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Inconsistency Tolerance in SNePS. Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo, The State University of New York 201 Bell Hall, Buffalo, NY 14260-2000 [email protected] http://www.cse.buffalo.edu/~shapiro/.

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Inconsistency tolerance in sneps l.jpg

Inconsistency Tolerance in SNePS

Stuart C. Shapiro

Department of Computer Science and Engineering,

and Center for Cognitive Science

University at Buffalo, The State University of New York

201 Bell Hall, Buffalo, NY 14260-2000

[email protected]

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


Acknowledgements l.jpg
Acknowledgements

  • João Martins

  • Frances L. Johnson

  • Bharat Bhushan

  • The SNePS Research Group

  • NSF, Instituto Nacional de Investigação Cientifica, Rome Air Development Center, AFOSR, U.S. Army CECOM

S. C. Shapiro


Outline l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Sneps l.jpg
SNePS

  • A logic- and network-based

  • Knowledge representation

  • Reasoning

  • And acting

  • System [Shapiro & Group ’02]

    This talk will ignore network and acting aspects.

S. C. Shapiro


Logic l.jpg
Logic

  • Based on R, the logic of relevant implication

    [Anderson & Belnap ’75; Martins & Shapiro ’88, Shapiro ’92]

S. C. Shapiro


Supported wffs l.jpg
Supported wffs

P{… <origin tag, origin set> …}

Set of hypotheses

From which P

has been derived.

hyphypothesis

derderived

Origin set tracks relevance and ATMS assumptions.

S. C. Shapiro


Outline7 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Rules of inference hypothesis l.jpg
Rules of Inference:Hypothesis

Hyp: P {<hyp,{P}>}

: whale(Willy) and free(Willy). wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>}

S. C. Shapiro


Rules of inference e l.jpg
Rules of Inference:&E

&E: From A and B {<t,s>}

infer A {<der,s>} or B {<der,s>}

wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>}

: free(Willy)? wff2: free(Willy) {<der,{wff3}>}

S. C. Shapiro


Rules of inference andore l.jpg
Rules of Inference:andorE

The os is the union of os's of parents

wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>}

wff6:all(x)(andor(0,1){manatee(x), dolphin(x), whale(x)})

{<hyp,{wff6}>}: dolphin(Willy)?

wff9: ~dolphin(Willy) {<der,{wff3,wff6}>}

At most 1

S. C. Shapiro


Rules of inference e11 l.jpg
Rules of Inference:=>E

The origin set is the union of os's of parents.

Since wff10: all(x)(whale(x) => mammal(x)) {<hyp,{wff10}>}

and wff1: whale(Willy){<der,{wff3}>}

I infer wff11: mammal(Willy) {<der,{wff3,wff10}>}

S. C. Shapiro


Rules of inference i l.jpg
Rules of Inference:=>I

origin set is diff of os's of parents.

wff12: all(x)(orca(x) => whale(x)) {<hyp,{wff12}>}

: orca(Keiko) => mammal(Keiko)?

Let me assume that wff13: orca(Keiko) {<hyp,{wff13}>}

Since wff12: all(x)(orca(x) => whale(x)) {<hyp,{wff12}>}and wff13: orca(Keiko){<hyp,{wff13}>}

I infer whale(Keiko) {<der,{wff12,wff13}>}

S. C. Shapiro


Rules of inference i cont d l.jpg
Rules of Inference:=>I (cont’d)

origin set is diff of os's of parents.

Since wff10: all(x)(whale(x) => mammal(x)) {<hyp,{wff10}>}

and wff16: whale(Keiko) {<der,{wff12,wff13}>}I infer mammal(Keiko) {<der,{wff10,wff12,wff13}>}

Since wff14: mammal(Keiko) {<der,{wff10,wff12,wff13}>}was derived assuming

wff13: orca(Keiko) {<hyp,{wff13}>}I infer

wff15: orca(Keiko) => mammal(Keiko) {<der,{wff10,wff12}>}

S. C. Shapiro


Outline14 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


I and belief revision l.jpg
~I and Belief Revision

  • ~I triggered when a contradiction is derived.

  • Proposition to be negated must be one of the hypotheses underlying the contradiction.

  • Origin set is the rest of the hypotheses.

