Abstract
Humans possess only five senses and very effectively coordinate their cross-sensory perceptions to situate themselves in uncertain operational environments for extracting context relevant actionable intelligence. Machines, on the other hand, may be embodied with a wider variety of electronic sensing devices but lack such situational intelligence in interpreting the sensed information. Despite significant advances in sensing technologies, machine perception remains primitive when compared to human perception. Lack of situational intelligence results in processing of large amounts of irrelevant information, leading to the often cited “curse of dimensionality” and computational explosions. These, in turn, limit the power of datadriven abstract reasoning and problemsolving algorithms, cause a lack of focus for drawing upon relevant past knowledge, and inhibit situational learning. As a consequence, autonomous systems cannot be trusted to adapt their behavior to unanticipated operational conditions. Current behavior-based modeling approaches address these issues by developing world models that modularly decompose the problem space. This requires a very detailed and somewhat complete understanding of the operational environments as a prerequisite. Yet, such models invariably prove inadequate for real world operations due to the rigidness of the decompositions. Autonomous system designs, therefore, are not robust and machine learning methods remain brittle. Situationally aware sensor fusion and machine perception present a new frontier in machine automation, which holds the promise of unprecedented levels of autonomy in executing complex tasks in dynamic operational environments. The goal of such automation is to accomplish these tasks with the perception and adaptation of humans, and often in collaboration with humans. Several technological challenges must be addressed to further the state-of-the-art toward this goal.
Highlights
CONTEXT LEARNING AND IN SITU DECISION ADAPTATION Faced with the challenge of data to action in a complex noisy world, research methods have emerged in diverse fields, over the past decade, for machines to extract current operational context from sensor data
Machine perception and learning contexts that were not labeled during the training phase, and dynamic modeling of context drift, remain promising research areas for improving machine perception and machine learning via situational intelligence
CROSS-SENSORY FUSION Today’s intelligent machines operate on a sensing infrastructure for measurement, communication, and computation with which they perceive the evolution of physical dynamic processes in their operational environment
Summary
CONTEXT LEARNING AND IN SITU DECISION ADAPTATION Faced with the challenge of data to action in a complex noisy world, research methods have emerged in diverse fields, over the past decade, for machines to extract current operational context from sensor data. An alternate approach to improving detection performance is to exploit differences in sensor behaviors across environments and treat them as a supplemental source for context-dependent-learning. Formalizing this approach, a mathematical characterization of machine extractable context, applicable to all sensing modalities relevant to an application, was recently presented in Phoha et al (2014), with the objective of enabling contextual decisionmaking in dynamic data-driven classification systems.
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