AI Magazine - Fall 2019

translated into a different pulse width or antenna rotational speed.

Finally, the internal configuration logics are used to provide the adaptation module with the information it needs to plan the adaptive actions. The mission of the online reconfiguration module is to change the internal structure of the fusion solution to satisfy the goals or better respond to external circumstances (context). Therefore, the relevant context and changes in the goals are the two sources used to decide actions over the structure of the fusion solution — activation/deactivation of fusion nodes or changes in the control flow.

Conclusions
The article identifies handling of CI and an appropri-
ate situation representation as key elements to develop
adaptive and explainable AI systems. Although AI
is certainly present in current applications and has
become a popular area of computer science, in part
because of business investments and media coverage
of AI success in games, some open challenges have
been discussed to motivate progress in this area,
called the next milestone or the third wave of AI.
Whereas previous milestones have been character-
ized by manually crafted knowledge (expert systems)
and statistical machine learning, the open challenge
is now to learn paradigms with explainable models
and context adaptation to gain generalization capa-
bility. Explainable AI can benefit from IF develop-
ments, context enhancement, and decision support
systems.

AI must address many problems that are current in the area of IF: perception and object recognition from sensory data or SAW. A basic challenge identified for both AI and IF future systems is understanding context, the ability to represent and relate how relevant the context is to the inference problems addressed, and mechanisms to adapt the inference processes to this context. In this parallelism, the challenges include perception, reasoning, and adaptation toward deploying AI and IF systems to support knowledge representation and situation understanding.

The preeminent challenge is context adapta-
tion common to both AI and IF research. A basic
objective is the capability to learn interpretable
models from contextual data to bind observations
with knowledge and use the semantics provided by
context. Current strategies for situation assessment
and HLIF have been reviewed and related with the
ongoing research in AI branches of computer sci-
ence (compositional learning models, cognitive
architectures, common-sense reasoning, and ra-
tional agents). A general approach for AI systems
should be based on conceptual representation to
allow automatic adaptation to domain conditions.
Future breakthroughs will stem from an architec-
ture to represent, access (middleware), and exploit
the context in IF processes that provide context-
enhanced systems.

Notes

1. See www.darpa.mil/program/explainable-artificial-intelligence.

2. Terms like information integration have been preferred by some to connote greater generality than earlier, narrower definitions of data fusion (and, perhaps, for distance from old data fusion approaches and programs), but such manipulations do not contribute toward better representation or understanding.

3. Quality is another term for which it is difficult to achieve consensus; data quality has been written about for general applications (Sycara et al. 2009) as well as for IF-specific applications (Zadeh 1983). We address the quality issue as part of the general content of this article in various ways.

4. A definition of a priori is “formed or conceived beforehand,” and here we mean it in the sense of incorporating contextual aspects in a design at the outset.

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