Anna Koop

Iteration eleventybillion of my proposal draft. Comments of all kinds welcome. I think I need to support some of the statements therein and talk up some of the “why”s but I’m not sure how much is needed in the abstract (or if proposal really should have abstracts)

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Update: proposal toned down in scope and claims, oddball definition of data disposed of, algorithm component added back (extending what work we have on empirical knowledge representation to identified areas of interest).

No abstract in the new version but I’ll post a summary soon.
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The problem of how to represent general knowledge in artificial systems remains unsolved. There have been many different approaches to knowledge representation, but these approaches are difficult to compare. No universally satisfactory solution has been found.

For the first part of my thesis research, I aim to analyze the strengths and weaknesses of a broad range of approaches to knowledge representation. I will use an inclusive definition of knowledge as the general, abstract, stable stuff of the mind. This allows me to consider a wide range of knowledge representation: from the logical knowledge bases of good old-fashioned AI to the models of control theorists and discriminative functions of supervised learning. An analysis of knowledge representation that takes such an inclusive stance is rare, as research generally focuses on the fine points of representational detail within a subfield.

I will be investigating the problem of knowledge and data: how the content of knowledge should be related to the data of sensorimotor experience. Existing analyses of knowledge representation frameworks focus on differences in structure and ignore differences in semantics: what the knowledge is meant to represent. I will be comparing knowledge that is concerned with representing objective reality, which is by far the dominant approach in artificial intelligence, to knowledge that is concerned with representing patterns in sensorimotor experience, a relative newcomer in AI.

I hypothesize that both of these approaches to the meaning of knowledge have distinct practical benefits. Knowledge about objective reality lends itself to general, abstract, and stable content, which I have given as definitive characteristics of knowledge. At the same time, knowledge about objective reality seems to require an external source of data for grounding and verification. Knowledge about empirical experience should have an easier task in grounding and verification, being about internally accessible data. However, constructing general, abstract and stable content from the ephemera of sensorimotor signals seems problematic.

Having completed the analysis and clearly identified the strengths and weaknesses of these two semantic approaches, I will propose developing an empirical representation that allows for the construction of general, abstract, and stable concepts. This will build on previous work in my Master’s thesis and provide a strong foundation for the emerging field of empirical knowledge representation.