Context

Time-Triggered switched Ethernet networks are increasing in size and complexity as they are planning to be deployed in large industrial networks such as mega-factories, and projected to be used in the future in smart cities. Traffic in time-triggered networks follows an offline schedule designed before hand that contains the transmission times of all the time-triggered frames through the links in the network. However, synthesizing this schedule means a major challenge when adapting time-triggered network in a larger scale. Schedule synthesis is a well known NP-complete problem with complexity driven by the network size and the number of frames it contains. State-of-the-art schedulers have been capable of synthesizing such schedules in a reasonable amount of time, but, they start to present scalability issues and are not able to cope with the size and complexity introduced by future applications.

In addition, applications are starting to require wireless capabilities in some areas of its networks, introducing a mixture of wired and wireless communication in the same network. Wireless communication also increases the complexity in schedule synthesis as the difference in transmission speeds between wired and wireless links is too significant to apply typical complexity reduction techniques, such as raster [1].

Research Goals

The goal of this research is to provide a new approach that is able to overcome the scalability issues of current schedule synthesis approaches before the deployment of the network. In general, a good strategy in engineering to cope with large problems is to use divide and conquer approach. In the case of schedule synthesis, we divide the schedule in smaller schedules, called segments, in which every segment will be a non-overlapping time interval of the complete schedule. Once all segments are defined, we allocate as many frames as we can using a state-of-the-art scheduler and concatenate them to obtain the final schedule. In our case, we apply Satisfiability Modulo Theories (SMT) solvers [3], that are able to find the satisfiability of a set of constraints and, if exists, provide an example of such satisfiability, in our case, frames allocated in a segment. However, some of the frames are dependent between them, and present inter-segment constraint that go beyond single segments. These inter-segment constraints bring a challenge to our approach as each segment is scheduled independently and there are no mechanism to satisfy constraints outside the segment being scheduled. To overcome this limitation, we release the SMT solver of such constraints, and handle them in our algorithms with the selection of frames and the addition of some extra constraints in each segment in order for the inter-segment constraints to be satisfied.

Results Obtained

We evaluated our approach using the topology of a network deployed to provide free Internet to a large touristic area and creating synthetic time-triggered traffic. This network, that we call Actual, consist in 81 end systems that exchange information between them through 44 switches and 248 links. Then, we created two new larger networks, Large, that consists is 5 times bigger than the Actual network, and Very Large, which is 11 times bigger. We provided between 5.000 and 50.000 synthetic frames to these networks and synthesize its schedules with our approach using the SMT solver Yices 2.4 [2] to allocate frames into the segments.

For the Actual size network we can schedule 5.000 frames in one minute and up to 50.000 frames in less than one hour. This is a huge performance and scalability improvement, as state of-the-art synthesizers could only schedule up to 1.000 frames in 20 minutes for smaller networks that the used in our evaluation. The synthesis time for Large and Very Large networks increases due to its larger complexity, but the synthesizer is still able to find valid schedules. It is important to note that the increase in synthesis time for networks with more frames is almost linear instead of exponential. This allows us to, if it is needed, to schedule networks with even more frames without experiencing scalability issues, which was the main problem of previous approaches.

Next Steps

Instead of keep pursuing an increase of performance in the schedule synthesis, we would like to abstract and automatize the process of constraint selection for the segments. In our research, we found that the selection of constraints, the number and how to add extra constraints in every segment has a great impact in the performance of the synthesizer. We would like to automatize this problem with the use of machine learning to find better segments and constraints division. This may allow us to use our approach to solve scheduling problem outside Time-Triggered networks if they are formulated as SMT constrained problems. It also have a good impact in our performance, as it could allow us to find segments that can be scheduled in parallel.

References

[1] Mok, A. and Wang, W., Window Constrained Real-Time Periodic Task Scheduling, in the IEEE 22nd Proceedings in Real-Time Systems Symposium (RTSS), 2001.

[2] B. Dutertre, Yices 2.2, in the Springer Computer Aided Verification, 2014. [3] Ranise, S., and Tinelli, C. Satisfiability modulo theories. Trends and Controversies in the IEEE Intelligent Systems Magazine, 2006.

[3] Ranise, S., and Tinelli, C. Satisfiability modulo theories. Trends and Controversies in the IEEE Intelligent Systems Magazine, 2006.

Abstract

The automotive domain has witnessed a  tremendous growth in the amount of software deployed in the cars. The car no longer contains mechanical components only but more and more functionality is controlled by embedded systems instead. Due to this, the field is a multi-disciplinary one involving engineers from mechanical, electrical, electronics and software disciplines. However, this is now changing as the problems being addressed in the domain are becoming more complex. Problems are now societal such as smart cities or green mobility. To solve such complex problems requires going beyond multi-disciplinarity and towards trans-disciplinarity. This involves including stakeholders that represents the users and also the societal interests. This paper discusses opportunities and challenges for trans-disciplinarity in the automotive domain. 

