of a DA–MES to model a continuous situation in which
a social organization S collectively develops an internal multi-level artificial cognitive
system (cf. Section 2.7), called a “
data analyser
” (DA). This DA should be able to collect
and memorize a large number of different data and knowledge and, through rich human–
machine interactions, act as a partner in collaborative decision-making.
As illustrated by Figure 6, the DA is conceptually equipped with four main units:
(i) A “receptors unit” with different kinds of ‘receptors’ (e.g., sensors, user interfaces, etc.)
and ‘effectors’ to communicate both ways between the system and its environment.
Let us note that, unlike in [
30
], we do not seek to describe a specific implementation
of this unit but only to indicate its role;
(ii) A central “processing unit” to analyse and treat information, for instance by construct-
ing de/complexifications;
(iii)
A “multi-level memory” which develops over time;
(iv)
An “output unit” which transmits commands to effectors.
Let us note that we are not looking for an explicit implementation of these units. We
are only looking for a conceptual way for them to communicate, not an explicit practical
way to implement them.
DA is “evolutive” in the sense that, in time, S may improve DA performances by
configuring relevant changes (in hardware or software) to address current challenges
effectively, for instance by increasing the number, the precision and/or the capacities of the
receptors and effectors.
In the MES representing S, DA acts as a co-regulator of the system (cf. Section 2.6) able
to accomplish the following operations, either alone or through interactions with higher
co-regulators to form a collaborative work system:
(i)
As a CR, DA forms its landscape by continuously collecting material and behavioural
data through its receptors (e.g., sensors) that come from the system and its environ-
ment, in particular from different co-regulators. As shown by Figure 7, it selects an
Mach. Learn. Knowl. Extr. 2023,5605
admissible procedure Pr with the help of the memory (seen as pr in the landscape)
and sends its commands to effectors E. The result is evaluated at the end of the step,
(ii)
DA helps to develop the memory. As shown by Figure 8, the activation of a pattern P of
receptors is transported, via the landscape, into the activation of a pattern P
0
in the processing
unit. The record of P will be a colimit of P0added via a de/complexification process.
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 9
(iv) An “output unit” which transmits commands to effectors.
Let us note that we are not looking for an explicit implementation of these units. We
are only looking for a conceptual way for them to communicate, not an explicit practical
way to implement them.
DA is “evolutive” in the sense that, in time, S may improve DA performances by
configuring relevant changes (in hardware or software) to address current challenges ef-
fectively, for instance by increasing the number, the precision and/or the capacities of the
receptors and effectors.
Figure 6. Presentation of DA. A DA with its 4 main units and the different kinds of interactions it
has with components of the system S.
In the MES representing S, DA acts as a co-regulator of the system (cf. Section 2.6)
able to accomplish the following operations, either alone or through interactions with
higher co-regulators to form a collaborative work system:
(i) As a CR, DA forms its landscape by continuously collecting material and behavioural
data through its receptors (e.g., sensors) that come from the system and its environ-
ment, in particular from different co-regulators. As shown by Figure 7, it selects an
admissible procedure Pr with the help of the memory (seen as pr in the landscape)
and sends its commands to effectors E. The result is evaluated at the end of the step,
Figure 7. DA landscape. DA acts as a CR by steps. One step is divided into three parts: (i) formation
of the landscape which is an ES with components that are the links activating at least one element
of DA during the step; (ii) selection of a procedure Pr in the memory via pr; and (iii) sending the
commands of Pr to its effectors. There is a fracture if the results of Pr are not achieved at the end of
the step.
Figure 6.
Presentation of DA. A DA with its 4 main units and the different kinds of interactions it has
with components of the system S.
This memory is organized in a “relational database” by evolutionary computing.
DA also cooperates with higher co-regulators to develop the global memory of the
system S and organize it into multiple levels up to the formation of a conceptual
level (in the semantic memory) and of more complex procedures and procepts (in the
procedural memory).
(iii)
Another important role of DA will be in helping in decision-making by interacting
with higher co-regulators. Thus, the MES also acts as a “collaborative decision-making
system” [
31
]; however, eventually DA can itself select already known procedures and
realize them (by activation of their effectors).
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 9
(iv) An “output unit” which transmits commands to effectors.
Let us note that we are not looking for an explicit implementation of these units. We
are only looking for a conceptual way for them to communicate, not an explicit practical
way to implement them.
