MACHINE BIOLOGICAL CLOCK

The Time Dimension in a Organic-Based Operating System

Mauro Marcelo Mattos

Computing Systems Department, FURB- University of Blumenau, R.Braz Wanka 238, Brazil

Blumenau, Santa Catarina, Brazil

Keywords: Organic Computing, Machine Biological Clock, Knowledge-based Operating System.

Abstract: A Knowledge-Based Operating System is an embodied, situated, adaptive and autonomic system based on

knowledge abstraction which has identity and intelligent behavior when executed. We have identified three

dimensions over which such a new operating system paradigm has to be based: (i) physical dimension, (ii)

logical dimension, and (iii) temporal dimension. The physical dimension describes the physical components

and their structural relationship. The logical dimension describes the functional characteristics of each

physical component and the time dimension is provided in order to enable the entire system to perceive the

time flow – as a biological clock in human beings. This work presents the Machine Biological Clock

concept.

1 INTRODUCTION

“Twenty years ago, on November 12, 1986, Fred

Turek, Dave Hudson, Joe Takahashi, and Gene

Block founded the Society for Research on

Biological Rhythms. Who would have imagined that

today we are analyzing the structural biology of

bacterial clock proteins? Or that we are performing

real-time measurements of rhythmic gene expression

within individual cells in a dish? Or that we are

delineating the neurobiology of a neurotransmitter

that underlies narcolepsy? And that’s just for

starters. Every other year, we have been gathering

at this meeting to announce, debate, and celebrate

these advances, in presentations that cut across.”

(Schwartz, 2006).

In his effort to describe time as a subjective

experience in terms of his philosophy of

Phenomenology, Husserl grapples with the

succession of perceived moments of conscious

existence by using the example of the melody to

illustrate his point. In a melody, each successive

note exists in time for a moment, and each moment

represents a new ‘now’ point. Each part of the

melody is related to its antecedent and to the notes

yet to come. Our apprehension of the melody cannot

be obtained outside the context of successive notes.

“Therefore, the perceiving of a melody is in fact a

temporally extended, gradually and continuously

unfolding act, which is constantly an act of

perceiving.” (Husserl, 1893-1917). This temporality

is a subjective construct. (Craft, 2000).

According to Stulp and Beetz (2002), agent-

based systems are solving more and more complex

tasks in increasingly challenging domains, the

systems themselves are becoming more complex

too, often compromising their adaptivity and

robustness.

In robotics projects, system designers cannot

foresee each situation, or all the possible outcomes

of an action. Therefore, hand-coding the controllers

is considered intractable and failure-prone task.

This situation is a reality in general purpose

computational systems. According to Hayes-Roth

(2006), “the best systems of our times have been

hand-crafted by great engineers. These system

makers have analyzed the task environments,

knowledge requirements, and reasoning skills

necessary for successful applications. This approach

can work for any well-defined and sufficiently

narrow task. But, if the system fails, the engineers

would diagnose and debug the errors. They would

determine what knowledge to add or modify, how to

program it, and how to modify and rebalance the

pre-existing programs to accommodate the new

performance without harming the parts that already

worked well. Automation in adaptation, learning,

and knowledge acquisition was very limited – a tiny

fraction of the overall knowledge required, which

the engineers mostly prepared manually. We have

Marcelo Mattos M. (2008).

MACHINE BIOLOGICAL CLOCK - The Time Dimension in a Organic-Based Operating System.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 76-83

DOI: 10.5220/0001046800760083

 SciTePress

not yet figured out how to make the systems

responsible for their own debugging and

improvement”.

A promising approach to solve this problem is to

provide agents with reflective capabilities. Agents

that can reflect on the effects and expected

performance of their actions are more aware and

knowledgeable of their capabilities and

shortcomings. This is called “action awareness”

(Stulp and Beetz, 2002).

Another approach is based on an “efficient

thought” concept (Hayes-Roth, 2006). This concept

lists eight steps that the most complex organizations,

in general, perform in parallel. This approach states

that the intelligent being (a) observes what’s

happening in the environment, (b) assesses the

situation for significant threats and opportunities, (c)

determines what changes would be desirable, (d)

generates possible plans to operate those changes,

(e) projects the likely outcomes of those plans, (f)

selects the best plan, and (f) communicates that plan

to key parties before implementing it. Throughout

the process, the intelligent being (g) validates and

improves its model.

