Towards embedding high-resolution intelligence into the built-environment

Abstract

Prevailing architectural design paradigms, identified as those informed by historically conservative positions and methods, are incompatible with the intelligent built-environment discourse. Two core considerations inform this assessment. The first asserts that such paradigms produce spaces and programmatic distributions in terms of discrete, precisely delimited, and artificially ordered static partitions. The second asserts that said paradigms preclude (at best) or exclude (at worst) discussions of technological intelligence from the early stages of the design process, thereby negating the possibility of imbuing the built-environment with inherent intelligence. The rigidity expressed in the first consideration, and the disregard for technological intelligence expressed in the second, produce very low-resolution and-adaptability architectures. As a result , occupants are compelled to conform to their built-environment rather than the expected vice versa, as it is fundamentally incapable of actively, reactively, and interactively promoting their well-being. In this paper, two key positions (i.e., high resolution space and high resolution intelligence) motivated by the above considerations are promoted as part of a fundamentally different design paradigm, one expressly geared towards personalization, interaction, and intelligence in a parametrically fluid and self-adapting built-environment capable of intuitive physical, spatial, and computational feedback-loops

Full text

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Towards embedding high-resolution
intelligence into the built environment
Alexander Liu Cheng // TU Delft
Abstract
Prevailing architectural design paradigms, identied as those informed by historically conserva-
tive positions and methods, are incompatible with the intelligent built-environment discourse.
Two core considerations inform this assessment. The rst asserts that such paradigms produce
spaces and programmatic distributions in terms of discrete, precisely delimited, and articially
ordered static partitions. The second asserts that said paradigms preclude (at best) or exclude
(at worst) discussions of technological intelligence from the early stages of the design process,
thereby negating the possibility of imbuing the built-environment with inherent intelligence.
The rigidity expressed in the rst consideration, and the disregard for technological intelligence
expressed in the second, produce very low-resolution and -adaptability architectures. As a re-
sult, occupants are compelled to conform to their built-environment rather than the expected
vice versa, as it is fundamentally incapable of actively, reactively, and interactively promoting
their well-being. In this paper, two key positions (i.e., high resolution space and high resolution
intelligence) motivated by the above considerations are promoted as part of a fundamental-
ly different design paradigm, one expressly geared towards personalization, interaction, and
intelligence in a parametrically uid and self-adapting built-environment capable of intuitive
physical, spatial, and computational feedback-loops
Keywords
Robotically Augmented Environments; Cyber-Physical Systems; Wireless Sensor Net-
works; Interactive Architecture; Ambient Intelligence.
Towards embedding high-resolution intelligence into the built-environment
Alexander Liu Cheng

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Introduction
Prevailing architectural design paradigms and their corresponding construction and fabrication
methods subsume a historically and technically identiable set of theoretical and technological po-
sitions that invariably produce buildings and structures with fundamentally similar shape grammars
and typologies (e.g., standardized building blocks, prescriptive structural hierarchies, and clearly
dened and delimited walls, oors, ceilings, etc.). These, in turn, yield as well as entail fundamentally
similar spatial congurations and physical limitations that impose articial static frameworks upon
dynamic occupants, forcing the latter to conform to the former. Such paradigms and their resulting
forms and spaces may have been, in their due time and in one expression or another, justied stan-
dard means of conceptualizing and realizing inhabited spaces. With the advent of the Information
Age, however, the promise of new kinds of soft services as well as hybrid technologies consisting
of both hard mechanical parts complemented or even driven by soft computational systems render
such conforming unnecessary and indeed unjustiable.
The feasibility of embedded intelligence in the built-environment is a relatively recent development.
Although visions of such have been present for longer (see Cook, 1970, 1972; Eastman, 1972; Ne-
groponte, 1969, 1975; Pask, 1975a, 1975b), it has not been until recently that the supporting com-
ponents required to sustain the entailed systems and services have reached a level of reliable and
affordable maturity—this is a necessary precondition for the present discourse, as no technologies
(may these be tangible products or intangible processes) can ourish without a robust framework
based on mutually complementary systems and/or services (both technical and market-oriented)
to support them (Milgrom, 1990). It was at the end of the 20th century and the beginning of the
21st that robotically or otherwise enabled intelligent environments became demonstrably and
functionally real—for example, The Aware Home (Kidd et al., 1999), RoboticRoom (Sato et al., 2004),
Wabot-House (Sugano et al., 2006), etc. Such solutions, in conjunction with other similar yet exper-
imental proof-of-concept implementations in practice and academia (for example, Fox and Kemp,
2009; Oosterhuis, 2003, 2011, 2012; Oosterhuis and Bier, 2013)—sometimes emphasizing material
or formal, i.e., geometrical, intelligence over computational and vice versa—demonstrated that
built-environments could express elementary forms of agency and intelligence in order to engage
its users physically and informationally. This saw to the proliferation of Interactive Architecture (IA)
and Ambient Intelligence (AmI) from trends and tendencies to established discourse.
But there remains no corresponding proliferation of IA and AmI projects in industry. This is due
to several factors such as: (1) costs typically associated with intelligent services (Andò et al., 2014;
Wichert et al., 2012) and (2) with corresponding late-stage design consolidation of said services
and their corresponding production (Isack and Gibb, 2003; Tam et al., 2007), as well as (3) inno-
vation-hindering conservatism from the Architecture, Engineering, and Construction industries (Bock
and Linner, 2015). Note that these factors are consequences of a mentality that hoists outdated
methodologies upon innovative concepts. For example, costs corresponding to the post hoc installa-
tion of intelligent services in a conventional environment are admittedly high due to retrotting—a
reality that would be unnecessary if the consideration of said services had been conceived and
conceptualized early in the design process. Similarly, the late-stage design of such services and their
production cannot be conceived with a bottom-up approach, since the architecture in which these
services are to be installed and deployed already prescribe limitations in size, scale, and scope. So
service components are not necessarily produced with efciency in mind, but with custom cohe-
sion and compatibility restrictions, which increase costs—again, something that could be avoided
via more appropriate early-stage design considerations.
Towards embedding high-resolution intelligence into the built-environment
Alexander Liu Cheng

