Building and Using Large Common Sense Knowledge Bases
A project in the DARPA High Performance Knowledge Base Initiative
Principal Investigator: Kenneth
Research Scientist: Ron Ferguson
At the conceptual level, qualitative representations provide an
ideal medium for expressing the framework knowledge of the physical world
that both provides simple inferences directly and also organizes and orchestrates
the use of more detailed knowledge.
- At the level of reasoning methods, analogical reasoning techniques
based on the best available theories in cognitive science can provide more
human-like case-based reasoning systems.
- At the level of interaction media, the use of sketching can provide
a more natural way for domain experts and software to communicate about
the spatial aspects of concepts and situations.
- We propose to create a set of fundamental
domain theories, focusing on common sense physical phenomena, that
can be used as components in knowledge bases for multiple purposes, including
effects-based planning and intelligence analysis tasks. These fundamental
domain theories will include aspects of space, quantity, time-varying behavior,
substances, physical processes, and causality. We will use these domain
theories to develop higher-level domain theories, include developing the
common sense concepts needed to model phenomena involving weather and climate,
civilian logistics distribution systems, and basic ideas of vehicles.
- We propose to develop a set of analogical
processing systems that can serve as a foundation for a new generation
of case-based reasoning systems. Our goals are to provide retrieval
times in the 10-20 second range even on 10,000 entry libraries of structured
propositional representations, and to enable domain experts to extend CBR
systems without having to understand the system's internals.
- We propose to develop sketching
as a tool for knowledge acquisition and refinement. Our goal is
to make sketching as natural a modality for explaining things to machines
as it is for explaining things to people, concentrating on the specific
tasks of defining new spatial recognizers and interactive explanation of
concepts with spatial aspects.
- We propose to create a Domain Theory Development Environment. Our goal is to create an environment that can handle 100,000 axiom-equivalent knowledge bases in a high-end networked computational environments, but is still fieldable on notebook computers for medium-scale tasks and knowledge bases, allowing the environment to be brought to the domain experts as well as the other way around.
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