The new research area that intends to solve some

of the problems pointed out is called Organic

Computing (OC). The main goal of OC is the

technical usage of principles observed in natural

systems. (Muller-Schloer, 2004).

In this paper, we introduce a computational

model for what we call “machine biological clock”

in order to make possible to build a really reflexive

environment where the system can perceive the time

flow. This concept, that has been ignored in previous

works is, in our point of view, the most important

concept that has to be considered if we plan to build

intelligent systems. Or, as said in Brachman (2002)

0, if “we want to transform them from systems that

simply react to inputs into systems that are truly, in a

word, cognitive. Most formal and intuitive

definitions tell us that cognition is about knowing.

Our image of a cognitive system, then, is one that

can indeed know things and act on that knowledge.

It can take explicit knowledge gleaned in a host of

ways and go beyond it to important implicit

knowledge, ranging from pure and simple logical

deduction to what we might call ‘plausible

reasoning’ ”.

The paper is organized as follows: a problem’s

contextualization in operating system area is

presented in section 2; a knowledge-based operating

system concept in section 3; an overview of the

biological clock concept in section 4; section 5

describes the time dimension in a KBOS system;

section 6 presents some related works and in the

conclusion section the final comments are presented

2 WHY A NEW CONCEPT?

In our point of view, three concepts contribute to

reduce our possibilities in building really intelligent

systems: (i) the multitasking concept, (ii) the

operator concept and, (iii) the program concept.

Traditional operating systems support the notion

of a hardware abstraction level in which each

application is supposed to possess its own processor

(and other resources). This situation and the fact

that, in general, all commercial operating systems

are based on a multitasking concept (introduced in

1964), contribute to the permanence of problems

identified 30 years ago (Linde, 1975). The problems

range from security to usability, including lack of

adequate behavior in fluctuating execution

conditions and user’s privacy (Brachman, 2002).

Today we are also faced by new demands like

pervasive computing and organic computing, where

self-adaptation and self-reconfiguring are the main

goals.

Besides that, there are two other concepts that

contribute to make things worse: (i) the operator

concept, and (ii) the program concept.

The operator function was necessary during the

first years of computing since computers were big

and difficult to use. Operators, at that time, were

responsible for turning the machine on/off, starting

programs, collecting reports, restarting programs and

so on. This scenario has changed as the computers

become smaller, cheaper and faster as they are

today. However, what was a real need in the past is

employed today as if there was no other way to

interact with computers. In fact, we are nowadays

operators – all of us using some kind of computer

(desktops, palmtops, and mobile phones). We are

trained today to learn how to pull virtual buttons the

same way the former operators were trained to pull

real buttons in real panels on those old mainframes.

This aspect has consequences and the program

concept is the main one. A program could be

thought as the programmer’s hands virtually

extended inside our machines. The programmer has

the knowledge about some specific domain and

knows how to establish the correct sequence of steps

in order to solve the problem. In this scenario, we

are users of such routines – in other words:

operators. Programs, within this context, are the way

through which programmers can implement their

procedural knowledge about the problem’s domain.

MACHINE BIOLOGICAL CLOCK - The Time Dimension in a Organic-based Operating System

This model does not enable the actual operating

system to acquire knowledge about what is

happening inside the machine. We believe that this

is one of those several sources of problems that we

experience today. So, we have proposed a new

model.

3 A KNOWLEDGE-BASED OS

The novel concept introduced in Mattos (2003) says

that a knowledge-based operating system is: “an

embodied, situated, adaptive and autonomic system

based on knowledge abstraction which has identity

and intelligent behavior when executed”.

The whole system is built inside a shell which

gives the endogenous characteristic. A hyper

dimensional world model (Mattos, 2005) enables the

entire system to perceive evolving and/or fluctuating

execution conditions (fig. 1).

Figure 1: Hyper-dimensional World Model.

3.1 The Dimensions

We have identified 3 dimensions over which such a

new operating system paradigm has to be based: (i)

Physical dimension, (ii) Logical dimension, and (iii)

Temporal dimension.

The physical dimension describes the physical

components and their structural relationship.

The logical dimension describes the functional

characteristics of each physical component. It is

called: physical context of a device. A state machine

describes the dynamic aspects of the component’s

behavior. Merging the entire physical context of all

physical devices described at the physical

dimension, we obtain the world’s physical context

(WPC). The logical dimension is provided by a

DEVS run-time environment (Mattos, 2005b).