DA is “evolutive” in the sense that, in time, S may improve DA performances by
configuring relevant changes (in hardware or software) to address current challenges ef-
fectively, for instance by increasing the number, the precision and/or the capacities of the
receptors and effectors.
Figure 6. Presentation of DA. A DA with its 4 main units and the different kinds of interactions it
has with components of the system S.
In the MES representing S, DA acts as a co-regulator of the system (cf. Section 2.6)
able to accomplish the following operations, either alone or through interactions with
higher co-regulators to form a collaborative work system:
(i) As a CR, DA forms its landscape by continuously collecting material and behavioural
data through its receptors (e.g., sensors) that come from the system and its environ-
ment, in particular from different co-regulators. As shown by Figure 7, it selects an
admissible procedure Pr with the help of the memory (seen as pr in the landscape)
and sends its commands to effectors E. The result is evaluated at the end of the step,
Figure 7. DA landscape. DA acts as a CR by steps. One step is divided into three parts: (i) formation
of the landscape which is an ES with components that are the links activating at least one element
of DA during the step; (ii) selection of a procedure Pr in the memory via pr; and (iii) sending the
commands of Pr to its effectors. There is a fracture if the results of Pr are not achieved at the end of
the step.
Figure 7.
DA landscape. DA acts as a CR by steps. One step is divided into three parts: (i) formation
of the landscape which is an ES with components that are the links activating at least one element
of DA during the step; (ii) selection of a procedure Pr in the memory via pr; and (iii) sending the
commands of Pr to its effectors. There is a fracture if the results of Pr are not achieved at the end of
the step.
Mach. Learn. Knowl. Extr. 2023,5606
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 10
(ii) DA helps to develop the memory. As shown by Figure 8, the activation of a paern
P of receptors is transported, via the landscape, into the activation of a paern P′ in
the processing unit. The record of P will be a colimit of P′ added via a de/complexifi-
cation process.
Figure 8. Formation of a record in DA memory. The activation of a paern P of receptors leads (via
the landscape) to the activation of a paern P′ in the processing unit. The record recP of P is obtained
as a colimit of P′ added via a de/complexification process.
This memory is organized in a “relational database” by evolutionary computing.
DA also cooperates with higher co-regulators to develop the global memory of the
system S and organize it into multiple levels up to the formation of a conceptual level
(in the semantic memory) and of more complex procedures and procepts (in the pro-
cedural memory).
(iii) Another important role of DA will be in helping in decision-making by interacting
with higher co-regulators. Thus, the MES also acts as a “collaborative decision-mak-
ing system” [31]; however, eventually DA can itself select already known procedures
and realize them (by activation of their effectors).
3.2. General Functioning of DA–MES
To simplify, when no confusion can arise, we will not make an explicit distinction
between a social organization S equipped with a data analyser DA and its associated DA–
MES in which DA operates either by itself or in coordination with humans.
By itself, DA acts as a co-regulator of the MES: at a given time, its landscape gathers
the information received from its receptors, coming from the memory or sent by other co-
regulators. Depending on the number and precision of its receptors, it can distinguish
some weak signals (e.g., a small anomaly in the data sent by a sensor) and alert the system.
If it has already met a similar experience, it can even select (through an activator link) a
procedure used to correct it and activate its effectors. This allows for a quicker answer,
possibly avoiding risks that are more serious, but may need some control to avoid errors
or unethical behaviour.
However, we are going to show that the main role of DA is to cooperate with a human
co-regulator G to ensure high quality decision-making, with help from the development
of rich human–machine interactions. In this situation, the two co-regulators G and DA
and the different links which connect them act as a (macro-)coregulator of the DA–MES.
Its landscape, named the macro-landscape (ML), constitutes a collective working space in
which we are going to show that G and DA proceed as follows:
(i) They share information and knowledge of different kinds, thus forming a paern AG
called the paern of ‘G-archetypal’ records;
Figure 8.
Formation of a record in DA memory. The activation of a pattern P of receptors leads (via
the landscape) to the activation of a pattern P
0
in the processing unit. The record recP of P is obtained
as a colimit of P0added via a de/complexification process.
3.2. General Functioning of DA–MES
To simplify, when no confusion can arise, we will not make an explicit distinction
between a social organization S equipped with a data analyser DA and its associated
DA–MES in which DA operates either by itself or in coordination with humans.
