The Relative Importance of Perception, Embodiment, Metaphors, and

Ethics for Cooperative Human-Machine Coexistence

Norris Lee Smith

and Oussama H. Hamid

School of Social Sciences, University of Kurdistan Hewl

er, 30 Meter Avenue, Erbil, Iraq

Department of Computer Science and Engineering, University of Kurdistan Hewl

er, 30 Meter Avenue, Erbil, Iraq

Keywords:

Human-Machine Coexistence, War Robots, Artiﬁcial Intelligence, Human Intelligence, Phenomenology,

Embodiment, Psychology of Perception, Metaphoric Language.

Abstract:

In applications of soft computing, one question raised is the extent to which artiﬁcial intelligence (AI) and

human intelligence (HI) share similar quantitative or qualitative properties or both. Recently, we have argued

that phenomenology emphasizes ﬁrst-person experience as one of the central differences between AI and HI.

Presently, we expand this argument to include perception, embodiment, metaphors and ethics. For experience

to occur, the experiencing entity needs a body, which contributes to the development of the ﬁrst-person per-

spective. The work of Gibson and Merleau-Ponty supports this view by providing alternatives to information

processing and behavioral models in the study of perception. Similarly, Lakoff’s central metaphors are com-

pelling in the ﬁeld of linguistics. In case of AI, however, embodiment seems not to be apparent. As a result,

AI has difﬁculty understanding natural language because perception and many metaphors are expressed and

learned in terms of the body. The implication is AI can thrive as long as long as there is HI, which corrobo-

rates our view of human-machine coexistence. Furthermore, and not necessarily paradoxical, the humanistic

endeavor of ethics may be more suitable for AI in the case of war robots that successfully adhere to uni-

versal laws. The integration of AI and HI is accomplished by having humans as the source for ﬁrst-person

experiences, whereas machines are the extended minds of humans.

1 INTRODUCTION

Soft computing is a bundle of methodologies that

are often synergistic, working together to supplement

each other and provide ﬂexible information process-

ing capability for handling vexing real-life scenarios

(Zadeh, 1994; Mitra et al., 2002). The ﬁeld of soft

computing encompasses three main branches of re-

search and applications: (i) neural networks, which

serves as a framework for modeling how brains func-

tion, (ii) fuzzy systems, which models linguistic as-

pects of how humans describe the world around them,

and (iii) evolutionary computation, which accounts

for variation and natural selection in the biological

world (Keller et al., 2016).

Research on soft computing aims at exploiting the

tolerance for uncertainty, ambiguity, approximate rea-

soning, and partial truth in order to achieve tractable,

robust, and computationally economic solutions. One

question raised in applications of soft computing is

the extent to which artiﬁcial intelligence (AI) and hu-

man intelligence (HI) share similar quantitative or

qualitative properties or both. Differences emerge

quickly in any comparison of AI and HI. At the same

time, despite the clear dichotomy between AI and the

ﬁrst-person perspective in the experience of humans,

there are many instances in which human-machine

coexistence is increasing (Hamid et al., 2017).

This paper extends the differentiation of AI and

HI to include one model of the process of perception

and a recognition of the existence of the body for per-

ception, learning, and language. The paper is orga-

nized as follows. Section 2 introduces phenomenol-

ogy as a framework for describing ﬁrst-person expe-

riences along with James Gibson’s (2015) account of

information in textured grounds as the source of di-

rect perception (as opposed to traditional accounts in

which information processing of stimuli after enter-

ing the eye becomes the source of perception). In

section 3, we argue for humans’ embodied existence

in the world as primary in the process of perception

and consider its implications in the design of interac-

tive artiﬁcial systems. We also examine the role and

prevalence of metaphoric language (Section 4), and

Smith N. and H. Hamid O.

The Relative Importance of Perception, Embodiment, Metaphors, and Ethics for Cooperative Human-Machine Coexistence.

DOI: 10.5220/0006558804290435

end with a consideration of one application of AI that

may have an ethical advantage over HI, namely war

robots (Section 5). Whether different aspects of AI

and HI are compartmentalized or integrated, the same

issues remain – the exercise of judgment, learning,

and appropriate decisions.