The Time Dimension is provided in order to

enable the entire system to perceive the time flow –

as a biological clock in human beings.

3.2 Knowledge based OS

and Knowledge Acquisition

Some works (Samsonnet et al, 1982; Vilensky,

Arens and Chin, 1984; Chikayama, 1988; Genera

2003; Larner, 1990; Yokote, 1992; Li et al, 1995;

Patki, Raghunathan and Khurshid, 1997) have been

described as aiming to develop a complete

knowledge-based operating system. Other

approaches consists of applying IA techniques

through making kernel implants (Seltzer, Small and

Smith, 1995) in order to get better user interfaces in

traditional operating systems (Pasquale 1988;

Cockcroft,1995; Hernández, Vivancos and Botti

1998; Zomaya, Clements and Olariu, 1998; Kandel,

Zhang and Henne, 1998; Siraj, Bridges e Vaughn,

2001). However, all of them have failed into

achieving their objectives because the main concept

over which they should base their work has not been

clearly specified – “what” is the knowledge. This

aspect has transformed those supposed new

operating systems projects into traditional operating

systems architectures with many specialized libraries

over some multitasking platform.

Knowledge in this context is conceived as being

a set of logical-algebraic operational structures that

makes possible to organize the system functioning

according to interconnection laws and behavior

laws. It is well known that a significant obstacle to

the construction of knowledge-based systems is the

process of knowledge acquisition.

The key to this process is how we may

effectively acquire the knowledge that will be

implemented in the knowledge base. In an operating

system environment, this is not an easy task. It is

usually done by hooking the OS API calls and

recording logs for further analysis. This is a time and

resources consuming process. The main drawbacks

to this approach are: (i) the information gathering

process impacts the overall performance, influencing

other applications that aren't involved in the

application context being considered; (ii) this impact

on performance also interferes with the application

being considered; and (iii) this scenario is probably

different from that of where the application was

developed.

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Figure 2: (a) General framework (b) Proposed framework.

3.2.1 Learning Phase

Figure 2a shows the traditional application life-cycle

characterizing that the documentation produced

during the analysis and design, in general, is stored

in folders after the implementation phase is

concluded. The traditionals operating systems

usually receives only binary code to manage.

In our approach, the development process must

be oriented by the application dynamics. As a

prerequisite, a KBOS environment establishes that

each application developed must have (i) a state-

machine that describes all dynamic behaviour and

(ii) the source-code associated (Figure 2b).

Figure 3: Plans registering relative starting time for each

sub-plans or sub-states.

This is the input for our system to learn about the

application’s intentions and to generate the

executable code. When the KBOS runtime

environment receives a new application state-

machine, it starts a procedure to convert sub-states

and portions of source-code into execution plans.

Those execution plans are built in a parallel

functional decision trees format and constitutes the

executable code that KBOS recognizes.

The knowledge at OS level emerges from a

library of execution plans and from the system’s

experience in running those plans.

Figure 3 shows an example with two plans A and

B. Each plan has its own sub-state architecture

(according to their particular purpose). Each sub-

plan is implemented by a set of machine instructions

(executable code). The time needed to execute each

sub-state is determined during the software test

phases by the development team and delivered with

the software (including the state machines –

dynamic model of the software).

4 BIOLOGICAL CLOCK

According to Siegel (2006), living organisms

evolved an internal biological clock, called the

circadian rhythm, to help their bodies adapt

themselves to the daily cycle of day and night (light

and dark) as the Earth rotates every 24 hours. The

term 'circadian' comes from the Latin words for

about (circa) a day (diem). Circadian rhythms are

controlled by "clock genes" that carry the genetic

instructions to produce proteins. The levels of these

proteins rise and fall in rhythmic patterns. These

oscillating biochemical signals control various

functions, including when we sleep and rest, and

when we are awake and active. Circadian rhythms

also control body temperature, heart activity,

hormone secretion, blood pressure, oxygen

consumption, metabolism and many other functions.

