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The SNePS Approach to Cognitive Robotics. Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science University at Buffalo shapiro@cse.buffalo.edu. Outline. Introduction Intensional Representation & Propositions SNePS Connectives and Quantifiers

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the sneps approach to cognitive robotics

The SNePS Approach to Cognitive Robotics

Stuart C. Shapiro

Department of Computer Science and Engineering

and Center for Cognitive Science

University at Buffalo

shapiro@cse.buffalo.edu

S.C. Shapiro

outline
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

slide3
Goal
  • A computational cognitive agent that can:
    • Understand and communicate in English;
    • Discuss specific, generic, and “rule-like” information;
    • Reason;
    • Discuss acts and plans;
    • Sense;
    • Act;
    • Remember and report what it has sensed and done.

S.C. Shapiro

embodied cassie
Embodied Cassie
  • A computational cognitive agent
    • Embodied in hardware
    • or Software-Simulated
    • Based on SNePS and GLAIR.

S.C. Shapiro

sneps
SNePS
  • Knowledge Representation and Reasoning
    • Propositions as Terms
  • SNIP: SNePS Inference Package
    • Specialized connectives and quantifiers
  • SNeBR: SNePS Belief Revision
  • SNeRE: SNePS Rational Engine
  • Interface Languages
    • SNePSUL: Lisp-Like
    • SNePSLOG: Logic-Like
    • GATN for Fragments of English.

S.C. Shapiro

glair architecture
GLAIR Architecture

Grounded Layered Architecture with Integrated Reasoning

Knowledge Level

SNePS

Perceptuo-Motor Level

Sensory-Actuator Level

NL

Percepts

Action

S.C. Shapiro

interaction with cassie
Interaction with Cassie

(Current) Set of Beliefs

[SNePS]

English

(Statement, Question, Command)

Reasoning

Clarification Dialogue

Looking in World

GATN Parser

(Updated) Set

of Beliefs

[SNePS]

(New Belief)

[SNePS]

Answer

[SNIP]

Actions

[SNeRE]

GATN Generator

Reasoning

English sentence expressing

new belief answering question reporting actions

S.C. Shapiro

cassie the fevahr
Cassie, the FEVAHR

S.C. Shapiro

uxo remediation cassie
UXO Remediation Cassie

Corner flag

Field

Drop-off zone

UXO

NonUXO object

Battery

meter

Corner flag

Corner flag

Recharging

Station

Cassie

Safe zone

S.C. Shapiro

outline1
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

entities terms symbols objects
Entities, Terms, Symbols, Objects
  • Cassie’s mental entity: a person named Bill
  • SNePS term: B5
  • Object in world:

S.C. Shapiro

intensional representation
Intensional Representation

Intensional entities are distinct even if coreferential.

“The morning star is the evening star.”

“George IV wondered if Scott was the author of Waverly.”

S.C. Shapiro

mccarthy s telephone number problem
McCarthy’s Telephone Number Problem

Mary's telephone number is Mike's telephone number.

I understand that Mike's telephone number is Mary's telephone number.

Pat knew Mike's telephone number.

I understand that Pat knew Mike's telephone number.

Pat dialed Mike's telephone number.

I understand that Pat dialed Mike's telephone number.

S.C. Shapiro

answering the telephone number problem
Answering the Telephone Number Problem

Did Pat dial Mary's telephone number?

Yes, Pat dialed Mary's telephone number.

Did Pat know Mary's telephone number?

I don't know.

S.C. Shapiro

representing propositions
Representing Propositions

Propositions must be first-class entities of the domain

Represented by terms.

S.C. Shapiro

discussing propositions
Discussing Propositions

That Bill is sweet is Mary's favorite proposition.

I understand that Mary's favorite proposition is that Bill is sweet.

Mike believes Mary's favorite proposition.

I understand that Mike believes that Bill is sweet.

S.C. Shapiro

outline2
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

logic for nlu commonsense reasoning
Logic for NLU &Commonsense Reasoning

Either Pat is a man or Pat is a woman or Pat is a robot.

I understand that Pat is a robot or Pat is a woman or Pat is a man.