1. Introduction

In [8], multi-disciplinarity is characterized by research involving  complex problems that require researchers from several disciplines to coordinate to solve the problem.  In  multi-disciplinary research, theories and models from the different fields are brought together and each field only solves a piece of the bigger problem.  The different disciplines merely complement each other usually in a sequential or juxtaposing manner, but no new knowledge is formed between them [8,10].  Trans-disciplinarity on the other hand, goes beyond just several disciplines collaborating to solve a problem. With trans-disciplinary research the focus is to synthesize and unify knowledge from the different disciplines in order to come up with  solutions for complex problems [11].  The models and theories from the different disciplines can be altered and improved  since  the major aim of the research is to solve a  practical problem. 

Over the past 20 years in the automotive domain, there has been a rise in the amount of embedded systems that are deployed in the car [2]. An embedded system is a system whose critical function is not computational but which is controlled with by a computer embedded in it [12]. A current modern car contains approximately 10 million lines of code in total distributed over about 100 Electronic Control Units (ECUs) [3], which are the physical devices hosting the software part of embedded systems. Examples of embedded systems in a car are adaptive cruise control (which automatically controls braking and acceleration of the car), brake assist system, airbag control systems and navigation systems.

The development of such embedded systems has made the automotive domain multi-disciplinary in nature due to the different disciplines that are involved in making these systems. For example, a typical embedded system needs to be worked on by mechanical engineers, electronic engineers, electrical and software engineers. All these engineers bring their expertise to create the final system. This kind of development is known as Systems Engineering [13].

Recently, this paradigm of embedded systems in the automotive domain is shifting towards more complex problems for example autonomous driving. This means that using only engineering knowledge from the different disciplines is no longer enough and this field needs more appropriate research methods. Since trans-disciplinary research focuses more on problem solving rather than specific field enhancements, it is a better suite for the complex problems the automotive industry is now tackling [5]. In this paper, we will point on the opportunities for trans-disciplinary research and the challenges in the automotive industry.

The rest of the paper is structured as follows. Section 2 gives a brief overview of the evolution from multi-disciplinarity to trans-disciplinarity, Section 3 discusses the opportunities and Section 4 discusses the challenges for trans-disciplinarity. The paper ends with Section 5 which gives a brief reflection and conclusion.

2. From Multi-disciplinarity to Trans-disciplinarity: Bigger and More Complex Problems
In their review of the past 30 years of autonomous driving, the researchers Klaus et al. [1] came up with a time line of how the automotive industry has evolved in terms of the systems being developed and also how the goals for these systems have evolved. The authors show that the focus is moving from purely technical goals such as vehicle dynamic stabilization which is more on how to improve the car itself technically to broader goals that involve the society such as automated and cooperative driving. This change in focus also coincides with the experience report on systems engineering in the automotive domain in [13]. This shows that the problems that are now being tacked with both industry and academia in the field of autonomous driving are leaving the bound of multi-disciplinary and becoming more trans-disciplinary. For example to be able to have an autonomous vehicle on the street, several things such as safety, ethics, legal issues and even urban planning need to be taken into account. This can only be achieved if researchers and practitioners from these different disciplines work together.

3. Opportunities for Trans-disciplinary research
This section outlines opportunities for both researchers and industry practitioners brought about by trans-disciplinarity.

3.1 Research Funding Opportunities
The rise of autonomous driving brings along some opportunities for researchers as well. For example there are now opportunities to apply for grants that support trans-disciplinary research [9]. Such grants are focused on solving societal problems more than scientific advancement of particular disciplines. For instance one societal problem is environmental pollution. For such a problem, autonomous vehicles can be seen as a solution to minimize waste caused by having so many cars produced and used. A more real example can be seen in the European Union Framework Program for Research and Innovation from 2014 to 2020 in which they have set aside funding to investigate three societal problems which are: 1. Smart, Green \& Integrated Transport, 
2. Secure, Clean \& Efficient Energy and 3. Climate Action, Environment, Resource Efficiency and Raw Materials [6].

3.2 Opportunity to Show Applicability of Research Results
Trans-disciplinary research can also be seen as an arena for researchers to show case the application of their research in different context. This is because for research focused on advancement of a particular discipline, the research usually ends at a proof of concept phase where theoretically or in very restricted environments, the advancements such as models and concepts are proved to work. Working in a trans-disciplinary research project is however a different story as researchers need to apply their knowledge to a complex problem that is also practical and in a practical environment. This facilitates more learning and synthesis of knowledge from various contexts.

4. Challenges of Trans-disciplinary research
This section describes the challenges of trans-disciplinary research in general and explains how they apply to projects in the automotive domain.

4.1 Coordinating large projects
Trans-disciplinary research typically involves very large projects [4]. This is because the project involves people from separate disciplines. Coordinating such projects is more complex as it requires the skills to be able to manage people from the different disciplines. It is also important to make sure that everyone in the project feels that they are important and needed and most of all feel that they are contributing to the final goal of the project. Depending on the project there might also be several goals and it is difficult for all the stakeholders to keep in mind the final goal. As in every large project, it is likely that the objectives of the different stakeholders might be conflicting. For instance for autonomous driving from the context of software engineers efficiency of the program might be an objective that is of importance but for safety engineers this may not be the case. Also research in mono-disciplinary projects typically has traditions that differ among disciplines. Bringing them together may lead to the conflicts between different traditions of conducting a project, presenting the results etc.

