World wide welding machine transforming data into proofs

2022-06

https://josd.github.io/Talks/2022/06welding/

by Jos De Roo of KNoWS office of IDLab - Ghent University - imec.

We want inter-engine interop which requires proof: SemWave diagram from Tim Berners-Lee

We need the full potential of the Semantic Web Databus and Proofbus from Tim Berners-Lee

Use case GPS4IntegratedCare

The GPS4IntegratedCare project objective is Automatic generation of dynamic and personalized care workflows

Technologies used:

• Semantic Web Language (JSON-LD, Turtle, N3)
• Reasoning Engine (EYE)

Goal driven Parallel Sequences (GPS):

• Inspired by Linear Logic
• "In linear logic we are instead concerned with the change of truth with a change of state. We model this in a very simple way: when an inference rule is applied we consume the propositions used as premises and produce the propositions in the conclusions, thereby effecting an overall change in state."
• Implemented in GPS plugin for EYE

Use case GPS4IntegratedCare lessons learned

The project worked out fine, but the architecture is centralized around a single smart workflow engine and that is really not scalable.

The proposal is to address the scalability with MAP

Multi-Agent Proofs (MAP):

• Multiple agents can work together by using their own knowledge/logic/data and proofs made by other agents.
• The proofs are guaranteeing a transparent and accountable way of working and they only disclose what is relevant, so there is no need to have an "All knowledge is contained in here" (what I learned from Tim Berners-Lee).

This is just a proposal with a proof of concept in which agent1 and agent2 are GPS agents and agent2 reaches his goal thanks to the lemmata made by agent1.

Multi-Agent Proofs: Agent2 reaches his goal thanks to the lemmata made by Agent1

Agent2-proof makes use of lemma9 from Agent1-proof

<#lemma13> a r:Extraction;
  r:gives {
    <http://josd.github.io/eye/reasoning/map/agent1-proof.n3#lemma9> a r:Inference.
  };
  r:because [ a r:Parsing; r:source <http://josd.github.io/eye/reasoning/map/agent1-proof.n3>].

<#lemma14> a r:Extraction;
  r:gives {
    <http://josd.github.io/eye/reasoning/map/agent1-proof.n3#lemma9> r:gives {
      :map-BE gps:description ({:i1 :location :Gent} true {:i1 :location :Brugge} :drive_gent_brugge 1500.0 0.006 0.96 0.99)
    }.
  };
  r:because [ a r:Parsing; r:source <http://josd.github.io/eye/reasoning/map/agent1-proof.n3>].

Reverse the burden of proof

The burden of proof is now on the server:

• the server has to find out why he should do the job for the client
• but there is no omniscient server

Reverse the burden of proof:

• the client provides a proof
• the server checks that proof
• if the proof is fine the server does the job for the client

This is much more scalable but requires client side reasoning + proof generation

The proof could be provided as HTTP GET payload

Thank you

Thank you for your attention

https://josd.github.io/Talks/2022/06welding/