  • SNeBR [Martins & Shapiro ’88] involved in choosing the culprit.

S. C. Shapiro


Adding inconsistent hypotheses l.jpg
Adding Inconsistent Hypotheses

wff19: all(x)(whale(x) => fish(x)){<hyp,{wff19}>}

wff20: all(x)(andor(0,1){mammal(x), fish(x)})

{<hyp,{wff20}>}

wff21: all(x)(fish(x) <=> has(x,scales))

{<hyp,{wff21}>}

S. C. Shapiro


Finding the contradiction l.jpg
Finding the Contradiction

: has(Willy, scales)?

Since wff19: all(x)(whale(x) => fish(x)) {<hyp,{wff19}>}

and wff1: whale(Willy) {<der,{wff3}>}I infer fish(Willy) {<der,{wff3,wff19}>}

Since wff21: all(x)(fish(x) <=> has(x,scales))

{<hyp,{wff21}>}

and wff23: fish(Willy) {<der,{wff3,wff19}>}I infer has(Willy,scales) {<der,{wff3,wff19,wff21}>}

Since wff20:

all(x)(andor(0,1){mammal(x), fish(x)})

{<hyp,{wff20}>}

and wff11: mammal(Willy) {<der,{wff3,wff10}>}I infer it is not the case that wff23: fish(Willy)

S. C. Shapiro


Manual belief revision l.jpg
Manual Belief Revision

A contradiction was detected

within context default-defaultct. The contradiction involves the newly derived proposition: wff24: ~fish(Willy) {<der,{wff3,wff10,wff20}>} and the previously existing proposition: wff23: fish(Willy) {<der,{wff3,wff19}>} You have the following options: 1. [c]ontinue anyway, knowing that a contradiction is derivable; 2. [r]e-start the exact same run in a different context which is not inconsistent; 3. [d]rop the run altogether. (please type c, r or d)=><= r

S. C. Shapiro


Br advice l.jpg
BR Advice

In order to make the context consistent you must delete at least one hypothesis from the set listed below.

This set of hypotheses is known to be inconsistent:

1 : wff20: all(x)(andor(0,1){mammal(x),fish(x)})

{<hyp,{wff20}>} (1 dependent proposition: (wff24))

2 : wff19: all(x)(whale(x) => fish(x)) {<hyp,{wff19}>} (2 dependent propositions: (wff23 wff22))

3 : wff10: all(x)(whale(x) => mammal(x)){<hyp,{wff10}>} (3 dependent propositions: (wff24 wff15 wff11))

4 : wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>} (8 dependent propositions:

(wff24 wff23 wff22 wff11 wff9 wff5 wff2 wff1))

User deletes #2: wff19.

S. C. Shapiro


Willy has no scales l.jpg
Willy has no Scales

Since wff21: all(x)(fish(x) <=> has(x,scales))

{<hyp,{wff21}>}

and it is not the case that wff23: fish(Willy)

{<der,{wff3,wff19}>}

I infer it is not the case that

wff22: has(Willy,scales) {<der,{wff3,wff19,wff21}>}

wff26: ~has(Willy,scales){<der,{wff3,wff10,wff20,wff21}>}

S. C. Shapiro


Final kb hyps positive ders l.jpg
Final KB: hyps & positive ders

: list-asserted-wffs

wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>}

wff6: all(x)(andor(0,1){manatee(x),dolphin(x),whale(x)})

{<hyp,{wff6}>}

wff10: all(x)(whale(x) => mammal(x)) {<hyp,{wff10}>}

wff12: all(x)(orca(x) => whale(x)) {<hyp,{wff12}>}

wff20: all(x)(andor(0,1){mammal(x),fish(x)}) {<hyp,{wff20}>}

wff21: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff21}>}

wff1: whale(Willy) {<der,{wff3}>}

wff2: free(Willy) {<der,{wff3}>}

wff11: mammal(Willy) {<der,{wff3,wff10}>}

wff15: orca(Keiko) => mammal(Keiko) {<der,{wff10,wff12}>}

S. C. Shapiro


Final kb hyps negative ders l.jpg
Final KB: hyps & negative ders

: list-asserted-wffs

wff3: free(Willy) and whale(Willy) {<hyp,{wff3}>}

wff6: all(x)(andor(0,1){manatee(x),dolphin(x),whale(x)})