4.2 Communication problems
When different disciplines need to coordinate, communication problems usually arise. This is because the different disciplines have their own vocabulary that may mean something else in other disciplines. For trans-disciplinary research project this is also challenge. Researchers suggest that before the beginning of the project the different stakeholders involved need to meet and discuss the objectives of the project. Also during the course of the project regular contact in form of meetings or workshops is advised in order to always ensure that you are all on the same page. It is important to communicate the expected outcomes as early as possible. Expectations of all the different disciplines should also be put to the table early on.

4.3 Applied research vs. Fundamental research
Fundamental research or basic research refers to the type of research where scientists act out of curiosity and gather knowledge for the sake of knowledge without a specific problem in mind that this knowledge will solve [7]. On the other hand applied research takes a more practical approach where scientists seek knowledge that will solve a particular problem. 
As mentioned before, what differentiates trans-disciplinary research from other types of research is that it is mainly aimed at solving an identified practical problem. For research this usually translates to mostly applied research rather than fundamental or basic research. 
This means that in the end the contribution is not a new advancement in any particular field but rather unified knowledge gained and a practical problem solved. While publishing such results is not a problem as many publication venues accept such papers, for researchers, it can be hard to have an identity of belonging and contributing to a particular discipline (something which is expected in academia). This can also be reflected in how the universities hire researchers based on needs of a certain discipline.

5. Conclusion
Based on the discussion of the challenges and opportunities for trans-disciplinary research in the automotive domain, there are two ways in which trans-disciplinarity can advance in the automotive domain. One is that using trans-disciplinary research, the automotive industry as a whole can be advanced by solving more complex problems. It is therefore an opportunity for advancement that would not have been possible using mono-disciplined research approaches. On the other hand, applying the different research methods suggested to be suitable for trans-disciplinary research gives an opportunity for such methods to be used and evaluated in the automotive industry. This does not only benefit researchers, but also practitioners and the society in the end.

References

1. K. Bengler, K. Dietmayer, B. Farber, M. Maurer, C. Stiller, and H. Winner.Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent Transportation Systems Magazine, 6(4):6–22,2014

     1. From Bodies to Bodies

One fundamental aspect of Human-Robot Interactions is the role of the morphologies of both humans and machines. Basically, humans are naturalistically oriented towards the social interaction with other humans, as wrote Aristotle in his classic Politics: “Man is by nature a social animal; an individual who is unsocial naturally and not accidentally is either beneath our notice or more than human. Society is something that precedes the individual. Anyone who either cannot lead the common life or is so self-sufficient as not to need to, and therefore does not partake of society, is either a beast or a god”. Considering it as the long result of an  evolutionary process, we can find the several cognitive mechanisms make possible these processes (Adolphs, 2003; Bechtel, 2001; Frith & Frith, 2007; Lieberman, 2012). Some of them, like constantly face-looking patterns allow some biased, like pareidolia or the faces convey primal information for our social life, which make possible to see faces into toasts, rocks or forests (Kato & Mugitani, 2015; Liu et al., 2014).

The constant analysis of morphological aspects is related to mating (Jaffé & Moritz, 2010; Wade, 2010), fly-or-fight responses (Bubic, von Cramon, & Schubotz, 2010), social coordination (Lieberman, 2000) or emotional interaction(Casacuberta & Vallverdú, 2015). This affects primarily the visual (Cavanagh, 2011) and metacognitive processes related to it (Kirsh, 2005), but must be understood as a multidimensional processes which involves several senses.  Finally, there is also the influence of cultural values into basic informational sensory processes, as shows the cultural psychologist (Nisbet, 2003).

Taking into account that fact that human morphologies run a social role, and that affection or emotion are fundamental aspects of the eco-cognitive and social processes, I want to remark some important aspects fundamental to be taken into account during the design of good HRI systems and environments.

    2. Moral Morphologies as Social Prejudices or Cognitive Bias?

Although 19^th Century psychomorphologists or physiognomists like Cesare Lombroso were wrong about the causal relationship between face shape and (usually wrong) moral behaviour, the truth is that human beings tend to correlate some morphologies  with moral and/or emotional content (Mazzarello, 2011; Stepanova & Strube, 2009). Here, bad guys are usually dark, angry, with some deformity or extreme trait (big nose, big ears, small head,...), weird cinematic body movement,…like we can find in most of popular cinema and Walt Disney’s villains characters(Gould, 2008). Obviously there are not only biologically determined aspects related to this process, but the role of cultural values must not be undervalued:

            Beyond the debates between continuous and categorical models of human caption of emotions, the outstanding fact is that morphology affects how we define the emotional output or even main character of an agent (Martinez & Du, 2012). Therefore, the morphology of the robot is one among a long list of emotional affordances I’ve described elsewhere in previous research (Vallverdu & Trovato, 2016), but at the same time the morphology has an outstanding role because determines a long set of related characteristics of the agent.