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In the last decade there has been an increase in the availability and accessibility of
cost-effective intelligence-enabling technologies (Baronti et al., 2007)—robotic and
otherwise—and their corresponding optimized fabrication methods (Bier, 2014; Bock
and Linner, 2015; McGee and Ponce de Leon, 2014; Menges, 2015). By adopting a
high-resolution paradigm prescriptive of early-stage design methodologies that sub-
sume fundamental and analytical intelligence considerations, the detracting factors
mentioned above may be mitigated or altogether avoided. For example, a number of
recent AmI projects developed under a more progressive paradigm demonstrated
that intelligent services—both in terms of architecture and of computation—may
be implemented successfully and affordably (see, for example, Guettler, J., Linner, T.,
Georgoulas, C., and Bock, T., 2015; Linner et al., 2015; Liu Cheng et al., 2015) via
Wireless Sensor Network (WSN) and Body Area Network (BAN) technologies.
Moreover, early-stage design consolidation of intelligent services in conjunction with
robotically driven production (Bier, 2014), which takes into account the fundamental
changes in the structure and infrastructure of the architecture that must be adopted
in order to enable robotic environments suitable for ubiquitous systems and service
robots (Bier, 2011; Forlizzi and DiSalvo, 2006; Linner et al., 2015), have instigated con-
siderable cost reductions (Bock and Linner, 2015).
The present discussion of high-resolution built-environments does not intend to pre-
scribe an exhaustive list of identifying desiderata. Instead, it is limited to promoting
two core characteristics that conform, in part, the common core characteristics of
high-resolution strategies in general. These characteristics resolve the two identied
disadvantages of prevailing architectural design paradigms—i.e., the predilection to-
wards discreet and articial spatial distribution that forces users to conform to static
physical and spatial conditions as well as to the lack of fundamental intelligence. For
detailed discussions of partial and/or full implementations of the positions promoted
below, see Liu Cheng et al., 2015, 2016; Liu Cheng and Bier, 2016.
High-resolution Space
High-resolution built-environments conceive physical form as well as programmat-
ic space as continuous and parametrically modiable—both physically, spatially, and
computationally—with respect to the physical behaviors and sensorial conditions of
its occupants. Such environments may be most appropriately characterized as uni-
ed robotic systems with differentiated yet mutually supporting and complement-
ing components, which mirrors Zappe’s suggestion that robots may be viewed as
scaled models of large-scale mobile buildings capable of changing their forms (2012)
and—in the present case—functions. In order to conceptualize such uidity of form
and function, programmatic needs and user-requirements must be carefully analyzed
and catalogued with respect to user-behavior across location and time. The resulting
formal and spatial grammar would be a de facto dictionary of fuzzy typologies that
correlate the occupant’s physical and sensorial activities with geometrical and spatial
transformations and function across time. Having done this, resulting programmatic
spaces ow, merge, and ebb from and into one another, and complementing furnish-
ings appear from and disappear into ambiguously identied oors, walls, and ceilings,
depending on the presence or absence of its occupants as well as their necessities
with respect to particular programs at specic moments in time.
In such environments, architectural systems, by virtue of their differentiated geome-
tries and fuzzy typologies as well as supported by computational resources, become
highly adaptable and transformable components that activate particular programs and
services based on careful analysis of user-behavior over time via specic activation
patterns. Consequently, a variety of programs may be instantiated in the same space,
or a complementary overlap may be effectuated to suit user-needs. Furthermore, as
the occupants and the habitat learn from and adapt to one another over time, the