{<hyp,{wff6}>}

wff10: all(x)(whale(x) => mammal(x)) {<hyp,{wff10}>}

wff12: all(x)(orca(x) => whale(x)) {<hyp,{wff12}>}

wff20: all(x)(andor(0,1){mammal(x),fish(x)}) {<hyp,{wff20}>}

wff21: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff21}>}

wff9: ~dolphin(Willy) {<der,{wff3,wff10}>}

wff24: ~fish(Willy) {<der,{wff3,wff10,wff20}>}

wff25: ~(all(x)(whale(x) => fish(x))) {<ext,{wff3,wff10,wff20}>}

wff26: ~has(Willy,scales) {<der,{wff3,wff10,wff20,wff21}>}

S. C. Shapiro


Summary l.jpg
Summary

  • Logic is paraconsistent:

    P{<t1, {h1 … hi}>},

    ~P{<t2, {h(i+1) … hn}>}

    ~hj

  • When a contradiction is explicitly found, the user is engaged in its resolution.

S. C. Shapiro


Outline24 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Credibility ordering and automatic belief revision l.jpg
Credibility Ordering and Automatic Belief Revision*

  • Hypotheses may be given sources.

  • Sources may be given relative credibility.

  • Hypotheses inherit relative credibility from sources.

  • Hypotheses may be given relative credibility directly. (Not shown.)

  • SNeBR may use relative credibility to choose a culprit by itself. [Shapiro & Johnson ’00]

    *Not yet in released version.

S. C. Shapiro


Contradictory sources l.jpg
Contradictory Sources

wff1: all(x)(andor(0,1){mammal(x),fish(x)}) {<hyp,{wff1}>}

wff2: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff2}>}

wff3: all(x)(orca(x) => whale(x)) {<hyp,{wff3}>}

: Source(Melville, all(x)(whale(x) => fish(x)).).

wff5: Source(Melville,all(x)(whale(x) => fish(x)))

{<hyp,{wff5}>}

: Source(Darwin, all(x)(whale(x) => mammal(x)).).

wff7: Source(Darwin,all(x)(whale(x) => mammal(x)))

{<hyp,{wff7}>}: Sgreater(Darwin, Melville). wff8: Sgreater(Darwin,Melville) {<hyp,{wff8}>}

wff11: free(Willy) and whale(Willy) {<hyp,{wff11}>}

Note: Source & Sgreater props are regular object-language props.

S. C. Shapiro


Finding the contradiction27 l.jpg
Finding the Contradiction

: has(Willy, scales)?Since wff4: all(x)(whale(x) => fish(x)) {<hyp,{wff4}>}and wff9: whale(Willy) {<der,{wff11}>}I infer fish(Willy) {<der,{wff4,wff11}>}

Since wff2: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff2}>}and wff14: fish(Willy) {<der,{wff4,wff11}>}I infer has(Willy,scales)Since wff6: all(x)(whale(x) => mammal(x)) {<hyp,{wff6}>}and wff9: whale(Willy) {<der,{wff11}>}I infer mammal(Willy)Since wff1: all(x)(andor(0,1){mammal(x),fish(x)})

{<hyp,{wff1}>}and wff15: mammal(Willy) {<der,{wff6,wff11}>}I infer it is not the case that

wff14: fish(Willy) {<der,{wff4,wff11}>}

S. C. Shapiro


Automatic br l.jpg
Automatic BR

A contradiction was detected within context default-defaultct.

The contradiction involves the newly derived proposition: wff17: ~fish(Willy) {<der,{wff1,wff6,wff11}>}

and the previously existing proposition: wff14: fish(Willy) {<der,{wff4,wff11}>}

The least believed hypothesis: (wff4)

The most common hypothesis: (nil)

The hypothesis supporting the fewest wffs: (wff1)

I removed the following belief: wff4: all(x)(whale(x) => fish(x)) {<hyp,{wff4}>}

I no longer believe the following 2 propositions: wff14: fish(Willy) {<der,{wff4,wff11}>} wff13: has(Willy,scales) {<der,{wff2,wff4,wff11}>}

S. C. Shapiro


Summary29 l.jpg
Summary

  • User may select automatic BR.

