Inference Web: Inference Web: Portable and Sharable Proofs Portable and Sharable Proofs

for Hybrid Systemsfor Hybrid Systems

Deborah L. McGuinness, Paulo Pinheiro da Silva and Bill MacCartney

with Richard Fikes, Gleb Frank, Jessica Jenkins, Rob McCool, Yulin Li

Knowledge Systems Laboratory

Stanford University

http://www.ksl.stanford.edu

{dlm | pp} @ksl.stanford.edu

McGuinness, Pinheiro da Silva, and MacCartney, 2003 2

Motivation - TRUSTMotivation - TRUST

If users (humans and agents) are to use and integrate system answers, they must trust them.

System transparency supports understanding and trust.

Thus, systems should be able to explain their actions, sources, and beliefs.

Also, if systems are hybrid, it is useful to work in an integrated yet separable manner.

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Technical Infrastructure ReqsTechnical Infrastructure Reqs

Provenance information - explain where source information: source name, date and author of last update, author(s) of original information, trustworthiness rating, etc.

Reasoning information - explain where derived information came from: the reasoner used, reasoning method, inference rules, assumptions, etc.

Explanation generation – provide abbreviated descriptions of the proof – may include reliance on a description of the representation language (e.g., DAML+OIL, OWL, RDF, …), axioms capturing the semantics, rewriting rules based on axioms, other abstraction techniques, etc.

Distributed web-based deployment of proofs - build proofs that are portable, sharable, and combinable that may be published on multiple clients, registry is web available and potentially distributed, …

Proof/explanation presentation - Presentation should have manageable (small) portions that are meaningful alone (without the context of an entire proof), users should be supported in asking for explanations and follow-up questions, users should get automatic and customized proof pruning, web browsing option, multiple formats, customizable, etc.

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Inference WebInference Web

Framework for explaining reasoning tasks by storing, exchanging, combining, annotating, filtering, segmenting, comparing, and rendering proofs and proof fragments provided by reasoners. DAML+OIL/OWL specification of proofs is an interlingua for proof

interchange

Proof browser for displaying IW proofs and their explanations (possibly from multiple inference engines)

Registration for inference engines/rules/languages

Proof explainer for abstracting proofs into more understandable formats

Proof generation service for facilitate the creation of IW proofs by inference engines

Prototype implementation with Stanford’s JTP reasoner and SRI’s SNARK reasoner

Discussions with Boeing, Cycorp, Fetch, ISI, Northwestern, SRI, UT, UW, W3C, …

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IW Browsers

Registrars

World Wide Web

Registry entries

Inference Web ArchitectureInference Web Architecture

proof fragments

non-IW documents

Webagent

Webdocument

URLreference

Agent dependency

CaptionDocumentmaintenance

Inferenceengines

Reasoneragent

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IW Registry and RegistrarIW Registry and Registrar IW Registry has meta-data useful for disclosing data provenance and

reasoning information such as descriptions of

inference engines along with their supported inference rules

Information sources such as organizations, publications and ontologies

Languages along with their axioms

The Registry is managed by the IW Registrar

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Inference Engine Registration Inference Engine Registration (1)(1)

An entry for SRI’s SNARK engine An entry for SNARK’s Binary Resolution inference rule

Engine registration involves the creation of an engine entry and its association with entries of inference rules

Rule entries can be either reused or added to the registry

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Inference Engine Registration Inference Engine Registration (2)(2)

Otter’s binary resolution, hyper-resolution and paramodulation rules were reused for the registration of SNARK

Assumption and negated conclusion rules were added for SNARK

Rule reuse

addition

addition

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Inference Engine Registration Inference Engine Registration (3)(3)

Summarizing the Inference Engine Registration process:

Use the registry to include meta-information about the engine and its rules

Add an entry for the new inference engine

Identify the core inference rules supported by the engine

Add unregistered core inference rules, if any

Associated the core rules with the core inference engine

Prepare the engine to dump proofs in the IW format

Implement a routine for calling the proof generator service

Example routines in Java and Lisp can be provided

Publish successful results of the proof generator services in portable proof format (OWL/DAML/RDF/XML compliant files)

Browse your proofs in the IW Browser

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Generation of IW proofsGeneration of IW proofs

Reasoner Proof fragments

Registry

Registrar

WWW

Proof generator service

(1) Send node information:reasoner ID, labelingsentence in KIF, ruleID, antecedent URIs,bindings, and sourceID (2) Verify information

(3) Return proof fragments

(4) publish proof fragments

(can collect statistics, provide feedback,…)

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Integration with SNARKIntegration with SNARK