A biological clock has three parts: a way to

receive light, temperature or other input from the

environment to set the clock; the clock itself, which

is a chemical timekeeping mechanism; and genes

that help the clock control the activity of other

genes. (Siegel, 2006)

In the last few decades, scientists have

discovered the genes responsible for running the

internal clocks: period (per), clock (clk), cycle (cyc),

timeless (tim), frequency (frq), double-time (dbt)

and others. Genes that control circadian rhythms

have been found in organisms ranging from people

to mice, fish, fruit flies, plants, molds and even

single-celled, blue-green algae known as

cyanobacteria. (Siegel, 2006)

The master circadian clock that regulates 24-hour

cycles throughout our bodies is found in a region

MACHINE BIOLOGICAL CLOCK - The Time Dimension in a Organic-based Operating System

called the suprachiasmatic nuclei (SCN) in the

hypothalamus of the brain. The SCN is made up of

two tiny clusters of several thousand nerve cells that

"tell the time" based on external cues, such as light

and darkness. The SCN regulates sleep, metabolism,

and hormone production (Siegel, 2006).

The SCN is believed to synchronize "local"

clocks in organs and tissues throughout the body,

either through hormones or changes in body

temperature. Gene-operated clocks independent

from the brain's master pacemaker have been found

in the liver, lung, testis, connective tissue and

muscle (Siegel, 2006).

4.1 Perception of Time

People and other animals are able to perceive the

duration of intervals between events, and the

accuracy of their perceptions can be assessed. In

situations in which there are many different time

intervals, these can be combined for the assessment

of the typical interval. Associative learning is

dependent upon time perception, and the

mechanisms of time perception involve an internal

clock (Church, 2006).

According to Craft (2000), “we take it for

granted that each experience takes place in a

continuum of time. In the course of the daily routine

one rarely, if ever, stops to notice as such the

passing of physical and psychological events while

they happen. But each passing moment, noticed or

unnoticed, is a successive instance of the ‘now’ that

marches on into a presumably infinite past that we

construct in memory. By extension, we anticipate a

succession of future ‘now’ moments that have yet to

come. In this process we meld our “experience of

heterogeneous events into a coherent sense of

persistence” (Flaherty, 1999 apud Craft, 2000).

It has been physically hypothesized that

biological processes and environmental factors

provide cues through which we construct our

perception of temporality. It has been suggested that

there is some sort of “biological clock” that

regulates our perception of time. This is not to be

confused with the more commonly known

“circadian” rhythms which have been usually

observed in most living things. Flowering plants,

bees and other animals demonstrate the existence of

internal or innate timing mechanisms in their

blossoming and movement patterns. Although much

attention has been given to these patterns in human

subjects, more notably in the isolation studies of

Aschoff and Weaver, such circadian clocks are not

likely to be involved in human time perception on

the fine scale of minutes and seconds (Campbell,

1990 apud Craft 2000).

The concept of ordered time, the fixed intervals

of minutes and seconds, and the synchronicity

between 10 minutes on the clock and the same

amount of time in lived duration is, at least partially,

a socialized phenomenon (Flaherty, 1991 apud

Craft, 2000).

5 THE TIME DIMENSION

In the current paradigm of computing systems, the

time is a variable that has to be explicitly read in

order to enable software entities to perceive the time

flow. It implies that if the program does not read that

variable, it will not be able to perceive the time flow.

In a KBOS system, the time flow is part of the

system and it is implemented in two phases: (i) by

establishing units of time flow perception and (ii) by

time-stamping the state machine that drives the

executable code (in KBOS run-time environment

executable code is implemented as parallel

functional decision trees) with that information.

The first phase makes possible the creation of a

kind of “machine biological clock” (MBC) concept

– a time unit that the whole system can perceive as

flowing and that is different from the time-slice

concept as adopted in multitasking based operating

systems today. The second phase enables parts of

code to perceive time without the need of explicitly

reading clock variables.

Figure 4 shows the situation in which one of the

sub-states of plan A at the second MBC unit

perceives that its sub-state is delayed in comparison

with the original situation presented by the figure 3.

Figure 4 also shows that hardware events (time ticks

from real-time clock and other hardware events from

mouse, hard disk and network card) demand specific

plans to be executed (fig. 5).

The MBC makes possible to introduce another

concept: the work capacity (WC) of a machine. This

concept is related to the amount of work a machine

can do in some BMC and introduces the capacity of

a system to perceive if it is becoming overloaded or

not.

Figure 6a shows an example of a program that is

time dependent. In general, the program needs to

call the getTime API in order to discover the current

time and make some calculation to discover if it is

delayed, on time or ahead of time. Also, in general,

only some portions of the code running in a system

have to deal with such constraints so we have a mix

of code dealing with time and code that was not

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conceived to deal with time running together on the

same environment.

Figure 4: Plans A and B perceiving delays.