2 PHENOMENOLOGY

Phenomenology has typically used a ﬁgure-ground

structure to explain experience in general and per-

ception in particular, applying the methodology to a

number of areas of interest (Smith, 2002; Pollio et al.,

1997) where nuances of ﬁrst-person human experi-

ence are important. Rubin’s vase in Figure 1A is prob-

ably the most common example of a ﬁgure-ground

grouping. James Gibson’s (2015) direct perception is

one such approach that emphasizes grounds and has

impacted the ﬁeld of ergonomics. His ecological view

claims grounds in the environment deﬁne the spatial

character of the visual world, not what happens to

stimuli after entering the eye. In short, information

in a textured ground, not objects, explains perception.

Gibson was interested in the world as a source of in-

formation, not processing mechanisms in the head,

which contradicted the centuries-old epistemology of

perception as the processing of copies. We perceive

space directly from the ground – hence the term di-

rect perception – which in many cases is a continuous

surface or an array of adjoining surfaces. Gibson pro-

vides examples of optic arrays and looks for patters of

optic ﬂow in them such as the experience of changes

in the ﬂow when approaching or moving away from

a certain point (Figure 1B). Moreover, learning does

not merely come from the stimulus-response model in

behaviorism. Gibson’s conclusions are that all spatial

perception is in regard to a textured ground, usually

the earth (Gibson, 2015).

The spatial character of the visual world is given

not by an analysis of the objects in it but rather by

a consideration of the background against which the

objects appear and become ﬁgural. In addition, most

perceivers are in motion either because they are mov-

ing in some direction or they are moving their heads.

Some information, however, remains constant as an

observer moves. Gibson calls these invariants, non-

changes that nevertheless persist during a change in

the observer. An invariant is a property of the en-

vironment that remains constant despite illumination

changes or movement of the observer, and Gibson

provides over 2 dozen examples in his three books

(Goldstein, 1981). In contrast to what Gibson refers

to as cognitivism, in Gibson’s view no intervening

mental processes are necessary and we reject any so-

called operations of the mind (mental entities such as

sense data). Grounds provide the information neces-

sary for us to perceive directly, and we use such in-

formation immediately with no need to transform it.

Unlike Gibson’s (2015) characterization of the “old

idea” that we process sensory inputs and convert them

into perceptions – the information processing model

in any of its various forms – the extraction of invari-

ants from the stimulus ﬂux is, arguably, a more accu-

rate model of visual perception.

As shown in ﬁgure 1C, an observer’s movement

towards a location results in environmental textures to

ﬂow everywhere except the invariant center. To stay

on course one needs to keep the unchanging center

of the optical ﬂow pattern on the destination. One

application of Gibson’s model involves pilots landing

aircraft in the earlier days of aviation. In some cases

airplanes were not stopping soon enough on runways.

Gibson determined that the relatively fast or slow ﬂow

of the optic array in relation to a ﬁxed point of refer-

ence gives the sensation of speed, and that the appar-

ent speed related to one’s distance from the ground.

Consequently, Gibson explained how pilots in sufﬁ-

ciently high airplanes sensed they were moving slow

enough to stop before the runway ended.

Although presented in contrast to information pro-

cessing and gestalt psychology, Gibson’s model of

perception is not entirely passive. The role of a mov-

ing, embodied active observer is clear throughout his

research. In his analysis of active touch, for instance,

there is a distinction between active and passive per-

ception. An observer may actively explore the sur-

faces of objects rather than merely feel an experi-

menter pushing on one’s skin. Bottom-up models

of perception where no learning is required, such as

that of Gibson, are not applicable to all situations in

the same way that any top-down model is not. One

theory postulates how the interaction of both bottom-

up and top-down processes in a perceptual cycle pro-

duces perception and interpretation (Neisser, 1976).

3 EMBODIMENT AND MAURICE

MERLEAU-PONTY

It is well understood that AI is particularly adept at

fast information processing that involves rules-based

logic and can be applied to related forms of compu-

tation, such as mortgage underwriting. Pattern recog-

nition (complicated situations when driving, for in-

stance) is better in HI with the development of ex-

pert thinking (Levy and Murnane, 2004). Complex

communication and ideation are also areas in which

Figure 1: A: Rubin’s ﬁgure. B: The pattern of optic ﬂow when looking out of the back of a train. C: The point towards which

a pilot is moving appears motionless, while the rest of the visual environment appears to move away from that point.

humans are superior owing to the ability to generate

new, good ideas and read nonverbal cues in commu-

nication. Recently, we have argued that a ﬁrst-person

perspective contributes to the difference between AI

and HI (Hamid et al., 2017) on conceptual grounds

without reference to perceived embodiment. At this

point, an examination of embodiment is warranted,

speciﬁcally the signiﬁcance of the body-as-subject.