Pat is a woman.

I understand that Pat is a woman.

What is Pat?

Pat is a woman and Pat is not a robot and Pat is not a man.

S.C. Shapiro

representation in fopl
Representation in FOPL?

Man(Pat)  Woman(Pat)  Robot(Pat)

S.C. Shapiro

representation in fopl1
Representation in FOPL?

Man(Pat)  Woman(Pat)  Robot(Pat)

but don’t want inclusive or

S.C. Shapiro

representation in fopl2

+

+

Representation in FOPL?

Man(Pat)  Woman(Pat)  Robot(Pat)

but don’t want inclusive or

Man(Pat) Woman(Pat) Robot(Pat)

T

T

T

F

T

So don’t want exclusive or either

S.C. Shapiro

andor
andor

andor(i, j){P1, ..., Pn}

True iff at least i, and at most j of the Pi are True

S.C. Shapiro

thresh
thresh

thresh(i, j){P1, ..., Pn}

True iff either fewer than i,

or more than j

of the Pi are True

Note: thresh(i, j) ~andor(i, j)

S.C. Shapiro

or entailment
or-entailment

{P1, ..., Pn} v=> {Q1, ..., Qn}

True iff for all i, j Pi Qj

S.C. Shapiro

and entailment
and-entailment

{P1, ..., Pn} &=> {Q1, ..., Qn}

True iff for all j

P1 &…& Pn Qj

S.C. Shapiro

numerical entailment
Numerical entailment

{P1, ..., Pn} i=> {Q1, ..., Qn}

True iff for all j

andor(i, n){P1, …, Pn }Qj

S.C. Shapiro

universal quantifier
Universal Quantifier

all(ū)({R1(ū),..., Rn(ū)} &=> {C1(ū),..., Cm(ū)})

Every ā that satisfies

R1(ū)&…& Rn(ū)

also satisfies

C1(ū),..., Cm(ū)})

S.C. Shapiro

numerical quantifiers
Numerical Quantifiers

nexists(i,j,k)(x)

({P1(x),..., Pn(x)}: {Q(x)})}

There are k individuals that satisfy

P1(x) ... Pn(x)

and, of them, at least i and at most j also satisfy

Q(x)

S.C. Shapiro

outline3
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

mental acts
MENTAL ACTS
  • Believe(proposition)
  • Disbelieve(proposition)

S.C. Shapiro

act selection
Act Selection
  • Do-One({act1 ... actn})
  • Snif(if(condition, act),

...

if(condition, act)

[else(act)])

S.C. Shapiro

act iteration
Act Iteration
  • Do-All({act1 ... actn})
  • Sniterate(if(condition, act),

...

if(condition, act),

[else(act)])

  • Snsequence(act1, ..., actn)
  • Cascade(act1, ..., actn)
  • P-Do-All({act1, ..., act2})

S.C. Shapiro

entity iteration
Entity Iteration

WithSome(var, suchthat,

do, [else])

WithAll(var, suchthat,

do, [else])

WithSome+(var, suchthat,

do, [else])

WithNew(vars, thatare, suchthat,

do, [else])

S.C. Shapiro

proposition act transformers
Proposition/Act Transformers
  • Achieve(proposition)
  • ActPlan(act, plan)
  • GoalPlan(proposition, act)
  • Precondition(act, proposition)
  • Effect(act, proposition)
  • WhenDo(proposition, act)
  • WheneverDo(proposition, act)
  • IfDo(proposition, act)

S.C. Shapiro

outline4
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

conditional plans
Conditional Plans

If a block is on a support then a plan to achieve that the support is clear is to pick up the block and then put the block on the table.

all(x, y)

({Block(x), Support(y), On(x, y)}

&=> {GoalPlan(Clear(y),

Snsequence(Pickup(x),

Put(x, Table)))})

(STRIPS-like representation: No times)

S.C. Shapiro

use of conditional plan
Use of Conditional Plan

GoalPlan(Clear(B),

Snsequence(Pickup(A),

Put(A, Table)))

Remember (cache) derived propositions.