  • Relative credibility is used.

  • User is informed of lost beliefs.

S. C. Shapiro


Outline30 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Reasoning in different contexts l.jpg
Reasoning in Different Contexts

  • A context is a set of hypotheses and all propositions derived from them.

  • Reasoning is performed within a context.

  • A conclusion is available in every context that is a superset of its origin set. [Martins & Shapiro ’83]

  • Contradictions across contexts are not noticed.

S. C. Shapiro


Darwin context l.jpg
Darwin Context

: set-context Darwin ()

: set-default-context Darwin

wff1: all(x)(andor(0,1){mammal(x),fish(x)})

{<hyp,{wff1}>}

wff2: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff2}>}

wff3: all(x)(orca(x) => whale(x)) {<hyp,{wff3}>}

wff4: all(x)(whale(x) => mammal(x)) {<hyp,{wff4}>}

wff7: free(Willy) and whale(Willy) {<hyp,{wff7}>}

S. C. Shapiro


Melville context l.jpg
Melville Context

: describe-context((assertions (wff8 wff7 wff4 wff3 wff2 wff1)) (restriction nil) (named (science))): set-context Melville (wff8 wff7 wff3 wff2 wff1)((assertions (wff8 wff7 wff3 wff2 wff1)) (restriction nil) (named (melville))): set-default-context Melville((assertions (wff8 wff7 wff3 wff2 wff1)) (restriction nil) (named (melville))): all(x)(whale(x) => fish(x)). wff9: all(x)(whale(x) => fish(x)) {<hyp,{wff9}>}

S. C. Shapiro


Melville willy has scales l.jpg
Melville: Willy has scales

: has(Willy, scales)?Since wff9: all(x)(whale(x) => fish(x)){<hyp,{wff9}>}and wff5: whale(Willy) {<der,{wff7}>}I infer fish(Willy) {<der,{wff7,wff9}>}

Since wff2: all(x)(fish(x) <=> has(x,scales))

{<hyp,{wff2}>}and wff11: fish(Willy) {<der,{wff7,wff9}>}I infer has(Willy,scales) {<der,{wff2,wff7,wff9}>}

wff10: has(Willy,scales) {<der,{wff2,wff7,wff9}>}

S. C. Shapiro


Darwin no scales l.jpg
Darwin: No scales

: set-default-context Darwin

: has(Willy, scales)?Since wff4: all(x)(whale(x) => mammal(x)) {<hyp,{wff4}>}and wff5: whale(Willy) {<der,{wff7}>}I infer mammal(Willy)Since wff1: all(x)(andor(0,1){mammal(x),fish(x)})

{<hyp,{wff1}>}and wff12: mammal(Willy) {<der,{wff4,wff7}>}I infer it is not the case that wff11: fish(Willy)

Since wff2: all(x)(fish(x) <=> has(x,scales)) {<hyp,{wff2}>}and it is not the case that wff11: fish(Willy)

{<der,{wff7,wff9}>}I infer it is not the case that wff10: has(Willy,scales)

wff15: ~has(Willy,scales) {<der,{wff1,wff2,wff4,wff7}>}

S. C. Shapiro


Summary36 l.jpg
Summary

  • Contradictory information may be isolated in different contexts.

  • Reasoning is performed in a single context.

  • Results are available in other contexts.

S. C. Shapiro


Outline37 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Default reasoning by preferential ordering l.jpg
Default Reasoning by Preferential Ordering

  • No special syntax for default rules.

  • If P and ~P are derived

    • but argument for one is undercut by an argument for the other

    • don’t believe the undercut conclusion.

  • Unlike BR, believe the hypotheses, but not a conclusion.

    [Grosof ’97, Bhushan ’03]

S. C. Shapiro


Preclusion rules in sneps l.jpg
Preclusion Rules in SNePS*

  • P undercuts ~P if

    • Precludes(P, ~P) or

    • Every origin set of ~P has some hyp h such that there is some hyp q in an origin set of P such that Precludes(q, h).

  • Precludes(P, Q) is a proposition like any other.

    *Not yet in released version.