       3. Emotional Morphologies for HRI

According to the previous data it is obvious that besides of considering the functional design of a robot, several socio-cognitive aspects related to their morphology must be taken into account: gender (Slepian, Weisbuch, Adams, & Ambady, 2011), related language semantics (Gendron, Lindquist, Barsalou, & Barrett, 2012), social context (Hertwig & Herzog, 2009; McHugh, McDonnell, O’Sullivan, & Newell, 2010), body gestures/cinematic (Castellano, Villalba, & Camurri, 2007), among a long list. It is very important for example, that most of previous studies  have been related to visual and linguistic HRI interactions, while others extremely important, like touch or olfactory have been almost neglected, basically due to the high complexity of these processes. These aspects are not only basic for a more deep relationship between humans and robots in classic domains (service, military, industrial, care), but also for new ones (like the taboo one of sexual robotics (Levy, 2007), surely one the niches with great expected revenues and implementation according current data on sexual surfing and related interests through the Web and Social Networks). As a conclusion of this section, I must to affirm that the study of the emotional affective aspects embedded into robot morphologies arises as a multidisciplinary research as well as a multidimensional process that goes beyond the basic description of size, shape, colour or texture, requiring more variables: temperature, cinematic speed, temporal flow and adjustment to a naturalistic emotional gestures dynamics, among other ones.

     4. The Challenge of Dynamically Augmented Morphologies: Transhumanism or Adaptable robotics.

There is a final idea to be discussed here: human agents are starting to modify severely their cognitive and bodily limits (up to date just as a repairing/prosthetic process or as fashionable gadgets) and this process will modify severely how the natural analysis of morphological phenomenology is performed. At the same time, we can find robots into the market with variable morphologies (combining biped walking with four-legged translation or even wheels; with adjustable body characteristics), something that can confuse the human interacting with the robot. While we do not have a clear control of current morphological aspects involved into HRI, a new set of challenges is in front of us.

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Casacuberta, D., & Vallverdú, J. (2015). Emotions and Social Evolution: A Computational Approach. Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics. http://doi.org/10.4018/978-1-4666-7278-9.ch004

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Extended abstract

Eco-cognitive computationalism sees computation as active in physical entities suitably transformed so that data can be encoded and decoded to obtain fruitful results. When physical computation is seen in the perspective of the ecology of cognition it is easy to understand Turing’s original ideas concerning the emergence of information, cognition, and computation in organic, inorganic, and artefactual agents. Turing’s speculations on how the so-called “unorganized brains” are transformed in organized “machineries” are very important. Brains are of course continuous systems that can be treated as discrete systems able to perform “discrete” computations, so that we can describe the possible states of these brains as a discrete set, with the motion occurring by jumping from one state to another. Turing clearly says: “The cortex of an infant is an unorganized machinery, which can be organized by suitable interference training. The organization might result in the modification of the machine into a universal machine or something like it. […] This picture of the cortex as an unorganized machinery is very satisfactory from the point of view of evolution and genetics” [1]. This intellectual perspective first of all clearly depicts the evolutionary emergence of information, meaning, and of the first rudimentary forms of cognition, as the result of a complex eco-cognitive interplay and simultaneous coevolution, in time, of the states of brain/mind, body, and external environment. At the same time it furnishes the conceptual framework able to show how thanks to an imitation of the above process the subsequent invention of the Universal Practical Computing Machine is achieved, as the externalization of computational capacities in those artefactual physical entities that compute for some human or artefactual agents: those computers that in this perspective offered by Turing I called “mimetic minds”.

Turing on the emergence of information, cognition, and computation in organic, inorganic, and artefactual agents. Aiming at building intelligent machines Turing first of all provides an analogy between human brains and computational machines. In [1] he maintains that “[...] the potentialities of human intelligence can only be realized if suitable education is provided”. The concept of unorganized machine is then introduced, and it is maintained that the infant human cortex is of this nature. The argumentation is indeed related to showing how such machines can be educated by means of “rewards and punishments”. Unorganized machines (and also paper machines) are listed among different kinds of existent machineries:

- (Universal) Logical Computing Machines (LCMs). A LCM is a kind of discrete machine Turing introduced in 1937 that has an infinite memory capacity obtained in the form of an infinite tape marked out into squares on each of which a symbol could be printed. The importance of this machine resorts to the fact that we do not need to have an infinity of different machines doing different jobs. A single one suffices: it is only necessary “to program” the universal machine to do these jobs.

- (Universal) Practical Computing Machines (PCMs). PCMs are machines that put their stored information in a form very different from the tape form. Given the fact that in LCMs the number of steps involved tends to be enormous because of the arrangement of the memory along the tape, in the case of PCMs “[...] by means of a system that is reminiscent of a telephone exchange it is made possible to obtain a piece of information almost immediately by ‘dialing’ the position of this informa- tion in the store” [1]. Turing adds that “nearly” all the PCMs under construction have the fundamental properties of the Universal Logical Computing Machines: “[...] given any job which could have be done on an LCM one can also do it on one of these digital computers” [1] so we can speak of Universal Practical computing Machines.