Merleau-Ponty is the best theorist to address the is-

sue of our embodied existence in the world as pri-

mary in the process of perception. In his view, our

embodied inheritance is more fundamental than our

reﬂexive capacity, and the analytic mode is deriva-

tive from the body’s immediate exposure to the world

(Merleau-Ponty, 1962). Such a ﬁrst-person perspec-

tive reveals the failings of both empiricism and what

he calls intellectualism, known as rationalism or ide-

alism. Stated simply, we have a world and access to

the world through the body.

When Merleau-Ponty states we are our bodies,

he does not understate so-called mental phenomena

but rather incorporates the perceiving mind in an in-

carnated body; using our minds is inseparable from

how we are situated physically. In an embodied

state of being, the material and ideational are inti-

mately linked in the body-subject that thinks and per-

ceives. Merleau-Ponty stresses the body is not solely

an object, merely one of many material components

of the world, but is our means of communication

with the world (Internet Encyclopedia of Philosophy,

2017). An example he provides is when the left hand

touches the right hand while the right hand touches

an object; the right hand as object (muscles, ﬂesh)

is different from it as a touching subject (Merleau-

Ponty, 1962). The body is both perceived object and

perceiving subject, if not simultaneously then in an

oscillation. Another example is when both hands are

pressed together. Either hand can alternate in its role

of touching or being touched. It is not the case that

one simply has two sensations together, as if grasp-

ing two objects next to one another. This ambiguity

of touching and touched is representative of the full

process of perception. Merleau-Ponty also refers to

the reversibility of the body in its ability to be both

sentient and sensible (Merleau-Ponty, 1962).

To continue with the emphasis on integration,

we can say when the body-subject acts, it is in-

separable from when the body-subject perceives.

Merleau-Ponty deﬁnes understanding as the experi-

ence of harmony between an intention and a per-

formance, or what we aim at and what is given to

us. Correspondingly, consciousness is better un-

derstood as a matter of “I can” instead of “I think

that”. In short, the lived experience of our bod-

ies denies the differentiation of mind from body and

does not allow the detachment of subject from object

(Internet Encyclopedia of Philosophy, 2017).

Technology design is one application that comes

from a recognition of the role of our experience of the

body. Somatics, in this case how it relates to experi-

ence in technology, provides embodied approaches to

learning and interacting that focus on attention, con-

text, and awareness in order to provide a set of de-

signs that show how to apply somatics (Schiphorst,

2009). Chow and Harrell (2011) use principles in

phenomenology along with embodied cognition ap-

proaches in cognitive science to examine animated

gestural interfaces in creative computing systems. In

their approach that centralizes embodied meaning

making, they focus on how the sensorimotor expe-

riences of users inform the construction of meaning

and how to incorporate this knowledge in the design

of interactive systems. The authors claim that to be

more engaging, creative computing systems in inter-

active narrative environments such as video games

and computer-based artwork require a new design

perspective that allows the use of more evocative ges-

tural interaction. The gist is that bodily motion em-

bodies our intention and should serve as the basis for

human-computer interaction (HCI). Such an outlook

emphasizes bodily familiarity and cognitive intimacy

in the design of creative computing systems, which

enables an engagement close in scale to our embod-

ied experiences (Chow and Harrell, 2011).

Considering mechanisms of user input, point-and-

click input and ongoing gestural input, Chow and

Harrell (2011) are concerned how the separation of

gestural input from other kinds of motor input may

lead us away from tight sensory-motor connection in

HCI. Instead, whether a kind of motor input has a

meaningful component of motion, i.e., is intentional

in a computational environment, is a more important

distinction. Gestural inputs that involve situated, em-

bodied, and evocative motions are more meaningful.

