While there is considerable progress on the
approaches currently being explored and several
emerging capabilities, there are still significant tech-
nical challenges and advances needed to enable the
seamless integration of humans and technology.
One major challenge is to build a library of
reusable algorithms that can be applied across multiple domain models. A shared infrastructure and data
representation is a necessary condition but is not sufficient for the implementation of algorithms that
cross-cut multiple application types. Within CCRF it
is possible to formulate the model for a particular
domain in many different ways, depending on the
emphasis of the system. It cannot be assumed that an
algorithm developed for one domain model will
function with another. One possible way to address
this problem will be to define data contracts for algorithms that state the types and structure of the data
inputs and outputs, and to provide a translation layer that maps variables within a domain model to the
variables used within the algorithm. This would
essentially allow a generalized algorithm to be bound
to a specific CCRF domain model assuming all the
necessary data is available and the algorithm’s problem formulation makes sense for the domain. Another goal is to use machine learning to adapt the system
interactions to individual differences over time. In
the near term, we wish to conduct several evaluations
to collect more quantitative performance data.
We have presented a framework for leveraging
context to support human-machine collaboration to
enhance performance. First, we provided a working
definition of context that has enough flexibility to
work across several domains and accommodate new
data and processes. The algorithms that operate in
these human or automation team systems often have
use across multiple types of applications and
domains. If each application develops its own representation and implementation of context from
scratch, then each application will also have to
implement its own algorithms. Having a common
application or domain-agnostic representation of
context will allow application developers to focus on
defining the important concepts needed for their
application rather than develop a custom software
implementation. They will also have access to a
library of algorithms that have been developed to
work with this common representation. Working
from this definition, we presented a framework for
representing, modeling, and reasoning about context. Finally, we discussed an example application
where we leveraged our framework to build a context-aware system.
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