I will take advantage in my presentation of the concept of unorganized brain (and machine) to stress the historical/epistemological interest of Turing’s discoveries. Unorganized Machines are largely random in their constructions. Infant brains too can be seen as unorganized machines and are organized through education. Brains very nearly fall into this class [discrete controlling machinery – when it is natural to describe its possible states as a discrete set] and there seems every reason to believe that they could have been made to fall genuinely into it without any change in their essential properties. However, the property of being “discrete” is only an advantage for the theoretical investigator, and serves no evolutionary purpose, so we could not expect Nature to assist us by producing truly “discrete brains”. Education in human beings can model “education of machinery” “Mimicking education, we should hope to modify the machine until it could be relied on to produce definite reactions to certain commands”. A graduate has had interactions with other human beings for twenty years or more and at the end of this period “[...] a large number of standard routines will have been superimposed on the original pattern of his brain” [1].

Computing machine as the “externalization” of computational capacities in artefactual physical entities that compute for some human or artefactual agents. Research in distributed cognition established that we humans delegate cognitive (and epistemic, moral, etc.) roles to externalities and then tend to “adopt” and recapitulate what we have checked occurring outside, over there, after having manipulated – often with creative results – the external invented structured model. A simple example: it is relatively neurologically easy to perform an addition of numbers by depicting in our mind – thanks to that brain device that is called visual buffer – the images of that addition thought as it occurs concretely, with paper and pencil, taking advantage of external materials. Mind representations are also over there, in the environment, where mind has objectified itself in various semiotic structures that mimic and enhance its internal representations. Turing adds a new structure to this list of external objectified devices: an abstract tool, the (Universal) Logical Computing Machine (LCM), endowed with powerful mimetic properties. The creative “mind” is in itself extended and, so to say, both internal and external: the mind is semiotic because transcends the boundary of the individual and includes parts of that individual’s environment, and thus constitutively artificial. Turing’s LCM, which is an externalized device too, is able to mimic human cognitive operations that occur in that interplay between the internal mind and the external one. Indeed Turing already in 1950 maintains that, taking advantage of the existence of the LCM, digital computers (as external physical appropriate objects) can be constructed, and indeed have been constructed, and they can in fact mimic the actions of a human computer very closely. In the light of my perspective both (Universal) Logical Computing Machine (LCM) (the theoretical artifact) and (Universal) Practical Computing Machine (PCM) (the practical artifact) are mimetic minds because they are able to mimic the mind in a kind of universal way (wonderfully continuing the activity of the so-called “disembodiment of the mind” and of semiotic delegations to the external materiality our ancestors rudimentary started).

Computational mimesis of morphological aspects, mimetic bodies, simplexity. It is in the framework I have just described that we can limpidly see  – naturally extending Turing’s perspective - that the recent emphasis on the simplification of cognitive and motor tasks generated in organic agents by morphological aspects implies - in robotics ¬- the need not only of further computational mimesis “à la Turing” of the related performances - when possible - but also the construction of appropriate “mimetic bodies” able to render the accompanied computation simpler, according to a general appeal to the “simplexity” of animal embodied cognition.

References

1. Turing, A.M., Intelligent machinery [1948]. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, 5, pp. 3–23. Edinburgh University Press, Edinburgh (1969)

Type of the Paper (Proceeding)

Computing with Nature^†

Marcin J. Schroeder ^1,*

¹   Akita International University, Akita, Japan; mjs@aiu.ac.jp

*   Correspondence: mjs@aiu.ac.jp; Tel.: +81-18-886-5984

†  Presented at the Symposium on Morphological Computing and Cognitive Agency, is4si Summit at Gothenburg, June 12-16, 2017.

Published: date

Abstract: Natural and morphological forms of computing have diverse conceptualizations. This paper presents an alternative view on morphological computing based on a slightly generalized form of a Turing machine in which one-way action of head on tape is replaced by mutual interaction. This generalized (symmetric) Turing machine can serve as a component of a multi-level complex computing system in much closer analogy to living objects which tend to form systems of very high level of complexity (with levels starting at molecular level, through cellular one to organismal level, or possibly to the level of population or eco-system.  

Keywords: Morphological computation; Natural computation; Hierarchic information systems; Interactive computation; Dynamics of information;

1. Introduction

Unconventional forms of computation (i.e. computation essentially different from and not reducible to the computation described by the theoretical model of an a-machine introduced by Alan Turing in his famous 1936 paper [1]) generated emotions comparable with those in discussions of life after life. In both cases the central concept of heated disputes, computation or life, remains vague. Also, in both cases the source of strongest contention is in the mystery of consciousness, especially in the context of the question about the role of natural processes and mechanisms responsible for its phenomenal experience. Questions of the type “Is the Brain a Digital Computer?”[2] stimulated hot discussions in the 1990’s, but they are no more in the center of attention not because they were answered, but it is now not so clear what computer is and what exactly is the role of the brain in cognition.

Fortunately the time of definite answers to indefinite questions and their furious defense seems to be over. This is why so important are maybe less ambitious, but more clearly formulated research programs focused on problems which when answered can help in dealing with the ultimate questions.

One of the fields of this type of research and accompanying it philosophical reflection is a question about the relationship between computing and natural processes. We have to accept the need for rather intuitive understanding of the expression “natural processes” and “computing”, the concepts whose understanding is rather a goal of the study, not a point of departure. The noun “nature” and adjective “natural” are subjects of many controversies and attempts to answer them open entire field of inquiry, which due to the restricted format of the present short paper is excluded from consideration. The same applies to “computing” which more and more frequently is discussed in the context of “hypercomputing” [3] or “unconventional computing”. Thus, the idea of “natural computing” involves double difficulty, but this does not diminish its attractiveness for research and reflection. Sometimes, when computing is understood in conventional way (modeled by Turing machine or automaton) and the focus is on its implementation in living organisms the reference to natural computing seems perfectly justified [4,5].