One of their examples shows that human-scale em-

bodiment is temporal as well as spatial. The round

jog dial of a videotape recorder, or VCR, allows the

user to include direction and speed. As with human

gestures, the faster the spinning, the more the inten-

tion is to hurry. For computer interaction, circling on

a touchpad could allow a user to browse through a

large database. With practice, more kinds of bodily

motion could be spatiotemporal embodiment of in-

tention (Chow and Harrell, 2011). The phenomena

of sensory substitution and phantom limbs along with

virtual reality, computer games, and overall human-

machine interaction are areas in which the experience

of embodiment is relevant.

Several other researchers in psychology, neuro-

science and other areas have examined embodiment.

Chilean biologist Francisco Valera, for instance, pop-

ularized neurophenomenology as an emerging ﬁeld in

neuroscience by incorporating the phenomenological

method along with an emphasis on embodiment. He

co-wrote The Embodied Mind to propose a common

ground between mind in science and mind in experi-

ence (Varela et al., 2017). Speciﬁcally related to AI,

the embodied approach has been referred to as nou-

velle AI, situated AI, behavior based AI, or embodied

cognitive science.

4 METAPHORIC LANGUAGE

AND GEORGE LAKOFF

George Lakoff and Mark Johnson (2003) have ex-

plored the role of metaphorical concepts in how we

fundamentally understand, organize, and share the

world, and they explicate the experiential grounding,

coherence, and systematicity of metaphorical con-

cepts. From this perspective metaphors are primary

not only for language but also in human thought pro-

cesses; they simply dominate cognition. The idea is

that our conceptual system is structured metaphori-

cally, and this is why metaphors as linguistic expres-

sions are possible and sensible. Lakoff and John-

son (2003) also demonstrate how an experiential view

can explain how metaphors are frequent, organized,

and useful, in contrast to schools of thought that ne-

glect the necessity of an experiential basis. Further-

more, metaphors structure our most important con-

cepts such as “education is a journey” and “life is

a play”. Although the authors’ explanations of par-

ticular metaphors (“time is money”) are not set in

stone, to understand metaphors fully one cannot sep-

arate them from their experiential base.

In addition to describing basic orientation

metaphors such “in vs. out” and “up vs. down” (“ra-

tional is up; emotional is down”), Lakoff and John-

son (2003) provide an excellent example of how ar-

guments are equated with battles in different forms in

the metaphor “argument is war”. As rational animals,

and especially in the legal, diplomatic, journalistic

and academic worlds, we have institutionally evolved

to ﬁght without physical conﬂict in the form of ver-

bal arguments, although we conceptualize such argu-

ments in the same way as physical battles. Scientists

have observed animals challenging, intimidating, at-

tacking, defending, retreating, and surrendering, and

human arguments are similar, whether characterized

as crude or rational. If I have something to win or

lose, I may develop a strategy to shoot down your ar-

gument, defend my position, establish territory, coun-

terattack, or convince you to accept my viewpoint. If

I am operating from a “lower” level, I may use what-

ever means I can, such as to threaten, invoke some au-

thority, insult, belittle, evade an issue, bargain, ﬂatter,

or even claim to give a rational reason while doing

some of these. Of course from a “higher” level, the

construction of premises and conclusions in rational

argumentation, these are forbidden, yet arguments in

this form operate the same way in that you attack and

destroy the opponent’s position while defending your

own in the expectation of victory or even wiping him

out if completely successful.

... not only our conception of an argu-

ment but the way we carry it out is grounded

in our knowledge and experience of physical

combat. Even if you have never fought a ﬁst-

ﬁght in your life, much less a war, but have

been arguing from the time you began to talk,

you still conceive of arguments, and execute

them, according to the ARGUMENT IS WAR

metaphor because the metaphor is built into

the conceptual system of the culture in which

you live. Not only are all the “rational” argu-

ments that are assumed to actually live up to

the ideal of RATIONAL ARGUMENT con-

ceived of in terms of WAR, but almost all of

them contain, in hidden form, the “irrational”

and “unfair” tactics that rational arguments in

their ideal form are supposed to transcend.

(Lakoff and Johnson, 2003), pp. 63-64.

Examples from Metaphors We Live By (Lakoff and

Johnson, 2003) that exemplify the path from “high”

rational argumentation to everyday, irrational argu-

ments include: (1) it would be unscientiﬁc to fail to

... (threat), (2) the work lacks the necessary rigor for

... (insult), (3) your position is right as far as it goes

... (bargaining), (4) the work will not lead to a formal-

ized theory (belittling), (5) in his stimulating paper ...

