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A Taxonomy of Computation and Information Architecture

Mark Burgin

Department of Mathematics,

UCLA, Los Angeles, USA

mburgin@math.ucla.edu

Gordana Dodig-Crnkovic

Chalmers University of Technology

and University of Gothenburg

dodig@chalmers.se

ABSTRACT

Nowadays computation is typically understood through the Turing

machine model, in the fields of computability, computational

complexity and even as a basis for present-day computer

hardware and software architectures. Those are technologies

designed in the first place to process data. Being description of

data manipulation, Turing model of computation presents only

one aspect of computation in the real world – an abstraction of the

execution of an algorithm. However, several other possible

aspects of computation, even those existing in today’s

applications, are left outside, thus adequate models in distributed,

self-organized, resource-aware, adaptive, learning computation

systems are needed. This paper presents taxonomy of existing

models of computation. It is connected to more general notion of

natural computation, intrinsic to physical systems, and particularly

cognitive computation in cognitive systems. We see Turing model

of computation as a basic mechanism which can be used to build

more complex computational architectures, that in combination

with interaction with the environment (learning) give advanced

information-processing behaviors in cognitive systems.

Keywords

Computation, Information Processing, Algorithms, Turing

Machine, Computing Architecture, Computing Taxonomy,

Cognitive computing

1. INTRODUCTION

Future progress of new computational devices capable of dealing

with problems of big data, internet of things, semantic web,

cognitive robotics and neuroinformatics depends on the adequate

models of computation. In this article we first present the current

state of the art through systematization of existing models and

mechanisms, and outline basic structural framework of

computation. We argue that defining computation as information

processing, and given that there is no information without

(physical) representation, the dynamics of information on the

fundamental level is physical/ intrinsic/ natural computation. As a

special case, intrinsic computation is used for designed

computation in computing machinery. Intrinsic natural

computation occurs on variety of levels of physical processes,

containing the levels of computation of living organisms

(including highly intelligent animals) as well as designed

computational devices.

The present article offers taxonomy of current models of

computation and indicates future paths for the advancement of the

field; both by the development of new computational models and

by learning from nature how to better compute using different

mechanisms of intrinsic computation.

The question “What is computation?” is answered differently

by different researchers (cf., for example, [1]–[8]). Some did this

in an informal setting based on computational and research

practice, as well as on philosophical and methodological

considerations. Others strived to build exact mathematical models

to comprehensively describe computation. When the Turing

machine (or Logical Computing Machine as Turing originally

named his logical device) was constructed and accepted as a

universal computational model, it was considered as the complete

and exact definition of computation (Church-Turing thesis).

However, the absolute nature of the Turing machine was

disproved by adopting a more general definition of algorithm [4].

In the spirit of broadening of the concept of computation, the

following definition was proposed: “For a process to qualify as

computation a model must exist such as algorithm, network

topology, physical process or in general any mechanism which

ensures definability of its behavior.” [9]

Nevertheless, in spite of all efforts, the conception of

computation remains too vague and ambiguous. This vagueness of

the foundations of computing has resulted in a variety of

approaches, including approaches that contradict each other. For

instance, [3] writes “to compute is to execute an algorithm.”

Active proponents of the Church-Turing Thesis, such as [10]

claim computation is bounded by what Turing machines are doing

(that is compute mathematical functions). For them the problem of

defining computation was solved long ago with the Turing

machine model. At the same time, Wegner and Goldin insist that

computation is an essentially broader concept than algorithm [11]

and propose interactive view of computing. While [12] argues that

computation is symbol manipulation, thus disregarding analog

computers computing over continuous signals, neuroscientists on

the contrary study sub-symbolic computation in neurons. [13]

Abramsky summarizes the process of successive changing of

computing models as follows:

“Traditionally, the dynamics of computing systems, their

unfolding behavior in space and time has been a mere means to

the end of computing the function which specifies the algorithmic

problem which the system is solving. In much of contemporary

computing, the situation is reversed: the purpose of the computing

system is to exhibit certain behaviour. (…) We need a theory of

the dynamics of informatic processes, of interaction, and

information flow, as a basis for answering such fundamental

questions as: What is computed? What is a process? What are the

analogues to Turing completeness and universality when we are

concerned with processes and their behaviors, rather than the

functions which they compute? [14]

Abramsky emphasizes that there is the need for second-generation

models of computation, and in particular process models. The first

generation models of computation originated from problems of

formalization of mathematics and logic, while processes or agents,

interaction, and information flow are results of recent

developments of computers and computing. In the second-

generation models of computation, previous isolated systems are

replaced by processes or agents for which the interactions with

each other and with the environment are fundamental. Hewitt too

advocates agent-type, Actor model of computation [8] which is

suitable for modeling of physical (intrinsic) computation.