Research on natural computing is already very diverse. Two main directions are in some sense leading in the opposite directions. One direction explores processes in nature, especially in living objects or their populations which can be used to implement conventional forms of computation or which exhibit behavior which can be interpreted as such computation. Advanced forms of such research have for instance objectives of the development of molecular or cellular robotics [6,7]. The other direction is to search for processes in nature which can provide examples of unconventional forms of computation, for instance reversible computation [8-10]. The directions are opposite in the sense that one assumes already existing conventional model of computation and looks up for their implementation in nature, the other explores nature in the search for new forms of computation.

Morphological computing (in its diverse ways of understanding) is at the cross-section of these two directions. Its diverse forms have in common interest in morphological characteristics of the computing systems. Thus, we can consider computing understood as transformation of acellular slime mould Physarum polycephalum within the wide spectrum of morphological patterns in response to changes in environment [4]. But the concept of morphological computation has consequences and applications in much broader context, for instance when human “extended or embodied cognition” is considered in its natural form of organismal morphology. It becomes increasingly clear that the attempts to understand cognition are hindered by the simplifying idealization expressed by the simile of a “brain in the vat”.

My own approach to naturalization of computation presented here is different, possibly more abstract and motivated rather by more general reflection on both natural processes and on computation without the assumption that either one has more primary status. Probably the closest affinity in my research interests is with the studies carried out by Kenichi Morita on reversible computing [10]. My original research questions were derived from the observations on similarities and differences between theoretical computing devices such as a Turing machine and actual physical systems studied in physical sciences [11]. Some of these questions were as follows:

1. Why conventional computation is irreversible, while processes of simple physical systems are always reversible? Irreversibility (breaking time-reversal symmetry) is coming with increased complexity and is manifested in systems far from equilibrium. If the Turing machine computing operates at the lowest level of complexity, why is it irreversible?
2. Reflection on implementations of computation in natural or physical systems is usually expressed in terms of causality. However, the concept of causality is absent in formalisms of physical theories. It is more a (doubtful) philosophical concept used in interpretation of physical theories or just a convenient expression to describe components of a system (“The revolution of Earth around Sun is caused by gravitational force of the mass of Sun” – the obvious physical nonsense as Earth is not revolving around Sun). The questioning of the cause as physical concept goes back at least to Bertrand Russells essay from 1917: “All philosophers, of every school, imagine that causation is one of the fundamental axioms or postulates of science, yet, oddly enough, in advanced sciences such as gravitational astronomy, the word ‘cause’ never occurs…”[12]. Naturalized computation should be described in terms of interaction not cause.
3. More careful reflection on the way Turing derived the description of his a-machine shows that the description involves some arbitrary elements probably coming from the original vision of the “human computer” performing calculation. There is no reason to insist that the entire content of the instructions has to be located in one central place with primary control function (head) and that the head has to have more active role in the computation than the tape.
4. Natural systems are typically of a complex hierarchical architecture. Natural computation should be generalized to make multilevel simultaneous computation possible.

This paper is devoted to the study of consequences of these questions.

1. Another Form of Morphological Computing

Conventional computing with a Turing machine can be described in the following way:

Figure 1. Conventional computation with Turing’s a-machine, but with modified morphological characteristics of the system [11].

Slight modification (generalization) makes the machine (s-machine) symmetric in the sense that the roles of the head and tape are equivalent and that the action of the head on tape is replaced by the interaction of the head and tape. It is a generalization, because when we make a non-physical assumption that only head is acting on tape, we return to the conventional Turing a-machine described at Fig. 1.  

Figure 2. Symmetric Turing machine in which head and tape are interacting and the distinction in the names of these components is purely conventional [11].  

Compounding of computational systems is based on the fact that at each level we can distinguish to levels in the information system: global (e.g. structure or configuration of characters on the tape) and local (e.g. selection of a character for a particular cell. 

Figure 3. “Simple” computing system (s-machine)

Now we can consider a compound computational system in which head of one level can be a tape of another (“lower” level) and tape of this level can be a head of another level.

Figure 3. Compound computing system of hierarchical, vertical architecture.

Conflicts of Interest: The author declares no conflict of interest.

