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Mitigating the Inevitable Failure of Knowledge Representation 14

Mitigating the Inevitable Failure of Knowledge Representatio...

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14th IEEE International Conference on Autonomic Computing (ICAC)

作者： Landauer, Christopher Topcy House Consulting Thousand Oaks CA 91320 USA

ISBN: (纸本)9781538617625

This paper is a continuation of a previous paper on self-modeling systems, concerning mitigation methods for the Get Stuck Theorems, which are powerful theorems about the limits of knowledge representation. The First Get Stuck Theorem says that since there are only finitely many data structures of any given size, it follows that as a system tries to save more and more data / information / knowledge, the structures necessarily get larger, and eventually they are too large for effective computation. The mitigations we described are Behavior Mining, which is about building models of the system and environment behavior, model deficiency analysis, which is about assessing the efficacy of those models and determining how to improve them, Knowledge Refactoring, which is about restructuring the saved data for more efficient access and smaller storage, and Constructive Forgetting, which is about explicitly discarding some data that is deemed to be less critical. We argue that these classes of mitigations, and a couple of new ones, can help a system retain effectively computable knowledge structures in a dynamic environment at higher levels of difficulty (of course, as the environment gets more dynamic, all systems, including individual biological organisms and even species, eventually fail).

关键词： Self-Aware Systems Self-Adaptive Systems Self-modeling Systems Get Stuck Theorems Behavior Mining model deficiency analysis Knowledge Refactoring

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model-Based Cooperative System Engineering and Integration 13

Model-Based Cooperative System Engineering and Integration

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13th IEEE International Conference on Autonomic Computing (ICAC)

作者： Landauer, Christopher Bellman, Kirstie L. Topcy House Consulting Thousand Oaks CA 91362 USA

ISBN: (纸本)9781509016532

This paper is about some difficult issues in systems of systems engineering, especially when the systems are self-modeling. The questions are about which of the available participant systems have the collection of capabilities desired, how to use them to provide those capabilities, and how to avoid introducing new error modes that emerge from the interaction. In this paper, we describe the process of designing and constructing a system of systems, starting with a provided mapping from the perceived need to a coordinated set of system capabilities that can address that need, some of which are missing capabilities for which new systems are needed. The system engineering aspects of this problem are difficult: the available information about the component system capabilities is always incomplete and often inconsistent, and there can be only approximations of the range of environmental behaviors. For these applications, the design will have to change as the environmental conditions change. These systems are in "continual dynamic development", which is why we think the systems should manage and perform the integration tasks themselves. This paper shows one way to do that.

关键词： Computational Reflection Wrapping Infrastructure Self-Monitoring Self-Improvement Self-Integration Cooperative System Engineering model deficiency analysis

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