Cse@buffalo Research in Knowledge Representation, Reasoning, and Acting Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy.

cse@buff

alo Research inKnowledge Representation,

Reasoning, and ActingStuart C. Shapiro

Professor, CSEAffiliated Professor, Linguistics, Philosophy

Director, Center for Cognitive Science

Director, SNePS Research Group

ACM Distinguished Scientist

Fellow, AAAI

Faculty Member:

Interdisciplinary MS in Computational Linguistics

Center for MultiSource Information Fusion

National Center for Ontological Research

National Center for Geographic Information and Analysis

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Long-Term Goal• Theory and Implementation of

Natural-Language-CompetentComputerized Cognitive Agent/Robot

• and Supporting Research inArtificial IntelligenceCognitive ScienceComputational Linguistics

• with application-oriented spinoffs.

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Cassie

• A computational cognitive agent– Embodied in hardware– or software-simulated– Based on SNePS and (M)GLAIR.

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KL (SNePS)

PMLa

PMLb

PMLc

SAL

Mind

BodyIndependentof lower-body

implementation

Hearing

Vision

Motion

Speech WORLD

I/P s o c k e t s

MGLAIR Agent Architecture

Dependenton lower-bodyimplementation

Proprioception

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SNePSSNePS is a

Logic-Based

Frame-Based

Network-Based

knowledge representation, reasoning,and acting system.

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Example Knowledge Base• As logical assertions:

wff1!: Inst({Albany,Syracuse,Rochester,Buffalo},City)wff2!: WestOf(Buffalo,Rochester)wff3!: WestOf(Rochester,Syracuse)wff4!: WestOf(Syracuse,Albany)

• As frames:(m1! (class City) (member Albany Syracuse Rochester Buffalo))(m2! (east Rochester) (west Buffalo))(m3! (east Syracuse) (west Rochester))(m4! (east Albany) (west Syracuse))

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Example KB as a Network

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Some Important SNePS Features

• First-person beliefs– Not third-person “truth” about agent or world

• Beliefs are current beliefs– Even if about the past

• On-line acting

• Reified propositions as well as acts & …

• Neither states nor times are privileged

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Some Recent & CurrentProjects

• A General Characterization of Answers to Questions

• KRR for Information Fusion for Cyber Security

• Ontology Tool

• Logic of Arbitrary and Indefinite Objects

• Intermedia Performance Studio

– Actor-agents in VR drama

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A General Characterization of Answers to Questions

• Every clause descended from a query clause in resolution theorem proving is an answer to the query.

• General form of an answer:[H ] [G ] Q

H is Hypothetical component (optional)G is Generic component (optional)Q is Question component (either generic or specific)

• Example:{~Cat(Boots), ~Tuna(x), Answer(Likes(Boots, x))}Cat(Boots) x (Tuna(x) Likes (Boots, x))If Boots is a cat, then Boots likes to eat any tuna.

[D. T. Burhans & S. C. Shapiro, Defining Answer Classes Using Resolution Refutation, Journal of Applied Logic 5,1 (March 2007), 70-91.

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in the Cyber Security Domain

• Use SNePS to reason about computer networks, and about potential, and actual attacks.

• A task of the National Center for Multisource Information Fusion

• With Moises Sudit (IE & CMIF), Adam Stotz (CUBRC), & Michael Kandefer (CSE: RA)

• Funded by U.S. Air Force Research Laboratory, Rome, NY

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SNePS fuses information from:SME domain rules

Analyst background knowledgeOntologyNetwork topologyCommon Vulnerabilities and

ExposuresINFERD attack tracks

Added Java-SNePS API

SNePS GUI in development

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• Allows the display of binary predicates as a tree hierarchy

SNePS Ontology GUI• SNePS GUI

supports the loading and exporting of SNePS files in several formats.

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Hierarchical Relations

• Examples:– Subclass

• A dog is a mammal is a vertebrate.

– Part of• A heart is part of a chest is part of a person.

– Reporting• A professor reports to a chair reports to a dean.

• SNePS GUI finds hierarchical relations from the KB.

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• The user selects a predicate from the drop down menu, after which the tree is generated

Selecting a Predicatefor the Tree View

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• This view shows a small class hierarchy for hosts on a network

Class Hierarchy

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• This view shows a part of hierarchy.

Part Of

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• Shows the network representation (drawn using the JUNG network visualization tool) of a SNePS belief base

• User can zoom, move nodes, and save images as JIMI supported file types (jpg, bmp, png)

• Tooltips provide information about the propositions expressed by the nodes in the network

• Ex. This view shows assertions about the connectivity of various hosts (h0-h4)

Network View

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Logic of Arbitrary & Indefinite Objects

• Instead of– Borders(LakeErie, NewYork) x[GreatLake(x) & Borders(x, NewYork)] x[GreatLake(x) Borders(x, Ontario)]

• Have– Borders(LakeErie, NewYork)– Borders(some x GreatLake(x), NewYork)– Borders(any x GreatLake(x), Ontario)

[S. C. Shapiro, A Logic of Arbitrary and Indefinite Objects, KR2004, 565-575.]

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Intermedia Performance Studio

• Buffalo-Region resource focusing on the integration of live actors, virtual avatars, intelligent actor-agents, dynamic sets and live, mobile audience members.

• With Sarah Bay-Cheng (THD & DMS), Josephine Anstey (DMS), David Pape (DMS), & Jon Bona (CSE: RA)

• Funded by UB Provost’s Interdisciplinary Research Development Fund (IRDF)

• See http://vrstudio.buffalo.edu/ips/wiki/

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The Trial The Trail

• Virtual Reality Drama with SNePS/GLAIR agent-actors.

• With Josephine Anstey (DMS), David Pape (DMS), and CSE grads: Jon Bona, Albert Goldfain, Mike Kandefer, Vishwac Sena Kannan, Madhumitha Nagarajan

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Bad guy agents hassling a human participant

The Trial The Trail

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For More Information• Shapiro:

http://www.cse.buffalo.edu/~shapiro/

• SNePS Research Group: http://www.cse.buffalo.edu/sneps/– Meets Tuesdays 10-12, 242 Bell Hall– Join us!