Six State Logic Makes Tomorrow’s Networks Optimal

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This podcast introduces the concept of a 6SL Search Network. The 6SL Search Network enables human preference driven, autonomous communication between machine agents as described in my Podcast 8 (https://youtu.be/2aQifJ4nEpQ). This Podcast 10 describes the optimization of the 6SL Search Network by application of the 6SL Search functionality introduced in Podcast 9 (https://youtu.be/C4z__p8v98A) to:

  • Determine the priority order of communications between machine agents,
  • Determine the priority order of human communications with machine agents
  • Assure the 6SL Search Network functions well regardless of the level of uncertainty of both search preferences and search topic data.

A human’s machine agent can maintain an unlimited number of parallel communications with other machine agents about any number of topics. Humans are more limited so 6SL search uses current human preferences and search topic data to prioritize incoming communications for humans and outgoing communications for machine agents.

For example, because it was a high priority for it’s human, a machine agent could have worked for weeks to find and maintain a list of car purchase opportunities. When the importance of a car purchase was reduced by the human, the machine agent would terminate car purchase related communications with other agents and move on to other communications that reflect most current human preferences.

To prioritize 6SL Search Network communications, 6SL Search calculates the average difference between search preferences and search topic data for every potential communication opportunity. Satisfaction is 100% when the preference and search topic data are an exact match and  0% when the preference and search topic data are at opposite extremes.  The satisfaction for unknown preferences and/or unknown search topic data falls between these two satisfaction extremes. The search result is a relative satisfaction ranking of all potential communications for both humans and machine agents.

Calculating satisfaction as a rank order results instead of an absolute value enables the 6SL Search Network to function well even when many search preferences and/or search topic data are uncertain. By design, the 6SL Search Network functions well in both this type of extreme uncertain environment as well as in the opposite environment where search preferences and search topic data are well known and actionable.

This Podcast describes how the 6SL Search Network prioritizes communications for machine agents and humans in a way that enables independence of search preferences and search topic data. This means that the 6SL Search Network can be infinitely extensible to manage constantly changing preferences from humans and constantly changing search topics data from any number of sources.

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