91723.3.2 / Public / Last updated today
Install: pkg i dwim-dweomer
License: Cognitech Open Usage & Modification License (Commercial & Non-Commercial)
Included-In: affective-interface, task-core, thinker-core, command-core, animating-core (see 37 others)
Depends-On: species-basics, culture-basics, era-basics, psych-generic, psych-loader (see 887 others)
The dwim-dweomer package contains the core routines of Cognitech’s Do What I Mean™ user-interpretation subsystem for user interface fluency and artificial intelligence alignment.
If you are developing for a system that makes use of context preferential interfacing, SQUID data, or other direct mind-state input, do not use this package. Use dwit-dweomer instead. If the system is intended to operate autonomously, consider using extrapolated-volition or coherent-extrapolated-volition in conjunction with this package or dwit-dweomer.
The dwim-dweomer package incorporates and integrates multiple models (based on extensive sophological, sociodynamic, and cliological studies) of sophont thought categorized by species, culture, altculture, current era, and so forth, including detailed information on thus-localized preferences and values. It cross-correlates requests with the standard world-model provided by the Imperial Ontology (or other supplied world-model), enabling it to better interpret user requests and validate them against identifiable probable user dislikes or those of world-entities of significance.
Callbacks in dwim-dweomer (required to be implemented) enable the package to report on, and request and require confirmation for, potentially problematic divergences between the implementation of the request and the package’s model of the user’s model of the implementation of the request.
Predictive modeling (enabled by hooks into the developed system) also allows the package to extrapolate when the user request would have been otherwise had the user been in possession of further information available to the AI, and report on these for confirmation also.
The dwim-dweomer package itself includes only generic modeling. For better modeling, we recommend using the dwim-dweomer-profile package, which integrates a per-user preference learning model permitting the AI to understand the variation in preferences and values of individual users. While capable of operating independently (for secure applications), dwim-dweomer-profile is capable of using shared preference learning models attached to one’s Personal File. This adds ucid, ucid-auth, and ucid-profile to the required dependencies, and the shared models can only be applied once the user has been authenticated and authorized.
dwim-dweomer-profile can also be configured to apply multiple per-user preference models in conjunction with a variety of consensus-priority-negotiation systems, a mode designed for use in applications such as house brains and office managers.