Existence of various types and kinds of computation, as well as a

variety of approaches to the concept of computation, shows

remarkable complexity that makes communication of results and

ideas increasingly difficult. Our aim is to explicate present

diversity and so call attention to the necessity of common

understanding: different models of computation may have their

specific uses and applications. It is just necessary to understand

their mutual relationships and assumptions under which they

apply.

In this paper, we present methodological analysis of the concept

of computation before and after electronic computers and the

emergence of computer science, demonstrating that history brings

us to the conclusion that efforts in building such definitions by

traditional approaches would be inefficient. An effective

methodology is to find essential features of computation with the

goal to explicate its nature and to build adequate models for

research and technology. In addition, we perform structural

analysis of the concept of computation through explication of

various structures intrinsically related to computation, and in

particular cognitive computation.

One important question related to computation (information

processing) architecture is if cognition can be understood as

computation in the sense of Turing-Church thesis or if cognitive

computation needs additional constructs in case of self-managing

(autonomous) distributed computing systems such as human

brain. Cognition is taken to be the ability to process information,

apply knowledge, and act accordingly, with intent, and it s

accomplishment through various processes that monitor and

control a system and its environment. “Cognition is associated

with a sense of “self” (the observer) and the systems with which it

interacts (the environment or the “observed”). Cognition

extensively uses time and history in executing and regulating

tasks that constitute a cognitive process. In [15], [16] Mikkilineni

argues that “cognition requires more than mere book-keeping

provided by the Turing machines and certain aspects of cognition

such as self-identity, self-description, self-monitoring and self-

management can be implemented using parallel extensions to

current serial von-Neumann stored program control (SPC).”

The paper is organized in the following way. In Section 2 we

develop computational taxonomy, which allows us to extract basic

characteristics of computation and distinguish fundamental types

of computation. The suggested system of computational classes

allows us to reflect natural structures in the set of computational

processes. In Section 3 we study the structural context and

architecture of computation, illuminating the computational triad

and several other structures intrinsically related to computation. In

Section 4 we present the development of computational models in

natural computing (computing nature). Finally, we summarize and

present open questions in Section 5.

2. COMPUTATIONAL TAXONOMY

In his famous work [1] Turing presents his fundamental model,

which later was called the Turing machine model of computation.

Turing writes: “We may now construct a machine to do the work

of this computer.” Here a computer is a person performing

mechanical procedure executing an algorithm. Thus, algorithms

were the first models of computation. Algorithm was the practice

of algebra in the 18th century. In the 19th century, the term came to

mean any process of systematic calculation. In the 20th century,

Encyclopedia Britannica described algorithm as a systematic

mathematical procedure that produces – in a finite number of

steps – the answer to a question or the solution of a problem.

It is common that we talk about computation as if it would be a

uniquely defined concept. However some think of computation as

algorithm, others as symbol manipulation, while yet others may

have in mind a more general phenomenon of information

processing.

Currently there are so many kinds of concepts of computation and

its implementations that it is useful to make classification and

systematization so to better understand their mutual relationships.

In what follows we will present several main ttaxonomies of

computation: existential/substantial, organizational, temporal,

representational, data-oriented, operational, process-oriented and

level-based.

2.1 Existential taxonomy

On the different levels of organisation of objects/agents (subjects)

(physical, structural, interpretant) we find different types of

computational processes. According to Burgin, [17] p. 93, reality

in the most abstract way can ben represented by the existential

triad (physical, structural, mental1) which corresponds to Plato’s

triad (material, ideas/forms, mental). This can also be related to

the Peirce's triad of (object, sign, interpretant).

The existential/substantial classification of computation based on

the existential triad [18] defines the following types:

1. Physical or embodied (object) computations

2. Abstract or structural (sign) computations

3. Mental or cognitive (interpretant) computations

The existential types from this taxonomy have definite subtypes.

The following are types of embodied computations, where each

next level emerges as a consequence of previous ones.