S. C. Shapiro


Animal modes of mobility l.jpg
Animal Modes of Mobility

wff1: all(x)(orca(x) => whale(x))

wff2: all(x)(whale(x) => mammal(x))

wff3: all(x)(deer(x) => mammal(x))

wff4: all(x)(tuna(x) => fish(x))

wff5: all(x)(canary(x) => bird(x))

wff6: all(x)(penguin(x) => bird(x))

wff7: all(x)(andor(0,1){swims(x),flies(x),runs(x)})

wff8: all(x)(mammal(x) => runs(x))

wff9: all(x)(fish(x) => swims(x))

wff10: all(x)(bird(x) => flies(x))

wff11: all(x)(whale(x) => swims(x))

wff12: all(x)(penguin(x) => swims(x))

S. C. Shapiro


Using preclusion for exceptions l.jpg
Using Preclusion for Exceptions

wff13: Precludes(all(x)(whale(x) => swims(x)),

all(x)(mammal(x) => runs(x)))

wff14: Precludes(all(x)(penguin(x) => swims(x)),

all(x)(bird(x) => flies(x)))

wff15: orca(Willy)

wff16: tuna(Charlie)

wff17: deer(Bambi)

wff18: canary(Tweety)

wff19: penguin(Opus)

S. C. Shapiro


Who swims contradictory conclusions l.jpg
Who Swims?(Contradictory Conclusions)

: swims(?x)?

I infer swims(Opus)

I infer swims(Charlie)

I infer swims(Willy)

I infer flies(Tweety)

I infer it is not the case that swims(Tweety)

I infer flies(Opus)

I infer it is not the case that wff20: swims(Opus)

I infer runs(Willy)

I infer it is not the case that wff24: swims(Willy)

I infer runs(Bambi)

I infer it is not the case that swims(Bambi)

S. C. Shapiro


Using preclusion to arbitrate contradictions 1 l.jpg
Using Preclusionto Arbitrate Contradictions (1)

Since wff13: Precludes(all(x)(whale(x) => swims(x)),

all(x)(mammal(x) => runs(x)))

and wff11: all(x)(whale(x) => swims(x)) {<hyp,{wff11}>}

holds within the BS defined by context default-defaultct

Therefore wff34: ~swims(Willy)

containing in its support

wff8: all(x)(mammal(x) => runs(x))

is precluded by wff24: swims(Willy)

that contains in its support

wff11:all(x)(whale(x) => swims(x))

S. C. Shapiro


Using preclusion to arbitrate contradictions 2 l.jpg
Using Preclusionto Arbitrate Contradictions (2)

Since wff14: Precludes(all(x)(penguin(x) => swims(x)),

all(x)(bird(x) => flies(x)))

and wff12: all(x)(penguin(x) => swims(x))

holds within the BS defined by context default-defaultct

Therefore wff31: ~swims(Opus)

containing in its support

wff10:all(x)(bird(x) => flies(x))

is precluded by wff20: swims(Opus)

that contains in its support

wff12: all(x)(penguin(x) => swims(x))

S. C. Shapiro


The swimmers and non swimmers l.jpg
The Swimmersand Non-Swimmers

wff38: ~swims(Bambi) {<der,{wff3,wff7,wff8,wff17}>}

wff28: ~swims(Tweety) {<der,{wff5,wff7,wff10,wff18}>}

wff24: swims(Willy) {<der,{wff1,wff11,wff15}>}

wff22: swims(Charlie) {<der,{wff4,wff9,wff16}>}

wff20: swims(Opus) {<der,{wff12,wff19}>}

S. C. Shapiro


Two level preclusion l.jpg
Two-Level Preclusion

wff1: all(x)(robin(x) => bird(x))

wff2: all(x)(kiwi(x) => bird(x))

wff3: all(x)(bird(x) => flies(x))

wff4: all(x)(bird(x) => (~flies(x)))

wff5: all(x)(robin(x) => flies(x))

wff6: all(x)(kiwi(x) => (~flies(x)))

Example from Delgrande & Schaub ‘00

S. C. Shapiro


Preferences l.jpg
Preferences

wff7: Precludes(all(x)(robin(x) => flies(x)),

all(x)(bird(x) => (~flies(x))))

wff8: Precludes(all(x)(kiwi(x) => (~flies(x))),

all(x)(bird(x) => flies(x)))

wff12: (~location(New Zealand))

=> Precludes(all(x)(bird(x) => flies(x)),

all(x)(bird(x) => (~flies(x))))

wff14: location(New Zealand)

=> Precludes(all(x)(bird(x) => (~flies(x))),

all(x)(bird(x) => flies(x)))

wff10: ~location(New Zealand)

wff15: Precludes(location(New Zealand),

~location(New Zealand))

S. C. Shapiro


Who flies l.jpg
Who flies?

wff16: robin(Robin)

wff17: kiwi(Kenneth)

wff18: bird(Betty)

: flies(?x)?

