Applications for Intelligent Information Agents (I2As):
Learning Agents for Autonomous Space Asset Management (LAASAM)

Authors:

  • Dr. James Crowder, Colorado Engineering Inc.
  • Dr. Lawrence Scally, Colorado Engineering Inc.
  • Michael Bonato, Colorado Engineering Inc.

Abstract:
Current and future space, air, and ground systems will continue to grow in complexity and capability, creating a serious challenge to monitor, maintain, and utilize systems in an ever growing network of assets. The push toward autonomous systems makes this problem doubly hard, requiring that the on-board system contain cognitive skills that can monitor, analyze, diagnose, and predict behaviors real-time as the system encounters its environment. Described here is a cognitive system of Learning Agents for Autonomous Space Asset Management (LAASAM) that consists of Intelligent Information Agents (I2A) that provide an autonomous Artificially Intelligent System (AIS) with the ability to mimic human reasoning in the way it processes information and develops knowledge [Crowder 2010a, 2010b]. This knowledge takes the form of answering questions and explaining situations that the AIS might encounter. The I2As are persistent software components, called Cognitive Perceptrons, which perceive, reason, act, and communicate. Presented will be the description, methods, and framework required for Cognitive Perceptrons to provide the following abilities to the AIS:

    1. Allows the AIS to act on its own behalf;
    2. Allows autonomous reasoning, control, and analysis;
    3. Allows the Cognitive Perceptrons to filter information and communicate and collaborate with other Cognitive Perceptrons;
    4. Allows autonomous control to find and fix problems within the AIS; and
    5. Allows the AIS to predict a situation and offer recommend actions, providing automated complex procedures.
A Cognitive Perceptron Upper Ontology will be provided, along with detailed descriptions of the I2A framework required to construct a hybrid system of Cognitive Perceptrons, as well as the Cognitive Perception processing infrastructure and rules architecture.

In particular, this paper will present an application of Cognitive Perceptrons to Integrated System Health Management (ISHM), and in particular Condition-Based Health Management (CBHM), to provide the ability to manage and maintain an AIS in utilizing real-time data to prioritize, optimize, maintain, and allocate resources.

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