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Architecture and Engineering of Self Aware Computer Systems

Explore the capabilities and knowledge bases needed to support self-aware computer systems. Learn about traces and explanations, including access to trace, abstraction of trace, and follow-up question capabilities. Discover the importance of ontologies and meta-information in providing explanations and informed information. Additionally, delve into the role of ontology and knowledge bases in self-aware computer systems.

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Architecture and Engineering of Self Aware Computer Systems

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  1. Architecture and Engineering of Self Aware Computer Systems Deborah McGuinness Stanford University

  2. Questions • What explanation capabilities should be supported by self-aware computer systems • What knowledge bases and ontologies are necessary to support self-aware computer systems

  3. Traces and Explanations • provide access to trace • provide abstraction of trace • may take trace as input • may use rewrite rules • provide follow-up question capabilities for more information • may use description of information manipulation (could be declarative representation of reasoning rules) • may use background domain ontologies with subject matter expertise • may use meta information (update date, authoritativeness, author, ….) • provide examples and counter examples • provide explanations for failures as well as success

  4. Explanations cont. • provide information informed by use of model of environment • may not include knowledge of other agents but physical information • provide information informed by use of model of other interacting agents • may use model of known recipient of explanation for presentation style, content, etc. • might include preferences, kb, reasoning strategies, context, what has been explained before • may be interruptible • may use description of plans, goals, known failures, intentions, beliefs, …. to present summary of current state, • provide information about alternatives • provide information about conflicting information • e.g., explanation of why x is red could offer an explanation of why x is not red if that is also deducible in the current state • may use language information (computer language e.g. OWL, presentation language (e.g., English, KIF, graph, …) • Make assumptions explicit • Explanations aimed at agents

  5. Ontologies and KBs • Upper level “general purpose” ontologies (SUMO, Cyc Upper Level, Dolce….) • Domain specific upper/mid levels (UNSPSC) • Domain specific upper/mid/&low levels (UMLS) • Domain specific KBs (large, current, authoritative.. Might consider CIA World Fact Book, crawled generated data, …) • Meta Information (dublin core properties, IW Registry, …) • Mental model ontologies

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