Figure 5: Plans A, B and C perceiving delays.

Figure 6b shows how a plan (in KBOS context)

should be developed: each procedure/function has to

explicitly declare sections where the time dimension

has to be considered as a functional requisite. During

the execution time of that plan, the system activates

the appropriated section (delayed, onTime or

aheadOfTime) according to the situation of that

plan. If the developer do not know what to do in

some situation, he can explicitly use an

IDoNotKnowWhatToDo clause and the KBOS run-

time will take appropriate actions.

The time dimension also makes possible to

introduce the notion of space concept. In other

words, if two different plans perceive that both are

delayed it is equivalent to say to each one that there

is someone else sharing resources within the same

MBC unit. It leads the logical path of some plan to

be changed to another path that implements the same

functionality but demands less resources. The self-

adaptive and self-reconfigurable characteristic of the

system is based on this facility.

Figure 6: (a) Explicit time (b) implicit time.

5.1 Identity and Intelligent Behaviour

A KBOS framework enables to introduce the

identity concept, which is resultant from the

embodiment, situatedness, adaptiveness and

autonomy characteristics. This leads to an

emergence concept. According to Muller-Schloer

(2004), emergence is defined as a property of a total

system which cannot be derived from the simple

summation of properties of its constituent

subsystems. Emergent phenomena are characterized

by (i) the interaction of mostly large numbers of

individuals (ii) without central control with the result

of (iii) a system behavior, which has not been

explicitly “programmed” into the individuals.

In this sense, the set of characteristics enables

the system to perceive, in an individualized manner,

a set of events occurring in some instant of time.

Thus, the intelligent behavior emerges from the

previous characteristics plus the relationship

between the system and the surrounding

environment

MACHINE BIOLOGICAL CLOCK - The Time Dimension in a Organic-based Operating System

6 RELATED WORK

Stulp and Beetz (2006), proposed a novel

computational model for the acquisition and

application of action awareness, showing that it can

be obtained by learning predictive action models

from observed experience and also demonstrating

how action awareness can be used to optimize,

transform and coordinate underspecified plans with

highly parametrizable actions in the context of

robotic soccer. The system works in two moments:

(a) idle time when the agent learns prediction

models from the actions in the action library and, (b)

during operation time, when action chains are

generated.

Tannenbaum (2007) argues that self-awareness

means learned behaviors that emerge in organisms

whose brains have a sufficiently integrated, complex

ability for associative learning and memory.

Continual sensory input of information related to the

organism causes its brain to learn its (the

organism’s) physical characteristics, and produce

neural pathways, which come to be reinforced,, so

that the organism starts recognizing, several features

associated to each reinforced pathway. The self-

image characteristic provides a mechanistic basis for

the rise of the concept of emergency of behavior

that, on its turn, is connected to the concepts of self-

awareness and self-recognition. On the basis of all

that process there is the notion of time perception.

7 CONCLUSIONS AND FURTHER

WORK

We have briefly given an overview of an

endogenous self-adaptive and self-reconfigurable

approach to operating system design and introduced

the MBC concept. The main aspect to be pointed out

is that there is no separation between what is known

as operating system and what is known as

application programs in today’s paradigm.

In a KBOS environment all the executable code

takes part into the system and all the code has the

ability to perceive the time flow. This perception

enables the whole code to execute self-adaptation

without explicitly demanding routines.

REFERENCES

R.J.Brachman. Systems That Know What They’re Doing.

IEEE- Intelligent Systems. Nov/Dec 2002. pp.67-71.

T.Chikayama. Overview of the Parallel Inference Machine

Operating system (PIMOS). Proc. Of the Intl.

Conference of Fifth Generation Computer Systems.

Pp. 230-235. 1988.

R.M.Church. Time Perception. Encyclopedia of Cognitive

Science. John Wiley & Sons, Ltd. 2006.

A.Cockcroft. New release of the SE Performance Toolkit.

March 1995. Available in

www.sun.com/960301/columns/adrian/ column7.html,

March 1995.

B.Craft. Twitch of the Snooze Button: Time Perception

and Cognition in Humans. Submitted in partial

fulfillment of the requirements for HCI 450, DePaul

University. January 31, 2000.

Genera Concepts: The Best Software Environment

Available. March 2003. Available in http://kogs-

www.informatik.uni-hamburg.de/~MOELLER/

symbolics-info/GENERA/genera.html.