References

1. Turing, A.M. On computable numbers, with an application to the Entscheidungsproblem. Proc. London Math. Soc., Ser.2, 1936, 42, 230-265, cor. 43, 544-546.
2. Searle, J.R. Is the Brain a Digital Computer? Presidential Address to the American Philosophical Association, 1990, http://users.ecs.soton. ac.uk/harnad/ Papers/Py104/searle.comp.html/ retrived 12/21/2013.
3. Copeland, B.J.; Proudfoot, D. Alan Turing’s forgotten ideas in computer science. Sci. Amer. 1999, 280, 76-81.
4. Adamatzky, A. Slime mould processors, logic gates and sensors. Phil. Trans. R. Soc. A. 2015, 37320140216.
5. Adamatzky, A. Martinez, G.J. Eds. Designing Beauty: The Art of Cellular Automata. Emergence, Complexity and Computation, Vol. 2; Springer, 2016.
6. Hagiya, M., et al. Molecular Robots with Sensors and Intelligence. Accounts of Chemical Research, (2014) 47, 1681-1690.
7. Hagiya, M., Wang, S., Kawamata, I., Murata, S., Isokawa, T. & Peper, F. On DNA-Based Gellular Automata. Unconventional Computation and Natural Computation, 13th International Conference, UCNC 2014, London, ON, Canada, July 14-18, 2014, Proceedings, Ibarra, O.H., Kari, L. & Kopecki, S. Eds.; LNCS, Springer, 2014, 8553, 177-189.
8. Kim, S.-J., Naruse, M. & Aono, M. Harnessing the Computational Power of Fluids for Optimization of Collective Decision Making. Philosophies 2016, 1(3), 245-260;
9. Kim, S.-J., Aono, M. & Nameda, E. Efficient decision-making by volume-conserving physical object. New J. Phys. 2015, 17, 083023.
10. Morita, K., An 8-state simple reversible triangular cellular automaton that exhibits complex behavior. In AUTOMATA 2016; Cook, M., Neary, T. Eds., LNCS 9664, 2016, pp. 70-184.
11. Schroeder, M.J. From Proactive to Interactive Theory of Computation. In Bishop, M., Erden, Y.J. Eds.; The 6^th AISB Symposium on Computing and Philosophy: The Scandal of Computation – What is Computation? The Society for the Study of Artificial Intelligence and the Simulation of Behaviour, 2013, pp. 47-51.
12. Russell, B. Mysticism and Logic, and Other Essays. Unwin, London, UK, 1963.

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As the scale of computations become large and as both people and machines demand communication, collaboration and commerce at the speed of light, rapid fluctuations in the demand for computing performance and fluctuations in available resource pools, both make it necessary to respond fast and readjust the computation structures and associated resources so as to not disrupt the user experience or the service transaction. Current Information Technologies from their memory-starved, server-centric, low-bandwidth origins from von Neumann's stored program control implementation of the Turing Machine are evolving with new architectures to meet the demand for scale and speed. In this paper we discuss the evolution of current IT to cognitive IT, where computing processes become self-aware of their resource requirements in real-time and seek to adjust them from a global knowledge of available resource pools and their provisioning processes. This is transforming the current state of the IT as we know it to a cognitive IT.

While self-managing IT has been the target of every enterprise since the introduction of autonomic computing, it has eluded us till recently. While there has been much progress in using autonomic computing to model, configure, monitor and control the external world outside of the computing processes, today, IT management consumes 70% of the IT budget to keep the applications available, secure and compliant with regulations. For every dollar spent on software development, another $1.38 is spent on managing and maintaining it. Every time a fault occurs, one has to stop, isolate it, diagnose it and fix it and it requires an army of experts from different disciplines to do it. This becomes an impossible task when large scale, and fluctuations both in workload demands and available resource pools are involved. In this paper we describe a new approach that brings finally self-managing properties to applications and workflows using a new computing model. Using this approach, we have reduced IT complexity and created an interoperable global network of clouds that can be used to support self-provisioning workloads with auto-scaling, auto-failover and live migration without disrupting user experience.

The paper reviews existing models for organizing information for machine learning systems in heterogeneous computing environments. In this context, we focus on structured knowledge representations as they have played a key role in enabling machine learning at scale. The paper highlights recent case studies where knowledge structures when combined with the knowledge of the distributed computation graph have accelerated machine-learning applications by 10x or more. We extend these concepts to the design of Cognitive Distributed Learning Systems to resolve critical bottlenecks in real-time machine learning applications such as Predictive Analytics and Recommender Systems.

Self-driving, also called fully autonomous or driverless cars are in focus in many domains, such as engineering, computer science, human-computer interaction and ethics. From an engineering and scientific perspective, technical problems are challenging, but are solved one step at a time. When it comes to ethics, it seems that many discussions run into a dead end. In a constructed ethical dilemma there is per definition no solution: whatever you do, the result will be bad.

The trolley problem, which is an ethical thought experiment [1], is a commonly used example of an unsolvable ethical dilemma: The self-driving car drives on a street with high speed. A group of people suddenly appears in front of the car. The car is too fast to stop before it reaches the group. If the car does not react, the whole group will be killed. The self-driving car could however evade the group by entering the pedestrian way and consequently killing a previously uninvolved pedestrian (Option A). Replacing the pedestrian with a concrete wall, which in consequence will kill the passenger of the self-driving car, is another option (Option B). Varying the personas of people in the group, the single pedestrian or the passenger can be used to alter the experiment. The use of personas allows including an emotional perspective [2], such as, e.g., stating that the single pedestrian is a child, a relative, very old, very sick or a brutal dictator, who killed thousands of people.