1.1. Physical computations

1.2. Chemical computations

1.3. Biological computations

1.4. Cognitive computations

1 Here mental denotes that which is of or relating to the mind. The

mind is a traditional and vague concept and in scientific terms it

corresponds to an emergent process that emerges from

cognition. Unlike mind, which is ascribed primarily to humans,

cognition can be attributed to both animals and machines and is

therefore a more useful concept in the context of computational

architecture.

With respects to the structures (objects) that are processed by

computations, it is possible to discern the following types:

2.1 Subsymbolic computations - data/signal processing

2.2 Symbolic computations - data structures processing

2.3 Hybrid/mixed subsymbolic and symbolic computations.

There are connections between the above types. For instance [19]

differentiates between verbal (linguistic) (symbolic and

subsymbolic) and non-verbal (non-linguistic) (symbolic and

subsymbolic) mental (cognitive) processes. He suggests that the

principle of object formation may be an example of the transition

from a stream of massively parallel subsymbolic micro-functional

events to symbol-type, serial processing through subsymbolic

integration. Even [20] suggests similar connection between

symbolic and subsymbolic (connectionist) computations.

Mental (cognitive, interpretive) computations can be observed at

the following levels:

3.1 Individual (computational network of the brain)

3.2 Group (computational networks of individuals)

3.3 Social (computational networks of groups)

A taxonomy of computation in relation to cognition is provided by

Fresco [21], who notes that, depending on the specific kind of

“information” used, information-processing accounts of

computation may differ greatly. Four main types of information in

this context are given:

3.a Syntactic (Shannon) information

3.b Algorithmic information

3.c Semantic information

3.d Instructional information

2.2 Organizational taxonomy

Organization of computation can be characterized as:

1. Centralized computations where computation is controlled

by a single algorithm.

2. Distributed computations where there are separate algorithms

that control computation in some neighbourhood that is

represented by a node in the computational network.

3. Clustered computations where there are separate algorithms

that control computation in clusters of neighbourhoods.

Turing machines, partial recursive functions and limit Turing

machines are models of centralized computations. Neural

networks, Petri nets and Cellular automata are models of

distributed computations. Grid automata in which some nodes

represent networks with the centralized control and the World

Wide Web are systems that perform clustered computations [4].

Grid automaton is the most advanced abstract model of distributed

computing systems, which performs concurrent computations,

while being a physical distributed computing system.

2.3 Temporal taxonomy

With respect to temporal characteristics, computations can be:

1. Sequential computations, which are performed in linear time.

2. Parallel or branching computations, in which separate steps

(operations) are synchronized in time.

3. Concurrent computations, which do not demand

synchronization in time.

While parallel computation is completely synchronized, branching

computation is not completely synchronized because separate

branches acquire their own time and become synchronized only in

interactions.

Classical models of computation, such as the classical Turing

machine or partial recursive functions, perform only sequential

computations. Models that appeared later, such as Turing

machines with several heads and tapes or cellular automata,

provide means for parallel computations. There are also various

models for concurrent computations, according to [22].

In this context, the most advanced device model is grid

automaton, while the most advanced operational model, which

also is a process model, is the EAP (event-action-process) model.

All these models form three classes:

3.1 Device models (Petri nets, Kahn process networks,

dataflow process networks, discrete event simulators, grid

automata, the Linda model and the Actors model).

3.2 Operational models (ACP (algebra of communicating

processes), VCR (extended view-centric reasoning),

EVCR (view-centric reasoning) and ESP (event-signal-

process) model).

3.3 Process models (CSP (communicating sequential

processes) model and CCS (composite current source

delay) model.

2.4 Representational taxonomy

With respect to the data representation on which computations are

performed, there are following types of computation:

1. Discrete computations, which include interval computations.

2. Continuous computations, which include fuzzy continuous

computations.

3. Hybrid/mixed computations, which include discrete and

continuous processes.

Digital computing devices and the majority of computational

models, such as finite automata, Turing machines, recursive

functions, inductive Turing machines, and cellular automata,

perform discrete computations.

Examples of continuous computations are given by abstract

models, such as general dynamical systems [23] and hybrid

systems [24], and special computing devices, such as the

differential analyzer [25][26].

Hybrid/Mixed computations include piecewise continuous

computations, combining both discrete computation and

continuous computation. Examples of mixed computations are

given by neural networks [27], finite dimensional machines and

general machines of [28].

It is possible to refine the representational taxonomy in the

following way, obtaining three additional classifications.