Done by non-SNARK author to test strategies for integration

Tests alternative reasoning strategy – proof by contradiction

No special modifications made as a test of leverage

Learned some new requirements (CNF processing, reasoning modes may be useful, …)

Initial integration fairly easy

More complete integration in process

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SNARK Example: nuclear threatsSNARK Example: nuclear threats

(1) ore refiner material

(2) black-mkt material

(3) black-mkt ore

(4) black-mkt ore

(5) material detonator casing warhead

(6) material warhead

(7) detonator warhead

(8) casing warhead

(9) warhead missile nuke

(10) warhead truck nuke

(11) missile truck

“Weapons-grade nuclear material may be derived from uranium ore if refining technology is available, or it may be acquired from a black market source. Foobarstan is known to have either uranium ore or a black market source, but not both. Foobarstan will build a nuclear warhead if and only if it can obtain nuclear material, a detonator, and the bomb casing. A warhead and a missile, or a warhead and a truck, constitute a nuclear threat. Foobarstan has either a missile or a truck.”

QUESTION: Is Foobarstan a nuclear threat?

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Example: proof by contradictionExample: proof by contradiction

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Example: a proof treeExample: a proof tree

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An example in FOLAn example in FOL

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Registering SNARK: next stepsRegistering SNARK: next steps

Add support for ‘source’ and ‘author’ fields Match with IW-registered ontologies where possible

Standardize treatment of SNARK rewrites When do rewrites correspond to resolution, hyperresolution,

paramodulation?

Utilize SNARK rewrites for IW abstraction strategies

Consider tableaux approaches for explanation

Implement correct handling of SNARK procedural attachments SNARK includes procedural attachments for math, lists

User can define new procedural attachments on the fly

This constitutes an inference rule with an open-ended definition

Track variable bindings through course of proof

Integrate IW interface into SNARK standard release

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Conclusion/Next StepsConclusion/Next Steps Proof specification ready for feedback/use

http://www.ksl.stanford.edu/software/iw/

Proof browser prototype operational and expanding Recent: ground axiom collection, source doc/ontology collection,

aggregation view

Current: multiple formats, simplification, pruning, …)

Registration service expansion - integration with XML database, use in EPCA, registration of services (with Fetch)

Inference engine integration work – further rewrites for JTP (temporal reasoner), SNARK, begin with KM – explanation style for HALO.

Integration with web services – current: KSL Wine Agent, KSL DQL client (NIMD implementation), begin with registration of web services (Fetch)

Documentation – more examples, etc. More comments solicited (thanks to

date to some for comments including Berners-Lee, Chalupsky, Chaudhri, Clark, Connolly,

Forbus, Hawke, Hayes, Lenat, Murray, Porter, Reed, Waldinger, …)

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ExtraExtra

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Proof browsing: an example Proof browsing: an example (1)(1)

Tools can be used for browsing IW proofs. The following example demonstrates the use of the IW Browser to visualize, navigate and ask follow-up questions.

Lets assume a Wines ontology:

Determination of the type of a concept or instance is a typical problem on the Semantic Web. A reasoner may ask either about the type of an object and may also ask if an object is of a particular type Example Query: (rdf:type TonysSoftShell ?X)

Example DAML KB: <rdf:RDF

xmlns:rdf =“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"><rdf:Description rdf:ID="TonysSoftShell">

<rdf:type rdf:resource="#CRAB"/> </rdf:Description>  <rdfs:Class rdf:ID="CRAB">

<rdfs:subClassOf rdf:resource="#SHELLFISH"/> </rdfs:Class>  <rdfs:Class rdf:ID="SHELLFISH">

<rdfs:subClassOf rdf:resource="#SEAFOOD"/></rdfs:Class> </rdf:RDF>

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Proof browsing: An example Proof browsing: An example (2)(2)

Browsers can display portions of proofs.

Selecting premises users can navigate throughout proof trees.

Proof browsing: An example Proof browsing: An example (2)(2)

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Trust DisclosureTrust Disclosure

IW proofs can be used:

to provide provenance for “lookup” information

to display (distributed) deduction justifications

to display inference rule static information

Trust DisclosureTrust Disclosure

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Technical RequirementsTechnical Requirements annotate information with meta information such as source, date,

author, … at appropriate granularity level (per KB, per term, …)

explain where source information is from

explain where derived information came from

prune information and explanations for presentation (utilizing user context and information context for presentation)

provide a query language capable of expressing user requests along with filtering restrictions

provide a ubiquitous source annotation language

provide a ubiquitous proof language for interchange

Compare answers

propagate meta information appropriately (if I got something from a source I consider trusted and you consider me a trusted source, you may want to consider my source trusted as well)

Identify multiple (or unknown) truth values