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built-environment develops a particular agency and non-deterministic behavior—Sanchez points
out that a reactive approach is less interesting than a behavioral approach, where architectural ele-
ments could express their own ‘attitude’ to environmental stimuli (2014).
In this manner, the relationship between architectural form and space and the user becomes intui-
tively intimate, decreasing the extent and degree of the negotiating medium or buffer between oc-
cupant and habitat. In this environment, architectural form (both positive and negative) and function
would be engaged in a perpetual dynamic dance with the occupant, enhancing and capitalizing on the
fact that architecture informs the way we move within a space—indeed, the formal language of a
space could be interpreted as a script, a choreography for the body (Wortelkamp, 2012). According
to Wortelkamp, dance transforms architectural space into movement, which consequently shapes
and forms it (2012), making transitions throughout space feel as a natural extension of the body. As
Schumacher pointed out, movement is characterized by a variety of acceleration and deceleration
rates in displacing from one stationary condition to another, which entails that precisely and evenly
dened sudden start-and-stop movements—implicitly involved in crossing imaginary programmatic
boundaries under prevailing design paradigms—are unnatural (2012).
The systems subsumed by high-resolution built-environments would effortlessly facilitate an imme-
diate and intimate version of what Oosterhuis has described as a Society of Home, where human and
non-human objects / products endowed with a fair degree of sophisticated intelligence will com-
municate with one another, thereby instantiating a home of Internet of Things and People; as well as a
Society of Building Components, where the environment’s components act and react informationally
(i.e., exchange data) and physically (i.e., change shapes) towards one another and towards the users
(2014).
High-resolution Intelligence
This high-resolution built-environment’s intelligence would supervene on WSNs, which are special-
ized autonomous sensors and actuators grouped (1) to monitor and/or to control environmental
and physical conditions, and (2) to transmit the corresponding data to a base station and/or hand-
over control to a specic actuator in the network (Yang, 2014; Yang and Cao, 2008). Data analysis
and computation may be undertaken by one or more of the network nodes or by Internet-based
analytics services, depending on data-volume, network scale, and service requirements. Since WSNs
are decentralized solutions, they avoid the high-costs generally associated with highly integrated and
centralized systems. Georgoulas et al. (2012) showed that a solution that seeks to reduce complex-
ity of functions and cost should be one that does not have all services and functions centralized in
a service robot or in a static location, but rather one that strategically distributes services along
a decentralized and distributed controlled environment. These considerations also make WSNs
particularly promising as enablers of intelligence, as distributed computing has been identied as
a key foundation for applications and technologies involved in interactive architecture (Dulman,
2014), which is a principal reason why WSN technologies are currently used for the development
of distributed and networked interactive environments and architectures that progressively con-
verge physical and virtual space (Bier, 2012). In addition WSNs, which gather data from the user
indirectly, BANs—a subset of WSNs that emerged in the last decade (see González-Valenzuela et
al., 2013; Ruiz and Shimamoto, 2006)—would gather data directly, which makes them ideal for am-
bulatory monitoring solutions. With the combination of both indirect and direct user-observation,
the intelligent system deployed as part of a fundamentally and analytically IA and AmI would be able
to generate a higher-resolution prole for the user(s), which would increase learning delity and
response accuracy. Furthermore, WSN nodes would be the primary source for interior / exterior
environmental data, while BANs would be the primary source for user data. The IA/AmI system
would be able to use this data difference to better understand the status of the user with respect
to the interior / exterior environment and vice versa.
High-resolution built-environments are equipped with varying degrees of intelligence, ranging from
computationally sophisticated physical and/or sensorial services based on heuristic decision-making
processes to simple and reexively reactionary ones (see gure 1). For example, a basic lighting
Towards embedding high-resolution intelligence into the built-environment
Alexander Liu Cheng

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Towards embedding high-resolution intelligence
into the built-environment
Figure 1.
A high-resolution intelligent built-environment’s systems architecture. The general execution sequence is to be read top-down.
However, due to its closed-loop character, routines may beckon one another without predetermined sequence.