By itself, DA acts as a co-regulator of the MES: at a given time, its landscape gathers
the information received from its receptors, coming from the memory or sent by other
co-regulators. Depending on the number and precision of its receptors, it can distinguish
some weak signals (e.g., a small anomaly in the data sent by a sensor) and alert the system.
If it has already met a similar experience, it can even select (through an activator link) a
procedure used to correct it and activate its effectors. This allows for a quicker answer,
possibly avoiding risks that are more serious, but may need some control to avoid errors or
unethical behaviour.
However, we are going to show that the main role of DA is to cooperate with a human
co-regulator G to ensure high quality decision-making, with help from the development
of rich human–machine interactions. In this situation, the two co-regulators G and DA
and the different links which connect them act as a (macro-)coregulator of the DA–MES.
Its landscape, named the macro-landscape (ML), constitutes a collective working space in
which we are going to show that G and DA proceed as follows:
(i)
They share information and knowledge of different kinds, thus forming a pattern AG
called the pattern of ‘G-archetypal’ records;
(ii)
They construct the macro-landscape ML with the help of AG and analyse it to make
sense of the present situation (retrospection process);
(iii)
They search for adequate procedures to answer the situation (prospection process)
and finally reach a consensus decision.
In these operations, DA does not just operate as a multi-level database but also as an
information collector able to detect some weak signals, and as an active coordinator.
3.3. Formation of a G-Archetypal Pattern AG of Shared Records
Initially, the members of G have different individual expertise and knowledge. A
higher order multifaceted record C, such as a polysemic concept integrating knowledge
of different modalities (explicit or not), may have different meanings for two members of
G depending on the ramifications through which they recall it. Exchanges of information
between them to reach a common understanding are perceived by DA receptors and
memorized so that DA may store C with all of its different ramifications, whence forming
a common enriched perspective C* of C accessible to all. C* encompasses the different
Mach. Learn. Knowl. Extr. 2023,5607
meanings of C and eventually some tacit knowledge, such as emotions aroused in the
course of the discussion. C* is called a G-archetypal record.
As illustrated by Figure 9, the G-archetypal records are connected by loops of strong
and fast complex links, which self-maintain their activity for a long time. With these
links they form the evolutive G-archetypal pattern, AG. (The development of AG is a
consequence of the emergence theorem).
Mach. Learn. Knowl. Extr. 2023, 5, FOR PEER REVIEW 11
(ii) They construct the macro-landscape ML with the help of AG and analyse it to make
sense of the present situation (retrospection process);
(iii) They search for adequate procedures to answer the situation (prospection process)
and finally reach a consensus decision.
In these operations, DA does not just operate as a multi-level database but also as an
information collector able to detect some weak signals, and as an active coordinator.
3.3. Formation of a G-Archetypal Paern AG of Shared Records
Initially, the members of G have different individual expertise and knowledge. A
higher order multifaceted record C, such as a polysemic concept integrating knowledge
of different modalities (explicit or not), may have different meanings for two members of
G depending on the ramifications through which they recall it. Exchanges of information
between them to reach a common understanding are perceived by DA receptors and
memorized so that DA may store C with all of its different ramifications, whence forming
a common enriched perspective C* of C accessible to all. C* encompasses the different
meanings of C and eventually some tacit knowledge, such as emotions aroused in the
course of the discussion. C* is called a G-archetypal record.
As illustrated by Figure 9, the G-archetypal records are connected by loops of strong
and fast complex links, which self-maintain their activity for a long time. With these links
they form the evolutive G-archetypal paern, AG. (The development of AG is a conse-
quence of the emergence theorem).
Remark 1. The formation of AG will be improved if DA can record voices through hearing devices,
memorize them, and process them to infer some personality traits of the speakers such as dominance
or trustworthiness (Ponsot et al. [32]). It may also detect some primary emotions (e.g., pleasure, or
arousal) from their aitudes and deduce other emotions (using the computational PAD Emotion
Model, Zangh et al. [33]). This would provide DA with a kind of theory of mind (ability to impute
unobservable mental states to others); for M. Kosinski [34]: “this ToM-like activity (thus far con-
sidered to be uniquely human) may have spontaneously emerged as a by product of language mod-
els’ improving language skills”.
Figure 9. Improving decision-making. Let G be a group of humans acting as a co-regulator of S. By
working with G, DA allows a beer cooperation between persons in forming a G-archetypal paern
AG of shared concepts. This allows the formation of a common macro-landscape, ML, in which
retrospection and prospection processes can develop.