S.C. Shapiro

use of conditional plan1
Use of Conditional Plan

GoalPlan(Clear(B),

Snsequence(Pickup(A),

Put(A, Table)))???

SNeBR to the rescue!

S.C. Shapiro

a fevahr acting rule
A FEVAHR Acting Rule

all(a, o) ({Agent(a), Thing(o)}

&=> {Precondition(Follow(a, o), Near(a, o)),

GoalPlan(Near(a, o), Goto(a, o)),

Precondition(Goto(a, o), Lookat(a, o)),

ActPlan(Lookat(a, o), Find(a, o))})

Uses a temporal model.

S.C. Shapiro

acting according to the rule1
Acting According to the Rule

Follow a red robot.

I found a red robot.

I am looking at a red robot.

S.C. Shapiro

acting according to the rule2
Acting According to the Rule

Follow a red robot.

I found a red robot.

I am looking at a red robot.

I went to a red robot.

I am near a red robot.

I am following a red robot.

S.C. Shapiro

a plan for blowing up uxos
A Plan for Blowing up UXOs

all(a)(Agent(a) =>

ActPlan(Blowup(a, UXOs),

Act(a,

Cascade(SearchforUxo(a),

WithSome+(obj,

Near(a, obj),

WithNew({ch ex},

{Charge(ch), Explosion(ex)},

Possess(a, ch),

Cascade(Place(a, ch, obj),

Hide(a),

Waitfor(a, ex),

SearchforUxo(a))),

goto(a, SafeZone))))))

S.C. Shapiro

outline5
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

representation and use of indexicals
Representation and Use of Indexicals
  • Words whose meanings are determined by occasion of use
  • E.g. I, you, now, then, here, there
  • Deictic Center <*I, *YOU, *NOW>
  • *I: SNePS term representing Cassie
  • *YOU: person Cassie is talking with
  • *NOW: current time.

S.C. Shapiro

analysis of indexicals in input
Analysis of Indexicals(in input)
  • First person pronouns: *YOU
  • Second person pronouns: *I
  • “here”: location of *YOU
  • Present/Past relative to *NOW.

S.C. Shapiro

generation of indexicals
Generation of Indexicals
  • *I: First person pronouns
  • *YOU: Second person pronouns
  • *NOW: used to determine tense and aspect.

S.C. Shapiro

use of indexicals 1
Use of Indexicals 1

Come here.

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use of indexicals 2
Use of Indexicals 2

Come here.

I came to you, Stu.

I am near you.

S.C. Shapiro

use of indexicals 3
Use of Indexicals 3

Who am I?

Your name is ‘Stu’

and you are a person.

Who have you talked to?

I am talking to you.

Talk to Bill.

I am talking to you, Bill.

Come here.

S.C. Shapiro

use of indexicals 4
Use of Indexicals 4

Come here.

I found you.

I am looking at you.

S.C. Shapiro

use of indexicals 5
Use of Indexicals 5

Come here.

I found you.

I am looking at you.

I came to you.

I am near you.

S.C. Shapiro

use of indexicals 6
Use of Indexicals 6

Who am I?

Your name is ‘Bill’

and you are a person.

Who are you?

I am the FEVAHR

and my name is ‘Cassie’.

Who have you talked to?

I talked to Stu

and I am talking to you.

S.C. Shapiro

outline6
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

a personal sense of time
A Personal Sense of Time
  • *NOW contains SNePS term representing current time.
  • *NOW moves when Cassie acts or perceives a change of state.

S.C. Shapiro

representation of time

find

Representation of Time

before

after

before

after

!

!

!

event

?????????????

time

agent

act

B1

action

object

B6

I

lex

NOW

S.C. Shapiro

movement of time

before

after

before

after

!

t2

!

t3

NOW

NOW

NOW

Movement of Time

t1

S.C. Shapiro

performing a punctual act

before

after

before

after

!

t2

!

t3

time

!

event

NOW

NOW

Performing a Punctual Act

t1

S.C. Shapiro

performing a durative act

before

after

!

t2

supint

!

subint

t3

time

!

event

NOW

NOW

Performing a Durative Act

t1

S.C. Shapiro

the pacemaker
The Pacemaker
  • PML process periodically increments variable COUNT.
  • *COUNT = some PML integer.
  • Reset to 0 when NOW moves.
  • Provides bodily “feel” of passing time.