2.5 Data-based taxonomy

With respect to the data and the domain of computation, the

following possibility exist:

1. The domain of computation is discrete and data are finite.

For instance, data are words in some alphabet.

2. The domain of computation is discrete but data are infinite.

For instance, data are ω-words in some alphabet. This

includes interval computations because real numbers

traditionally are represented as ω-words.

3. The domain of computation is continuous.

2.6 Operational taxonomy

With respect to operations in computation, the following

taxonomy can be found:

1. Operations in computation are discrete and they transform

discrete data elements. For instance, addition or

multiplication of whole numbers.

2. Operations in computation are discrete but they transform

(operate with) continuous sets. For instance, addition or

multiplication of all real numbers or of real functions.

3. Operations in computation are continuous. For instance,

integration of real functions.

2.7 Process-oriented taxonomy

1. The process of computation is discrete, i.e. it consists of

separate steps in the discrete domain, and it transforms

discrete data elements. For instance, computation of a Turing

machine or a finite automaton.

2. The process of computation is discrete but it employs

continuous operations. An example is given by analogue

computations [25][26] as quoted in [4] p. 122.

3. The process of computation is continuous but it employs

discrete operations. For instance, computation of a limit

Turing machine [4] p. 140.

2.8 Computation levels taxonomy

In [6] three generality levels of computations are introduced:

1. At the top and most abstract/general level, computation is

perceived as any transformation of information and/or

information representation.

2. At the middle level, where computation is distinguished as a

discretized process of transformation of information and/or

information representation.

3. At the bottom, least general level, computation is defined as

a discretized process of symbolic transformation of

information and/or symbolic information representation.

There are also spatial levels or scales of computations. Even

though the highest levels subsume the lower ones, computation is

performed/interpreted at a given level, as follows:

1. Macro-level includes computations performed by mechanical

calculators as well as electromechanical devices.

2. Micro-level includes computations performed by integrated

circuits.

3. Nano-level includes computations performed by fundamental

parts that are not bigger than a few nano meters.

4. Molecular level includes computations performed by

molecules.

5. Quantum level includes computations performed by atoms

and subatomic particles.

At present there are no commercially available nano-computers,

molecular or quantum computers, but they are being developed.

3. STRUCTURAL FRAMEWORK AND

ARCHITECTURE OF COMPUTATION

The earliest idea of computation was application of an algorithm.

The Turing machine model of computation is equivalent to an

algorithm. Thus, the first and most commonly encountered

computational structure is the computational dyad [29]

(computation, algorithm). The computational dyad reflects the

existing duality between computations and algorithms. In 1971

Dijkstra in his A Short Introduction to the Art of Programming

[30] defined an algorithm as a static description of computation,

which is a dynamic state sequence induced in a machine by the

algorithm. Later a more systemic explication of the duality

between computations and algorithms was elaborated. Namely,

computation is a process of information transformation, which is

organized and controlled by an algorithm, while an algorithm is a

system of rules for a computation [4]. In this context, an algorithm

is a compressed informational/structural representation of a

process. A computer program is an algorithm written in

(represented by) a programming language.

Thus, an algorithm is an abstract structure and it is possible to

realize the same algorithm as different programs (in different

programming languages). It is important to understand the

difference between algorithm and its representation or

embodiment. An algorithm is an abstract structure, which can be

represented in a multiplicity of ways: as a computer program, a

control schema, a graph, a system of cell states in the memory of a

computer, a mathematical system, such as an abstract finite

automaton, etc. Interestingly, many people think that neural

networks perform computations without algorithms. However,

this is not true as neural networks algorithms have representations

even though very different from traditional representations of

algorithms as systems of rules/instructions. The neural networks

algorithms are represented by neuron weights and connections

between neurons. This is similar to hardware

representation/realization of algorithms in computers (analog

computing).

The computational dyad is a simplification and incomplete

because there is always a system that uses algorithms to organize

and control computation. This observation suggests that the

computational dyad has to be extended to the triangular basic

computational triad (algorithm, computation, device/agent) that

reflects that specifies computation is performed by a device, such

as a computer, or by an agent, such as a human being or bacteria.

Functioning of the device or work of the agent that/who performs

computation is directed or controlled by an algorithm embodied in

a program, plan, scenario or hardware. Consequently, the process

of computation is also directed/controlled by an algorithm.