R.Hayes-Roth. Puppetry vs. Creationism: Why AI Must

Cross the Chasm. IEEE Intelligent Systems.

September/October 2006; 21(5):7-9.

L.Hernández,E.Vivancos,V.Botti. Intelligent Scheduling

of Production Systems in a Real-Time Architecture.

IBERAMIA’98,1998,p429-438.

A.Kandel,Y.Zhang, M.Henne. On use of fuzzy logic

technology in operating systems. Fuzzy Sets and

Systems 99, Elsevier Science, pp 241-251, 1998.

Larner,D.L. A Distributed, Operating System Based,

Blackboard Architecture for Real-Time Control.

CACM. 1990.

R.Linde. Operating Systems Penetration. AFIPS Conf.

Proceedings, Vol 44, 1975.

M.M.Mattos. Main Concepts to Build Knowledge-Based

Operating Systems. Doctoral thesis. UFSC-

Universidade Federal de Santa Catarina, Brasil,

Novembro, 2003. (In Portuguese)

M.M.Mattos. Next Generation of Operating Systems

Design Based on Knowledge Abstraction. In:

Proceedings of the IADIS International Conference on

Applied Computing. Algarve, Portugal. 2005.

M.M.Mattos. KBOS Run-Time Environment Based on

DEVS Formalism. In: Proceedings of the IADIS -

International Conference on Applied Computing.

Algarve, Portugal. 2005.

C.Müller-Schloer. Organic computing: on the feasibility of

controlled emergence. In Proceedings of the 2nd

IEEE/ACM/IFIP international Conference on

Hardware/Software Codesign and System Synthesis

(Stockholm, Sweden, September 08 - 10, 2004).

CODES+ISSS '04. ACM Press, New York, NY, 2-5.

DOI= http://doi.acm.org/10.1145/1016720.1016724.

A.B.Patki , G.V.Raghunathan ,A.Khurshid. FUZOS—

Fuzzy Operating System support for Information

Technology. Proceedings of Second On-line World

Conference On Soft Computing In Engineering,

Design And Manufacturing. Cranfield University, UK,

June 1997.

J.Pasquale. Using Expert Systems to Manage Distributed

Computer Systems. IEEE Network. Set.1988.

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

J.P.Sansonnet,M.Castan,C.Percebois,D.Botella,J.Perez.

Direct Execution of LISP on a List Directed

Architecture. Proceedings of ASPLOS. Palo Alto,

Califórnia, March 1982, pp. 132-139.

M.Seltzer,C.Small,K.Smith. The Case for Extensible

Operating Systems. Harward Computer Center for

Research in computing Technology -Technical Report

TR-16-95 Depto. Of Computer Science, Harward

University. 1995.

L.J. Siegel. The Time of Our Lives. Learn.Genetics –

Genetic Science Learning Center. The University of

Utah. In: learn.genetics.utah.edu/features/clockgenes.

2006.

A.Siraj.,S.Bridges,R.Vaughn. Fuzzy Cognitive Maps for

Decision Support in Intrusion Detection Systems.2001.

Available in http://www.sc.msstate.edu/~security/iids/

publications/nafips_ifsa_2001.htm.

F. Stulp and M. Beetz. Action awareness – enabling

agents to optimize, transform, and coordinate plans.

In Proceedings of the Fifth International Joint

Conference Autonomous Agents and Multiagent

Systems (AAMAS), 2006.

W. J. Schwartz. President’s Welcome. In Proceedings of

the Tenth Meeting of Society for Research on

Biological Rhythms, Sandestin, FL. May 21–25, 2006.

E.Tannenbaum. Speculations on the emergence of self-

awareness in big-brained organisms. Jun 2007.

Available in http://eprintweb.org/S/article/q-

bio/0701017.

R.Vilensky,Y.Arens,D.Chin. Talking to Unix in English:

An Overview of UC. CACM 17,6,pp.574-593, Junho

1984.

Y.Yokote. The Apertos Reflective Operating System: The

Concept and Its Implementation. Proceedings of

OOPSLA’92, ACM Sigplan Notices, v. 27, pages

414–434, 1992.

A.Zomaya,M.Clements, S.Olariu. A Framework for

Reinforcement-Based Scheduling in Parallel

Processor Systems. IEEE Transactions on Parallel and

Distributed Systems. V9, N3,p249-260,Mar 1998.

MACHINE BIOLOGICAL CLOCK - The Time Dimension in a Organic-based Operating System