3.4. Construction and Analysis of the Macro-Landscape ML
AG acts as an engine for the construction of the macro-landscape ML which contains
the landscapes of G and DA, interconnects them and extends them both spatially and
temporally Indeed, the recall by G or DA of a G-archetypal record C* in their landscape
first diffuses in AG through archetypal loops, then propagates to lower levels through the
Figure 9.
Improving decision-making. Let G be a group of humans acting as a co-regulator of S. By
working with G, DA allows a better cooperation between persons in forming a G-archetypal pattern
AG of shared concepts. This allows the formation of a common macro-landscape, ML, in which
retrospection and prospection processes can develop.
Remark 1.
The formation of AG will be improved if DA can record voices through hearing devices,
memorize them, and process them to infer some personality traits of the speakers such as dominance
or trustworthiness (Ponsot et al. [
32
]). It may also detect some primary emotions (e.g., pleasure, or
arousal) from their attitudes and deduce other emotions (using the computational PAD Emotion
Model, Zangh et al. [
33
]). This would provide DA with a kind of theory of mind (ability to impute
unobservable mental states to others); for M. Kosinski [
34
]: “this ToM-like activity (thus far
considered to be uniquely human) may have spontaneously emerged as a by product of language
models’ improving language skills”.
3.4. Construction and Analysis of the Macro-Landscape ML
AG acts as an engine for the construction of the macro-landscape ML which contains
the landscapes of G and DA, interconnects them and extends them both spatially and
temporally Indeed, the recall by G or DA of a G-archetypal record C* in their landscape
first diffuses in AG through archetypal loops, then propagates to lower levels through the
unfolding of ramifications and switches between them, thus extending ML to lower levels.
Moreover, ML lasts longer due to the self-maintained activation induced by AG.
In ML, current observations and recent events can be related to past, more-or-less
similar cases, allowing to sense and to make sense of the present situation, its trends and
its possible evolution. This retrospection process is followed by a prospection process, still
using the engine role of AG, to search for possible anticipatory assumptions, to ‘virtually’
evaluate their risk of dysfunction, and finally to select one. Once a consensus decision has
been taken, it is memorized by DA together with the rejected dissenting views, as well as
its later outcomes, to be of help if a similar situation recurs.
Remark 2.
The above constructions generalize those undertaken in a D–MES (Béjean and Ehres-
mann [
35
]) to model how a simple group of humans collaborates. The benefits of introducing DA
are the detection of more (even weak) signals and the development of a more efficient collaboration
thanks to a larger sharing of explicit or tacit knowledge.
Mach. Learn. Knowl. Extr. 2023,5608
4. Applications and Conclusions
4.1. DA–MES Becoming Futures Literate (FL) [16,36]
For a system to become FL means that it develops a rich variety of anticipatory
assumptions (AA) which “are the fundamental descriptive and analytical building blocks
for understanding FL” and “using-the-future” (Miller [
16
], p. 24). An AA for a co-regulator
CR (at a given time t) corresponds to the choice of a specific procedure Pr on its landscape
and evaluation of its expected result (if the realization of Pr succeeds). These AAs can
be explicit (conscious or not for human groups) or only tacit. The rich human–machine
interactions with DA can help transform tacit AAs into explicit ones that become realizable.
For instance, the anticipation of a risk will help to prevent it.
In Miller [
16
] (Chapter 2), AAs are classified in different groups. Here we adapt this
classification in our frame.
(i) AAs corresponding to some kind of “repetition”. These correspond to cases where the
present situation has already been met, and there is an activator link from its record
to a procedural record which had given a satisfying result. The AA will then consist
of the activation of the same procedure (via the activator link). An example is given
by the use of statistics, where this represents a kind of “colonization” of the future,
impeding real novelty. In cases where the situation has prompted different responses
in former occurrences, there can be several activator links, and some choice will have
to be made. In any case, the result of the procedure can be different from the expected
one, due to possibly unrecognized changes in the situation;
(ii)
AAs corresponding to novel futures in answer to the detection of “Specific-Unique”
phenomena such as weak signals. These may require new procedures, eventually
leading to the emergence of higher complex objects and links;
(iii)
In a DA–MES, the richer interactions between DA and G make it possible to find
more AAs, e.g., corresponding to weak signals, and to answer with a richer stock
of innovative procedures on extended landscapes. Thus, the system increases its
futures literacy.