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system that activates when the environment’s perceived lumens reach
below a certain threshold may be considered reexively intelligent, as
it is driven by a simple if-then decision-making mechanism. We may
contrast this with a highly sophisticated system that employs multi-lay-
ered, multi-dimensional, and heterogeneous mechanisms to recognize
the mood of its occupant(s). Such a system could gather, for example,
time series datasets on body-temperature, breathing and heart rates,
blood-pressure, acceleration and spatial displacement patterns, vol-
ume and tone of voice, etc., and consider them against both a general
pre-determined mood-categorizations baseline as well as the user’s
particular previously recorded datasets in a variety of contexts to
ascertain correlations and probabilities associated with a variety of
mood-proles. Over time, and via Articial Neural Networks (ANN),
the system would learn to distinguish exhilaration from anger, even
though both emotions are associated with elevated temperature and
heart-rates, sudden and often erratic spatial displacement, etc., based
on something as personal and subtle as the user’s speech inections.1
During the initial stages of operation there would be frequent instances
of inaccuracies. But the character of a self-learning system—assuming
that the particular ANN model employed is appropriate to the learn-
ing task at hand2—is such that it becomes more accurate over time,
learning from its own inaccuracies to approximate a high-resolution
user-prole. The built-environment would thus be able to identify neg-
ative emotions and to attempt to mitigate their short- and long-term
effects via sensorial stimulation by, for example, reconguring spaces
to enhance particular spatial qualities and environmental conditions; or
by regulating lighting and ventilation conditions to instantiate a perceiv-
ably more tranquil atmosphere, and so on. Treur et al. have demonstrat-
ed via a computational model that strategic and targeted emotion reg-
ulation may mitigate depression in unstable people as well as mitigate
the onset of depression in highly unstable people (2014). Accordingly, it
would be pertinent for high-resolution solutions to integrate categori-
cally similar preemptive strategies into their environments.
The built-environment’s architecture would instantiate spatial comput-
ing, where computation and the local spatial properties become invari-
ably entangled, and where distance, connectivity, and density become
attributes that inuence use of space (Dulman, 2014). In this architec-
ture, intelligent systems would begin to infer and to correlate action to
reaction (and vice versa), habit to phenomena (and vice versa), and to
adapt accordingly, which is in line with Dulman’s belief that such intel-
ligent systems must be able to respond to change via self-adaptation,
where the behavior of the system, and the way users interface and
interact with them evolve over time (2014).
Conclusions
The scope of the present paper has been limited to discussing two
core considerations of the high-resolution built-environment design
paradigm. These, as well as other general high-resolution consider-
ations, are motivated by the conviction that intelligent built-environ-
ments enhance the quality of user-experience within a space; and that
they have the potential to promote the user’s short- and long-term
well-being. These considerations qualitatively and quantitatively con-
tribute to an increase in quality of life. It may be argued against the rst
conviction that user-experience is by nature subjective, and that it may
1. For example, Malcangi
(2015) has developed a bio-
metric authentication system
based on multiple ANN-mod-
els that is capable of identi-
fying individual and distinct
voiceprints. It may be conceiv-
able that such a system—or
an extension thereof—may
be adapted to discriminate in-
ections, as it already factors
a voice’s speed and stress.
2. This is imperative. The indis-
criminate application of ANN
models to given tasks may not
only result in inefciency but
also in an undesirable output.

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Alexander Liu Cheng
be difcult to conceive of individually appealing high-resolution built-environments. It may be con-
ceived, after all, that some will prefer their environments to remain static and unresponsive, even if
such design paradigms can no longer be justied neither by functional nor economic considerations.
However, the second conviction may prove more difcult to dismiss. According to Espinoza (2011),
the Organization for Economic Cooperation and Development predicts that the health expenditure in
the EU alone is expected to rise by 350% by the year 2050 compared to an economic expansion of
only 180%. This reality alone serves to promote intelligent built-environments as potential promot-
ers and extenders of health that would alleviate health-services from an unnecessarily premature
burden.
Additionally, the thought of an environment replete with intelligent sensing-actuating devices with
agency may seem chaotic, and indeed there is the risk that an interactive environment may be
counterproductive if the user is bombarded by too many reacting systems at once. In such a sce-
nario the user would become unnecessarily self-conscious of his/her actions triggering undesired
ambiance reactions accidentally, which would compel the user to micromanage every feature in his/
her environment, which may cause more problems than benets (Jaskiewicz, 2014). This represents
a challenge that cannot be overlooked, therefore, intelligent environments must be designed and
implemented in such a way as to sustain a new kind of articial ecosystem, where the environ-
ment’s components are self-sustaining, and where their development, adaptation, and evolution
occur in symbiosis with their corresponding users (Jaskiewicz, 2014). In such an ecosystem, inter-
action should be bi-directional, where the environment would not only react to a user’s action, but
where the user would react to the environment’s action as well. Citing from Fox and Kemp (2009),
Kolarevic points out that such systems do not merely ‘react’ but indeed ‘interact’ (2014) with an
environment’s variety of agents (human and robotic). The more a system and its user interact with
each other, the more attuned to one another’s agency, behavior, and corresponding effects.
Acknowledgements
This paper has proted from the contribution of Hyperbody researchers and students.

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Towards embedding high-resolution intelligence into the built-environment
Alexander Liu Cheng
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