Note that the computing device can be either a physical device,

such as a computer, or an abstract device, such as a Turing

machine, or a programmed (virtual or simulated) device when a

program simulates some physical or abstract device. For instance,

neural networks and Turing machines are usually simulated by

programs on conventional computers. A Java virtual machine can

be run on different operating systems and is processor- and

operating system- independent. Besides, with respect to

architecture, it can be an embracing device, in which computation

is embodied and exists as a process, or an external device, which

organize and control computation as an external process.

The basic computational triad reflects the structure of the world

represented by the existential triad [17].

4. NATURAL COMPUTATION/

COMPUTING NATURE

In previous sections we presented concept of computation and its

models and developed computational taxonomies in the context of

information processing. Adopting the approach of structural

realism we provided structural framework of computation based

on Burgin’s existential triad (physical, structural, mental/

cognitive) of the world [17]. We have chosen triadic structures as

fundamental elements in our study of structures, based on the

classical triad of Plato – (matter, structure, mind), Peirce's triad of

(object, sign, interpretant) and Poppers structural triad (physical

world, knowledge, mentality).

In what follows, we will concentrate on the first two elements of

the existential triad – physical and structural in their cognitive

aspect, both of them addressed through the idea of computing

nature (natural computing), [7] [31].

According to the Handbook of Natural Computing [32] natural

computing is “the field of research that investigates both human-

designed computing inspired by nature and computing taking

place in nature.” In particular, it addresses: intrinsic computation

performed by natural materials and computational nature of

processes taking place in nature. Natural computing comprises,

among others, areas of cellular automata and neural computation,

evolutionary computation, molecular, quantum and organic

computing, biocomputing, nature-inspired algorithms.

Knowledge in natural computing is generated bi-directionally,

through the interaction between computer science and natural

sciences. As the natural sciences are promptly absorbing concepts,

tools and methodologies of information processing, computer

science on its side is broadening the notion of computation,

recognizing information processing found in nature as special type

of computation – intrinsic, natural computation. [33] [34][35][36]

This development in understanding of computation led Denning to

argue that computer science today is a natural science [37] [38].

In his “Simulating Physics with Computers” Feynman argued that

the specific forms of computation are best carried out by the

physical substrate we are trying to describe [39]. Even though

DNA computation can be emulated in silico, the efficiency of

computing with DNA substrate instead of its digital representation

is higher by many orders of magnitude [40]. Similar view of

physical (material) computation is presented in [41] where it is

argued for clear benefits of intrinsic (material) computation, as in

the Brook’s famous observation that “the world is its own best

model” [42]. Cooper addresses the question of the relationship

between abstract mathematical models and physical computation

that underlies every real-world computation, suggesting that we

are under the spell of the “mathematician bias” and arguing for

the return to embodied computation [43] [44]–[46]showing how

nature can help us to compute.

4.1 Intrinsic vs. designed computation

We are used to quick increase of computational power, memory

and usability of our computers, but the limit of miniaturization is

approaching as we are getting close to quantum dimensions of

hardware components. We are also facing the explosive increase

in the amounts of data, “big data” and the emergence of the

“internet of things” where computers are becoming an integral

part of practically all devices and indeed of our physical

environment. All of this poses huge demands on effective

computation techniques. Ever since the time of Turing, one of the

ideals was intelligent computing, which would besides mechanical

symbol manipulation include even intelligent problem solving.

That would help us manage complexity and vast amounts of data

that have to be processed, often in real time. In that direction

currently, there is a development of cognitive computing [47]–

[50] aimed towards human-level abilities of machines that

process/organize/ and even understand information.

At the same time the development of computational models of

human brain has for a goal to reveal the exact mechanisms of

human brain function (https://www.humanbrainproject.eu) that

will help us understand not only how humans actually perform

information processing when they follow an algorithm, but also

how humans create algorithms or models in a recursive cascades

of self-reflective computational processes from physical substrate

to information/patterns to mental states through cognitive

computing. Those new developments can be seen as a part of the

research within the field of natural computing, where natural

system performing computation is embodied and embedded

human brain.