(ﬂattery), (6) Behaviorism has led to ... (challenging

authority), (7) Hume observed that ... (authority), and

(8) Obviously ... (intimidation).

Moreover, conceptual metaphors such as “knowl-

edge is food” or “social organizations are plants”

shape perception and communication. Although dif-

ferent cultures formulate key concepts differently –

truth, love, hate, war – our bodies are the basis for

much of our everyday thinking and conceptualization

of more abstract thought. In this view, we form cogni-

tive models early in development, the basis of which

are early sensorimotor experiences. A related discus-

sion of contextual learning of time is found in a previ-

ous work by the coauthor of this paper (Hamid, 2015).

The container metaphor is a good example of how

we think in spatial terms. A glass contains juice,

and we project this metaphor onto something we en-

counter that is less concrete. For instance, we could

use the word in to think of something else in the

same way, perhaps a tribe that lives in a forest (Lakoff

and Johnson, 1999). This process is in stark contrast

to computational and representational models of the

mind and challenges the concept of truth in humans

because from this perspective, truth – or at least com-

prehension and coherence – is relative to the concep-

tual system used to understand situations. If our con-

ceptual systems arise from interactions with the envi-

ronment, it is axiomatic that understanding is always

grounded in metaphorical experience.

5 DO WAR ROBOTS GIVE AI AN

ETHICAL ADVANTAGE?

Rather than delineate the differences between AI and

HI by pointing out the respective advantages of each

when working separately or together, it is construc-

tive to present at least one emerging area in which AI

may hold a clear advantage: ethics in war involving

robots. A look at the literature on the subject shows

an acceptance of the inevitability of fully or nearly

autonomous weapons and a debate over their partial

or total elimination. The views of the USA, China,

and Russia regarding robotic weapons appear to be

to increase automation while simultaneously empha-

sizing the continued presence of humans (Guizzo and

Ackerman, 2016). Any serious arms-control agree-

ment seems unlikely, and if robotic military systems

are here to stay, then a focus on protocols such as the

Geneva Conventions and global/local Rules of En-

gagement (ROE) is warranted so as to highlight the

Laws of War (LOW) for international exposure. The

creation of an independent robot may be easier than

other forms of AI because of the numerous treaties on

laws of war that have existed for decades.

Ronald Arkin (2008) is one researcher who be-

lieves robots can perform more ethically than human

soldiers in certain circumstances because we can pro-

gram them appropriately. International humanitarian

laws, for instance, can become incorporated in robot

behavior by translating particular concepts into vari-

ables and operations accordingly. Whether it is a

Boolean variable of permitted or prohibited use of

lethal force, or a determination whether a target is

an enemy, Arkin successfully tested his set of algo-

rithms. There is much room for improvement in bat-

tleﬁeld ethics, and robotic systems can exercise more

restraint when necessary and invariably follow our

guidelines to avoid harming civilians (Arkin, 2008).

They do not suffer from negative emotions or other

factors that cloud judgment, which means they can

outperform humans (Guizzo and Ackerman, 2016).

Colin Allen provides a diverging view of the ethics

of military robots in that programming speciﬁc rules

would likely work well in an ideal war free of civil-

ians, though this is increasingly rare. Employing ma-

chine learning would be preferable, but this is un-

likely in the next 50 years (Bland, 2009).

Apart from beneﬁts provided to a military that

uses them, such as immunity to biological or chem-

ical weapons, precision, endurance, and persistence,

robotic weapons systems could lessen collateral dam-

age by an ability to act conservatively. Unless pro-

grammed with self-preservation, there is no need to

act when certainty is low. Sensors equipped for bat-

tleﬁeld observations and faster integration of infor-

mation from multiple sources are other factors. In

addition, robotic systems do not suffer from scenario

fulﬁllment or cognitive dissonance, and they are even

able to monitor independently the behavior of human

soldiers or anyone within range of observation, which

could lead to a reduction in ethical infractions (Arkin,

2009).

