Collaborator login
New collaborator registration
Forgot password

Welcome to Representing Knowledge with Primitives

Computers should help people deal with everyday problems from making decisions to playing games. To offer more help, computers need more complete representations of knowledge, defined as useful incorporated information. This web site provides tools to collaborate on creating more complete representations, starting with textual natural language.

Hypothesis

This research web site explores a hypothesis about representing knowledge:

Conditional probabilities
Conditional probabilities deal with uncertainty in information, ambiguity, vagueness, and approximation. Probabilities factor in utilities, which principled Decision-Analytics may use to choose the best among an exponential quantity of processing alternatives. Probabilities subsume other uncertainty measures and include higher-order probabilities about the measures themselves.
with high-order logic expressions
To get an expressive and fine-grained enough representation, conditional probabilities, which are statements about events, can use complex logic expressions, including high-order logic. Logic can express negation and the ignored stop words of other approaches. Providing a unified methodology, high-order logic can express intent, including subjunctive hypotheticals, along with other moods without using modal logic; processes; and contexts like frames.
with predicates from a limited set of primitives
Predicates in logic expressions can come from a limited set of templates, called primitives, which parameterize values, e.g., numbers and strings. Specific to generic relations among conditions provide taxonomies. Expressions can be compared and manipulated to handle analogies.
can tractably represent common sense knowledge.
This combination should lead to new processing capabilities, despite the unbounded nature of ideas, the complexity of ideas, and the uncertainty of the ideas communicated with natural language.

Tractable means easily managed or controlled (probabilistic) rather than running in polynomial time (deterministic).

Benefits

Knowledge Representations drive the implementations of general purpose computing, called Artificial Intelligence as it approaches human capabilities. An implementation of the hypothesis can satisfy goals for Knowledge Representation of being: helpful, understandable, principled, parsimonious, small, and secure. Many classes of use cases could use this implementation:

An implementation of the hypotheses could support several requirements that can be derived from these use cases: capability, access, context, uncertainty, judgment, adaptability, efficiency, and possibly scale. The comparison of methods page gives more details.

Getting started

Collaborators are welcome to add their thoughts about how anything may be described. Such philosophical insights may be given without specific consideration of the logic, probability, or algorithms that handle them.

Register to read more. Make an edition to collaborate. As desired, add primitives or condition groups with outcomes.

This introduction, comparison of methods, glossary, references, and site implementation pages are openly available. However, most pages are only available to registered collaborators.

Collaborators register with the site providing at least postal codes, such as a U.S. ZIP code, which provides a locality, and E-mail addresses so that a site administrator may authenticate them. Since authentication is manual, new potential collaborators may gain full access slowly, particularly if the information provided is minimal.

Except for a self-assigned identifier string, all collaborator information is only available to administrators, unless collaborators choose to share their information with other authenticated collaborators.

Site navigation

Only a few pages are visible without logging in.

Copyright © 2016 Robert L. Kirby.  All rights reserved.