The new understanding of computation as complex distributed

concurrent computational system of systems with inspiration in

natural information processing allows among others learning

about nondeterministic complex computational systems, such as

living organisms, with self-* properties (self-organization, self-

configuration, self-optimization, self-healing, self-protection, self-

explanation, and self-awareness). Natural computation has a

potential to provide a basis for a unified understanding of

phenomena of embodied cognition, intelligence and knowledge

generation. [51][47]

Recently, a focus issue of the journal Chaos was dedicated to the

intrinsic and designed computation under the title “Information

Processing in Dynamical Systems—Beyond the Digital

Hegemony” addressing challenges of intrinsic computing in

dynamical systems as complementary to designed computing in

digital systems. [36]

This relates to the view of a brain as a dynamical system

processing information (computing) at different levels of

organization – from molecular/electro-chemical, cellular

processes (DNA, protein networks), through neural circuits,

cortical through columns (morphologically distinct regions of the

brain processing and exchanging information), and finally through

the level of whole-brain information integration that is considered

to provide the function of consciousness, [52]. Cellular and

whole-brain levels of computation correspond to the cognition

level of cells and the brain. In a biological sense, cognition is the

property of an autopoietic system, with self-production, self-

organisation and closure and in structural coupling with the

environment. [53]

At this point, there are several avenues of the future development

of computing both in the setting of physical devices and of

theoretical models. Natural computation promises new and

broader ways of understanding of computational processes that

can be found intrinsically in various physical systems. In

connection to that, the structure and its particular case -

architecture of computational processes becomes increasingly

important, especially in the case of cognitive computing.

4.2 Levels of physical organization in natural

computing as natural information processing

In the section on Taxonomy of computation, under the heading

Hierarchy of computation levels, we mentioned levels and scales

of physical organization at which computation is performed in

designed computation. In the case of cognitive computing, the

following levels of designed, nature-inspired computation are

distinguished: synapses, neurons, microcircuits (modeling brains

cortical columns), long range interconnections (modeling axons

function) and the whole-brain level integration of processes,

representing non-von-Neumann architecture:

“overarching cognitive computing architecture is an on-chip

network of light-weight cores, creating a single integrated system

of hardware and software. This architecture represents a critical

shift away from traditional von Neumann computing to a

potentially more power-efficient architecture that has no set

programming, integrates memory with processor, and mimics the

brain’s event-driven, distributed and parallel processing.”

http://www-03.ibm.com/press/us/en/pressrelease/35251.wss

Mikkillineni [15], [16] describes cognitive architectures based on

his Distributed Intelligent Computing Element (DIME)

computing model, consisting of a “recursive managed distributed

computing network, which can be viewed as an interconnected

group of such specialized Oracle machines” that “provides the

architectural resiliency, which is often associated with cellular

organisms, through auto-failover; auto-scaling; live-migration;

and end-to-end transaction security assurance in a distributed

system.” Mikkillineni argues that “the self-identity and self-

management processes of a DIME network” add the elements of

cognition into Turing machine type computing. This approach

extends Burgin’s view of computing consisting of hardware,

software and infoware [4] with one more architectural layer

corresponding to cognitive function related to knowledge.

5. CONCLUSIONS AND OPEN

QUESTIONS

Present account suggests that the concept of computation as

information processing develops together with the constantly

increasing scientific knowledge and tools of analysis [54]. We

present the structural framework of information processing and

computation starting with existential triadic relationships between

(physical, structural, mental/cognitive) and taxonomies of

numerous aspects of computation that follow this basic existential

layered architecture that is paralleled by physico-chemical,

chemo-biological and bio-cognitive levels of information

processing [55]. As there is no information without (physical)

representation [18], the dynamics of information is implemented

on different levels of granularity by different physical processes,

including the level of computation performed by computing

machines (designed computation), as well as by living organisms

(cognitive computation).

We highlight several topics of importance for the development of

new understanding of computation and its role: natural

computation, interactivity as fundamental for computational

modeling of concurrent information processing systems such as

living organisms and their networks, and the new developments in

modeling needed to support this generalized framework for

cognitive architectures [4][16]. In such a way, we achieve better

understanding of computation as information processing on

different levels.

There are still many open problems related to the nature of

information and computation, as well as to their relationships.

How is information dynamics represented in computational

systems, in machines, as well as in living organisms? Are

computers capable of processing only data or information and

knowledge as well, as recent work of Mikkillineni suggests? What

can we know of computational processes in machines and living

organisms and how these processes are related to the

computational architectures? What can we learn from natural

computational processes in cognitive systems that can be useful

for engineered information systems and knowledge management?

6. REFERENCES

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[5] P. Denning, “What is computation?: Editor’s

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