S. C. Shapiro


Outside new zealand l.jpg
Outside New Zealand

wff24: ~flies(Kenneth){<der,{wff6,wff17}>,

<der,{wff2,wff4,wff17}>,

<der,{wff2,wff4,wff6,wff17}>}

wff21: flies(Robin) {<der,{wff5,wff16}>,

<der,{wff1,wff3,wff16}>}

wff19: flies(Betty) {<der,{wff3,wff18}>}

S. C. Shapiro


Inside new zealand l.jpg
Inside New Zealand

: location("New Zealand").

wff9: location(New Zealand)

: flies(?x)?

wff24: ~flies(Kenneth) {<der,{wff6,wff17}>,

<der,{wff2,wff4,wff17}>,

<der,{wff2,wff4,wff6,wff17}>}

wff21: flies(Robin) {<der,{wff5,wff16}>,

<der,{wff1,wff3,wff16}>}

wff20: ~flies(Betty) {<der,{wff4,wff18}>}

S. C. Shapiro


Summary51 l.jpg
Summary

  • Contradictions may be handled by DR instead of by BR.

  • Hypotheses retained; conclusion removed.

  • DR uses preferential ordering among contradictory conclusions or among supporting hypotheses.

  • Precludes forms object-language proposition that may be reasoned with or reasoned about.

S. C. Shapiro


Outline52 l.jpg
Outline

  • Introduction

  • Some Rules of Inference

  • ~I and Belief Revision

  • Credibility Ordering and Automatic BR

  • Reasoning in Different Contexts

  • Default Reasoning by Preferential Ordering

  • Summary

S. C. Shapiro


Summary inconsistency tolerance in sneps l.jpg
SummaryInconsistency Tolerance in SNePS

  • Inconsistency across contexts is harmless.

  • Inconsistency about unrelated topic is harmless.

  • Explicit contradiction may be resolved by user.

  • Explicit contradiction may be resolved by system using relative credibility of propositions or sources.

  • Explicit contradiction may be resolved by system using preferential ordering of conclusions or hypotheses.

S. C. Shapiro


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For more information

http://www.cse.buffalo.edu/sneps/

S. C. Shapiro


References i l.jpg
References I

A. R. Anderson, A. R. and N. D. Belnap, Jr. (1975) Entailment Volume I (Princeton: Princeton University Press).

B. Bhushan (2003) Preferential Ordering of Beliefs for Default Reasoning, M.S. Thesis, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY.

J. P. Delgrande and T. Schaub (2000) The role of default logic in knowledge representation. In J. Minker, ed. Logic-Based Artificial Intelligence (Boston: Kluwer Academic Publishers) 107-126.

B. N. Grosof (1997) Courteous Logic Programs: Prioritized Conflict Handling for Rules, IBM Research Report RC 20836, revised.

S. C. Shapiro


References ii l.jpg
References II

J. P. Martins and S. C. Shapiro (1983) Reasoning in multiple belief spaces, Proc. Eighth IJCAI (Los Altos, CA: Morgan Kaufmann) 370-373.

J. P. Martins and S. C. Shapiro (1988) A model for belief revision, Artificial Intelligence 35, 25-79.

S. C. Shapiro (1992) Relevance logic in computer science. In A. R. Anderson, N. D. Belnap, Jr., M. Dunn, et al.Entailment Volume II (Princeton: Princeton University Press) 553-563.

S. C. Shapiro and The SNePS Implementation Group (2002) SNePS 2.6 User's Manual, Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY.

S. C. Shapiro and F. L. Johnson (2000) Automatic belief revision in SNePS. In C. Baral & M. Truszczyński, eds., Proc. 8th International Workshop on Non-Monotonic Reasoning.

S. C. Shapiro


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