S.C. Shapiro

quantizing time
Quantizing Time

Cannot conceptualize fine distinctions in time intervals.

So quantize, e.g. into half orders of magnitude (Hobbs, 2000).

S.C. Shapiro

movement of time with pacemaker

!

duration

time

before

after

!

Movement of Time with Pacemaker

q

t1

t2

KL

PML

hom

COUNT

n

NOW

0

S.C. Shapiro

the problem of the fleeting now
The Problem of the Fleeting Now

How can you reason about “now”

if it never stands still?

S.C. Shapiro

fleeting now example 1
Fleeting Now Example 1

12:15:00: “Is John having lunch now?”

12:15:02: Agent walks to John’s office.

12:17:00: Agent sees John at his desk, eating.

12:19:00: Agent reports “yes”.

Appropriate granularity.

S.C. Shapiro

fleeting now example 2
Fleeting Now Example 2

12:15:00: “Is John having lunch now?”

Agent knows John is at home without a phone.

Agent contemplates driving to John’s home.

Don’t bother---inappropriate granularity.

S.C. Shapiro

the vagueness of now
The Vagueness of “now”

I’m now giving a talk.

I’m now supervising PhD students.

I’m now visiting Paris.

I’m now living in Buffalo.

The agent is now walking to John’s office.

The agent is now seeing if John is eating lunch.

Multiple now’s at different granularities.

S.C. Shapiro

now mtf
NOW-MTF

Maximal Temporal Frame based on *NOW

NOW

Semi-lattice of times, all of which contain *NOW,

any of which could be meant by “now”

Finite---only conceptualized times of conceptualized states

S.C. Shapiro

moving now with mtf
Moving NOW with MTF

NOW

S.C. Shapiro

typical durations
Typical Durations

“If the walk light is on now, cross the street.”

Relevant duration is typical duration of walk lights.

“Is John having lunch now?”

Relevant duration is typical duration of lunch.

Use quantized typical durations when updating NOW-MTFs.

S.C. Shapiro

using appropriate granularity
Using Appropriate Granularity

Lunch time

Lunch?

Lunch!

NOW

Yes!

S.C. Shapiro

outline7
Outline
  • Introduction
  • Intensional Representation & Propositions
  • SNePS Connectives and Quantifiers
  • SNeRE Acting Constructs
  • Example Plans
  • Representation and Use of Indexicals
  • A Personal Sense of Time
  • Summary

S.C. Shapiro

slide76
Goal
  • A computational cognitive agent/robot
  • That can communicate in natural language.

S.C. Shapiro

intensional representation propositions
Intensional Representation& Propositions
  • SNePS terms represent mental entities.
  • May assert that two entities are coreferential.
  • Relations/acts may be declared transparent.
  • Propositions are first-class entities.

S.C. Shapiro

sneps connectives and quantifiers
SNePS Connectives and Quantifiers
  • Designed logical connectives and rules of inference

More appropriate for NLU and Commonsense reasoning

than in standard FOPC.

S.C. Shapiro

snere acting constructs
SNeRE Acting Constructs
  • Separate, but Coordinated

Syntax and Semantics

For Acting and for Reasoning

S.C. Shapiro

representation and use of indexicals1
Representation and Useof Indexicals
  • Use of Deictic Center for parser to interpret indexicals as current referents
  • And for generator to generate indexicals from current referents.

S.C. Shapiro

a personal sense of time1
A Personal Sense of Time
  • *NOW is current time.
  • Updated when Cassie acts

or perceives a change of state.

  • Points into MTF to support vagueness of “now”.

S.C. Shapiro

for more information
For More Information
  • Personnel
  • Manual
  • Tutorial
  • Bibliography
  • ftp’able SNePS source code
  • etc.
  • http://www.cse.buffalo.edu/sneps/

S.C. Shapiro