4.2. An Illustration in the Case of Risk Prevention
Let us give a potential illustration that has been at the root of this research work,
namely the problem of health, or risk prevention in a care-home for elderly persons,
developed in Ehresmann and Vanbremeersch [37].
In this scenario, the medical staff (physicians and nurses), S, trains an evolutionary
multi-level artificial cognitive data analyser, DA, to assemble a large number of medical
knowledge and personal data on the residents (collected through non-invasive devices),
to analyse them, and to learn, from S, possible treatments and their effects. The DA–MES
conceptual framework could show how, together, they can develop creative processes to
monitor health risks and, as much as possible, prevent or reduce stressful events. Once DA
has memorized pathological symptoms, it can quickly recognize them, inform the medical
team and eventually begin an already used adequate treatment. Thus, pathologies are
recognized and cured more quickly, e.g., preventing dissemination of epidemics.
4.3. Conclusions
The aim of this article was to study how a social organization of any kind, say S, can
improve its internal cohesion and its external output by designing a high-tech multi-level
artificial cognitive system, called a data analyser (DA), able to cooperate with certain
human teams to improve their collaboration and so achieve a better quality of decisions.
Generally, in such a social organisation S, there are a number of high-tech machines,
acting as lower-level specialized co-regulators, but these machines are usually controlled
by human rules, leaving them with little freedom. In this paper, the idea was different
because we aimed to consider a rich collaboration between a multi-level artificial cognitive
data analyser, DA, constructed to act on its own and anticipate future situations.
Mach. Learn. Knowl. Extr. 2023,5609
To do so, we used the MES (Ehresmann and Vanbremeersch [
13
]) framework, a
conceptual mathematical approach based on category theory, developed for studying
complex evolutionary hierarchical systems, such as social or cognitive systems. The global
dynamic of such systems is obtained by an interplay between the dynamics of a family of
sub-systems, called co-regulators, acting as agents, each of which has its own complexity
level and temporality (cf. Section 2).
We then introduced the notion of a DA–MES (in Section 3), namely an MES which, in
time, modelled the building of a customized evolutionary multi-level artificial cognitive DA
with specific characteristics to make it more autonomous and independent from humans
than usual machines, even high-tech machines, and to become more anticipatory over time.
It was limited only in its assignment to respect human ethical rules.
The DA–MES framework opens new ways for modelling the collaboration between
machine and human teams. In particular, it provides conceptual tools to study how the
artificial cognitive system DA can collaborate with a human group working on a certain
topic. By modelling the different devices of DA as well as the characteristics of the co-
regulator G representing the human group (cf. Section 3), it is possible to formalize how
DA can exchange both ways with the members of G, considering the case where DA
explicitly participates in the discussions and even possibly supervises them. For instance,
the DA–MES framework renders it possible to model the way in which two members of
G can discuss the “same” multifaceted object by using its different facets, e.g., by using
two meanings of the same word, without realizing the differences. In this situation, the
DA–MES makes it possible to formalize at least two contrasting cases. First, the case where
the discussion leads to a misunderstanding threatening the cooperation between the
two G
members. Second, the case where DA identifies the problem with the multifaceted object,
memorizes the two meanings by forming an archetypal object (cf. Section 3) and finally
communicates with the two G members to facilitate their cooperation. The DA-MES could
even consider the case where DA identifies the way in which some emotions make it
difficult to reach a common understanding.
As a whole, the article provides a conceptual mathematical framework to conceive and
design artificial cognitive data analysers (DA) (which, as mentioned at the end of Section 2,
could exemplify a third generation AI) able to enhance the collaboration between humans
and machines. While potentially improving the quality of the decisions made, such new
DA would also be able not only to keep track of the decisions made, but also of the way
they were made, so that they could be recalled in cases where a similar experience occurs
later on. In particular, in Section 4, we gave an application in which the presence of such a
DA can be useful for decisions about the future. This allows the DA–MES to develop better
anticipatory assumptions, as far as becoming “futures literate” (as described in [
16
]). In
practical situations, this may help prevent some risks and lead to better futures.
Author Contributions:
Conceptualisation and methodology: A.C.E., M.B. and J.-P.V. have equally
contributed to this paper. This is a consequence of the fact that they have been working together for
many years in adjacent domains and is shown by the necessary self-references to some of their older
publications in the Bibliography; Writing: A.C.E. and M.B.; Graphics: J.-P.V. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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