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The Web of Smart Entities 125

artificial intelligence (AI) techniques. We imagine that when gathering data
from different scenarios to form an overarching model there will be incon-
sistencies. Detecting and possibly resolving inconsistencies or conflicts can
be accomplished with AI techniques such as proof checkers. The connected
nature of WSE requires further, perhaps more mundane uses of AI tech-
niques. In this section, we will highlight some of these, as they suggest addi-
tional benefits from WSE.
Constraint satisfaction. The most obvious use of constraint satisfaction is
when more than one person occupies the same space. Consider temperature
settings, light settings, or entertainment choices that need to be resolved.
A more sophisticated example involves regulating sleep. With the creation
of smart beds and wearables, it is possible to monitor people’s sleeping pat-
terns. A model of sleeping patterns informs whether one is getting enough
sleep each night. The sleep model can interact with several systems in an
attempt to regulate sleep. For example, it could be empowered to regulate
the temperature in the bedroom. It could interact with the meal planner to
detect foods or drinks that are not conducive to sleep. It could be empow-
ered to remove or rescheduled these items to earlier in the day. The sleep
model could interact with the calendar to reschedule certain kinds of phys-
ical exercises that are detrimental to sleep.
Recommender system. Given models of people’s behavior, we are in a posi-
tion to make recommendations. For example, the “yummly.com” website
makes recommendations based on the preferences entered by a user. We
imagine that in the future recommendations can be made based on matching
a user’s meal-time recipe usage to those of others. This matching would be
similar to how Netflix and Amazon.com recommend movies and goods.
Similarly, based on a user’s exercise patterns, we imagine recommendations
for modifications, additions, or substitutions of exercise regimes.
Epidemics. Automatic collection and consolidation of health data will
enable public agencies to detect developing trends in real-time ( Jalali, Ola-
bode, & Bell, 2012). Since time is of the essence in formulating a response,
the more real-time data that is available, the faster one can detect trends. On
a more local scale, it will help health-care providers in a given community to
determine what sort of illness is afflicting their patients, enabling them to act
accordingly.
Cognitive assistants. Cognitive assistants, as proposed by IBM (Kelly,
2015), are aimed at digesting vetted data to provide additional information
to health-care providers. IBM sees cognitive assistants as “wise counselors”
(IBM Watson, 2012). As IBM sees it, “IBM Watson, through its use of
information retrieval and natural language processing, draws from an