In 2008 the U.S. Navy (Lin et al., 2008) exam-

ined multiple issues involving the ethics of robots

in a broad report. A quick summary of these fol-

lows. With legal challenges, it is unclear who would

be assigned blame for harm resulting from an au-

tonomous robot. What would happen when a robot

refuses an order, and do soldiers need to give con-

sent to additional risks? Who would give the or-

der to strike if the system is not fully autonomous,

and if it is, should we demand 100% accuracy in

the identiﬁcation of a target if human soldiers cannot

reach this level? Barriers to start wars could be low-

ered, and there is imprecision in the LOW and ROE

that could easily complicate interpretation. Technical

challenges include discrimination among targets and

acceptable standards, problems with ﬁrst-generation

models and beta-testing, unauthorized overrides and

hacking, competing and possibly inconsistent ethical

frameworks in the design stage, coordinated sharing

of information before attacks, and the establishment

of a chain of command among robots.

Regarding human-robot interaction, there could

be effects on squad cohesion if human soldiers are

being watched. Robots may need self-defense ca-

pabilities, and local populations may not build trust

with robots as well as with humans. So-called

comfort robots could become a controversial topic

of increased scrutiny as well. If military victory

is quicker and more dominant with the use of au-

tonomous weapons, then there could be counter tac-

tics resulting from asymmetric warfare as well as pro-

liferation, including into space. Related concerns are

our increased dependency on technology and the en-

croachment of robots into everyday life and the pri-

vacy/security dilemma that ensues. Finally, a mil-

itary could co-opt the work of ethicists to serve its

own agenda, robots could or may need to have legal

rights, and excessive precaution could slow progress

and limit the effectiveness of solutions to many of

these concerns (Lin et al., 2008). Regardless of the

eventual relevance of the above points, it is clear that

the juxtaposition of AI and HI is increasing and the

same issues of decision making arise.

6 CONCLUSIONS

What does it mean to learn something? Can we teach

wisdom? Is a robot a weapon or does it operate a

weapon? The more we can clarify terms, especially

those that are particularly emotional, the more under-

standing emerges. Even a quick look at metaphors re-

veals how we often structure issues, including in AI,

as “technology versus the humanities” or “rationalism

versus romanticism” when such projections may not

apply. For instance, while there are legitimate con-

cerns about so-called killer robots, there is no men-

tion of any serving in law enforcement, which would

immediately change the context and possibly one’s

perception of threat if abuses by the police are sub-

sequently curtailed. A related consideration is how

comfortable we are having automated systems record

our own aggression. While it is difﬁcult to conceive of

AI either being or having a body or following the de-

velopmental progression of human children, there is

an emerging trend of increasing integration of AI and

HI not only in the design of creative computing sys-

tems but also in forms of cooperative coexistence such

as increased efﬁciency with automation in the work-

place, playing chess, strategies in professional sports,

writing simple news articles, and disease detection in

radiology (Hamid et al., 2017).

One utilization of automation that has merged

with a human proﬁciency is commercial piloting. The

basic idea is that increased use of automation is sup-

posed to prevent mistakes from human shortcomings,

such as fatigue and inattention, and free pilots to think

about the big picture and thus improve overall watch-

fulness and attentiveness. Unfortunately, more au-

tomation has led to more mind-wandering such that

lack of monitoring has become a factor in the major-

ity of accidents. Although it is difﬁcult to identify and

assess complacency and boredom, it is simple to con-

clude that pilots need to pay attention and properly

monitor the controls when employing autopilot (Kon-

nikova, 2014). It appears that automation has not re-

duced the number of errors, but it has changed their

form. A smoother integration would involve taking

into account the way pilots’ minds operate when in-

troducing and innovating forms of automation. This

is an immensely practical application of successful

coexistence. One study (Casner et al., 2014) tested

how the prolonged use of cockpit automation affected

the manual ﬂying skills of pilots, based on a concern

of potential deterioration resulting from the adoption

of a more supervisory role after the increased em-

ployment of automation systems. The results showed

that while some skills remained intact, even if infre-

quently practiced, certain cognitive tasks needed to

ﬂy the plane manually were more problematic. Cru-

cially, performance on these tasks was associated with

how often pilots were inattentive when using cock-

pit automation. Again, a more detailed understanding

of the mechanisms at work in the coordination of AI

and HI allows us to acknowledge problems and imple-

ment solutions in various subﬁelds, which is already

happening (Hamid, 2017).

ACKNOWLEDGEMENTS

The authors would like to thank the anonymous re-

viewers for their valuable time and helpful comments.