Even though the scenarios are similar, the responses of humans, when asked how they would decide, differ [3]. The problem is that the question asked has a limited number of possible answers, which are all ethically questionable and perceived as bad or wrong. Therefore, a typical approach to this problem is to analyze the scenarios by following ethical theories, such as e.g., utilitarianism, other forms of consequentialism or deontological ethics. For example, utilitarianism would aim to minimize casualties, even if it means to kill the passenger, by following the principle: the moral action is the one that maximizes utility (or in this case minimize the damage). Depending on the doctrine, different arguments can be used to prove or disprove the decision.

Applying ethical doctrines to analyze a given dilemma and possible answers can only be done by humans. How would self-driving cars solve such dilemmas? There are a number of publications that suggest to implement moral principles into algorithms of self-driving cars [3]–[6]. We find that this does not solve the problem, but it reassures that the solution is calculated based on a given set of rules or other mechanisms, moving the problem to engineering, where it is implemented.

It is worth to notice that the engineering problem is substantially different from the hypothetical ethical dilemma. While an ethical dilemma is an idealized constructed state that has no good solution, an engineering problem is always by construction such that it can differentiate between better and worse solutions. A decision making process that has to be implemented in a self-driving car can be summarized as follows. It starts with an awareness of the environment: Detecting obstacles, such as a group of humans, animals or buildings, and also the current context/situation of the car using external systems (GPS, maps, street signs, etc.) or locally available information (speed, direction, etc.). Various sensors have to be used to collect all required information. Gaining detailed information about obstacles would be a necessary step before a decision can be made that maximizes utility/minimizes damage. A computer program calculates solutions and chooses the solution with the optimal outcome. The self-driving car executes the calculated action and the process repeats itself.

The process itself can be used to identify concrete ethical challenges within the decision making by considering the current state of the art of technology and its development. In a concrete car both the parts of this complex system and the way in which it is created have a critical impact on the decision-making. This includes for instance the quality of sensors, code and testing. We also see ethical challenges in design decisions, such as whether a certain technology is used because of its lower price, even though the quality of information for the decision making would be substantially increased if more expensive technology (such as sensors) would be used.

Since building and engineering of self-driving vehicle involves various stakeholders, such as software/hardware engineers, sales people, management, etc., we can also pose the following questions: does the actual self-driving car have a moral on its own or is it the moral of its creators? And who is to blame for the decision making of a self-driving car?

Prototypes of self-driving cars are already participating in public traffic among human-driven cars [7]. Therefore it is important to investigate how self-driving cars are actually built, how ethical challenges are addressed in their design, production and use, and how certain decisions are justified. Discussing this before self-driving cars are officially introduced into the market, allows taking part in the setting and definition of ethical ground rules. McBride states that “Issues concerning safety, ethical decision making and the setting of boundaries cannot be addressed without transparency” [8]. We think that transparency is only one factor, as it is necessary to start further investigations and discussions. In order to give a more detailed perspective on the complex decision making process, we propose to create a conceptual ethical model that connects the different components, systems and stakeholders. It will show interdependencies and allow pinpointing ethical challenges. Focusing on important ethical challenges that should currently be addressed and solved is an important step before ethical aspects of self-driving cars can actually be meaningfully discussed from the point of view of societal and individual stakeholders as well as designers and producers. It is important to focus not on abstract thought experiments but on concrete conditions that influence the behavior of self-driving cars and their safety as well as our expectations from them.

References

[1]        P. Foot, “The Problem of Abortion and the Doctrine of Double Effect,” Oxford Rev., vol. 5, 1967.

[2]        A. Bleske-Rechek, L. A. Nelson, J. P. Baker, M. W. Remiker, and S. J. Brandt, “Evolution and the trolley problem: People save five over one unless the one is young, genetically related, or a romantic partner.,” J. Soc. Evol. Cult. Psychol., vol. 4, no. 3, pp. 115–127, 2010.

[3]        J.-F. Bonnefon, A. Shariff, and I. Rahwan, “The social dilemma of autonomous vehicles,” Science (80-. )., vol. 352, no. 6293, pp. 1573–1576, 2016.

[4]        N. J. Goodall, “Can you program ethics into a self-driving car?,” IEEE Spectr., vol. 53, no. 6, 2016.

[5]        L. Dennis, M. Fisher, M. Slavkovik, and M. Webster, “Formal verification of ethical choices in autonomous systems,” Rob. Auton. Syst., vol. 77, pp. 1–14, 2016.

[6]        L. Dennis, M. Fisher, M. Slavkovik, and M. Webster, “Ethical choice in unforeseen circumstances,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8069 LNAI, pp. 433–445.

[7]        M. Persson and S. Elfström, “Volvo Car Group’s first self-driving Autopilot cars test on public roads around Gothenburg,” Volvo Car Group Press Release, 2014. [Online]. Available: https://www.media.volvocars.com/global/en-gb/media/pressreleases/145619/volvo-car-groups-first-self-driving-autopilot-cars-test-on-public-roads-around-gothenburg.

[8]        N. McBride, “The Ethics of Driverless Cars,” SIGCAS Comput. Soc., vol. 45, no. 3, pp. 179–184, Jan. 2016.

We consider arguments for the technological and economic singularities from the perspective of Artificial Intelligence (AI) and Machine Learning (ML) perspectives, and argue that cognitive computing is a promising response to the economic singularity.