REFERENCES

Arkin, R. C. (2008). Governing lethal behavior: embedding

ethics in a hybrid deliberative/reactive robot architec-

ture. In Proceedings of the 3rd ACM/IEEE interna-

tional conference on Human robot interaction, pages

121–128. ACM.

Arkin, R. C. (2009). Ethical robots in warfare. IEEE Tech-

nology and Society Magazine, 28(1):30–33.

Bland, E. (2009). Robot warriors will get a guide to ethics.

(online accessed: 2017, July 26).

Casner, S. M., Geven, R. W., Recker, M. P., and Schooler,

J. W. (2014). The retention of manual ﬂying skills in

the automated cockpit. Human factors, 56(8):1506–

1516.

Chow, K. K. and Harrell, D. F. (2011). Enduring interac-

tion: an approach to analysis and design of animated

gestural interfaces in creative computing systems. In

Proceedings of the 8th ACM conference on Creativity

and cognition, pages 95–104. ACM.

Gibson, J. J. (2015). The Ecological Approach to Visual

Perception: Classic Edition. Psychology Press, Taylor

& Francis Group, New York and London.

Goldstein, E. B. (1981). The ecology of JJ Gibson’s per-

ception. Leonardo, 14(3):191–195.

Guizzo, E. and Ackerman, E. (2016). Do we want robot

warriors to decide who lives or dies. IEEE Spectrum,

31.

Hamid, O. H. (2015). A model-based markovian context-

dependent reinforcement learning approach for neu-

robiologically plausible transfer of experience. In-

ternational Journal of Hybrid Intelligent Systems,

12(2):119–129.

Hamid, O. H. (2017). Breaking through opacity: A context-

aware data-driven conceptual design for a predictive

anti money laundering system. In 2017 9th IEEE-

GCC Conference and Exhibition (GCCCE), pages

421–426.

Hamid, O. H., Smith, N. L., and Barzanji, A. (2017). Au-

tomation, per se, is not job elimination: How artiﬁcial

intelligence forwards cooperative human-machine co-

existence. In 2017 IEEE 15th International Confer-

ence on Industrial Informatics (INDIN), pages 899–

904.

Keller, J. M., Fogel, D. B., and Liu, D. (2016). Funda-

mentals of Computational Intelligence. Wiley-IEEE

Press.

Konnikova, M. (2014). The hazards of going on autopilot.

The New Yorker.

Lakoff, G. and Johnson, M. (1999). Philosophy in the

Flesh: The Embodied Mind and its Challenge to West-

ern Thought. Basic books.

Lakoff, G. and Johnson, M. (2003). Metaphors we live by.

University of Chicago press.

Levy, F. and Murnane, R. J. (2004). The New Division of

Labor: How Computers Are Creating the Next Job

Market. Princeton University Press.

Lin, P., Bekey, G., and Abney, K. (2008). Autonomous

military robotics: Risk, ethics, and design. Techni-

cal report, California Polytechnic State Univ San Luis

Obispo.

Internet Encyclopedia of Philosophy (2017).

Maurice Merleau-Ponty. Last accessed on July

28, 2017.

Merleau-Ponty, M. (1962). Phenomenology of perception.

trans. Smith, London: Routledge and Kegan Paul.

Mitra, S., Pal, S. K., and Mitra, P. (2002). Data mining in

soft computing framework: a survey. IEEE transac-

tions on neural networks, 13(1):3–14.

Neisser, U. (1976). Cognition and reality: Principles

and implications of cognitive psychology. WH Free-

man/Times Books/Henry Holt & Co.

Pollio, H. R., Henley, T. B., and Thompson, C. J. (1997).

The Phenomenology of Everyday Life: Empirical In-

vestigations of Human Experience. Cambridge Uni-

versity Press.

Schiphorst, T. (2009). Body matters: The palpability of

invisible computing. Leonardo, 42(3):225–230.

Smith, N. L. (2002). A phenomenological study of the ex-

perience of travel. PhD thesis, The University of Ten-

nessee Knoxville.

Varela, F. J., Thompson, E., and Rosch, E. (2017). The

embodied mind: Cognitive science and human experi-

ence. MIT press.

Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft

computing. Communications of the ACM, 37(3):77–

85.