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Models of Legal Argumentation. Trevor Bench-Capon Department of Computer Science The University of Liverpool Liverpool UK. Overview. Argument and Proof Arguments Based on Cases HYPO CATO Arguments Based on Rules Bodies of Arguments – Dung Argument Schemes – Toulmin

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Models of legal argumentation l.jpg

Models of Legal Argumentation

Trevor Bench-Capon

Department of Computer Science

The University of Liverpool

Liverpool

UK


Overview l.jpg
Overview

  • Argument and Proof

  • Arguments Based on Cases

    • HYPO

    • CATO

  • Arguments Based on Rules

  • Bodies of Arguments – Dung

  • Argument Schemes – Toulmin

  • Persuasion Using Purpose and Value


Argument and proof l.jpg

Argument

John is old because he is aged 82

Arguments persuade, not compel

Arguments leave things implicit. The hearer fills in the gaps and may be convinced

Proof

John is aged 82

John is a man

All men aged greater than 70 are old

82 > 70

Therefore, John is old

Argument and Proof


Argument and proof4 l.jpg

Arguments may contain open textured concepts

The proof requires a threshold for old

The hearer needs only to accept that 82 is above the threshold

Proof

John is aged 82

John is a man

All men aged greater than 70 are old

82 > 70

Therefore, John is old

Argument and Proof

  • Argument

    • John is old because

    • he is aged 82


Argument and proof5 l.jpg

Arguments may introduce new information

Speaker may assume John is man, but hearer knows he is a tortoise

Proof

John is aged 82

John is a man

All men aged greater than 70 are old

82 > 70

Therefore, John is old

Argument and Proof

  • Argument

    • John is old because

    • he is aged 82


Legal argument l.jpg
Legal Argument

  • Legal Argument displays these typical characteristics of argument:

    • Unstated background and uncontested facts

    • Open texture and context dependent interpretation

    • New information and considerations


Arguments are defeasible l.jpg
Arguments are Defeasible

  • A sound proof has to be accepted

  • But arguments are inherently and inescapably defeasible

    • They may be accepted, or challenged

      • Different audiences may respond differently, accepting for different reasons, or offering different challenges

    • The challenge can be accepted and the argument withdrawn, or it can be rebutted

    • Thus arguments are embedded in a dialectic context – and the audience is important


Arguments based on cases l.jpg
Arguments Based on Cases

Wyoming

Rutgers

Amherst

GREBE

Branting

Industrial Injuries

Semantic Net Based

Pittsburgh

SMILE

IBP

Ashley and Bruninghaus

Trade Secrets Law

Outcome Prediction


Arguments based on cases9 l.jpg
Arguments Based on Cases

  • This was a focus of AI and Law from the beginning

  • I will focus on

    • HYPO (Rissland and Ashley)

    • CATO (Ashley and Aleven)

  • Both systems operate in

    US Trade Secrets Law


Slide10 l.jpg
HYPO

  • Main Features

    • Three-Ply Argument Structure

    • Use of Dimensions to Represent and Compare Cases


Three ply argument l.jpg
Three Ply Argument

  • First Ply:

    • A case is cited by proponent

  • Second Ply:

    • The citation is attacked by opponent

      • By distinguishing the case

      • With a counter example

  • Third Ply:

    • The attack is rebutted by proponent

      • Distinguishing the counter examples


Three ply argument12 l.jpg
Three Ply Argument

  • Provides a simple but effective way of organising the argument

  • Is clearly adversarial in nature

  • Allows for both distinguishing and counter examples

  • Can be considered as an argument scheme


Dimensions l.jpg
Dimensions

  • A dimension is a feature of the case which may need to be considered

    • In Trade Secret Law e.g.

      • Security Measures Adopted

      • Disclosures Subject to Restrictions

      • Competitive Advantage Gained


Dimensions14 l.jpg
Dimensions

  • Are associated with

    • Preconditions

    • A range

    • Facts which determine the position within the range

    • A direction


Security measures adopted l.jpg
Security Measures Adopted

  • Precondition

    • The plaintiff adopted some security measures

  • Range

    • From Minimal measures through some physical measures to nondisclosure agreements

  • Facts

    • List of security measures adopted

  • Direction

    • Stronger measures favour the plaintiff


Disclosures subject to restriction l.jpg
Disclosures Subject to Restriction

  • Precondition

    • Some disclosures were restricted

  • Range

    • 0-100% of disclosures restricted

  • Facts

    • Percentage of disclosures restricted

  • Direction

    • Plaintiff favoured by more restricted disclosures


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Competitive Advantage Gained

  • Precondition

    • Defendant saved development cost

  • Range

    • $10,000 - $10,000,000

  • Facts

    • Plaintiff development time and cost

    • Defendant development time and cost

  • Direction

    • Greater savings favour plaintiff


Hypo trade secret example l.jpg

π = plaintiff

∆ = defendant

HYPO Trade Secret Example

CASE16 Yokana (∆)

F7 Brought-Tools (π)

F10 Secrets-Disclosed-Outsiders (∆)

F16 Info-Reverse-Engineerable (∆)

CASE30 American Precision (π)

F7 Brought-Tools (π)

F16 Info-Reverse-Engineerable (∆)

F21 Knew-Info-Confidential (π)

CASE Mason (?)

F1 Disclosure-in-Negotiations (∆)

F6 Security-Measures (π)

F15 Unique-Product (π)

F16 Info-Reverse-Engineerable (∆)

F21 Knew-Info-Confidential (π)

F10 (∆)

F9 (π)

Yokana(∆)

F7 (π)

F16 (∆)

American

Precision (π)

Mason (?)

F1 (∆)

F21 (π)

F6 (π)

F15 (π)

CFS

F19 (∆)

F18 (π)


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Comparing Cases

  • On the basis of similarities between past cases and the current fact situation, HYPO forms a case lattice



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Typically

  • The first level will contain both plaintiff and defendant cases

  • These are available to be cited

    • KFC, American Precision or Digital Development for plaintiff

    • Speciner, Carver or Speedry for defendant

  • If no case is available at the first level, we would need to descend a level until we found a case supporting our side

    • If F1 absent, Midland Ross or Yokana for defendant


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First Ply (for Plaintiff)

  • Where disclosure in negotiations, security measures, knew information confidential and unique product, plaintiff should win. Digital Development

  • Note that pro-defendant factors are included here


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Distinguishing

  • Either additional pro-defendant factor in current case

  • Or additional pro-plaintiff factor in cited case

  • Thus we may distinguish Mason from Digital Development since the product was reverse engineerable in Mason but not Digital Development.

  • Note that Unique Product does not distinguish Mason from KFC – it makes Mason better


Counter example l.jpg
Counter Example

  • A case at the same level of the case lattice held for the other side

  • E.g. Carver is CE to Digital Development

  • Better a case with all the shared factors and more (“trumping CE”)

  • E.g. American Precision if Midland Ross cited for the defendant


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Third Ply - Rebuttal

  • Distinguishes the counter examples

  • E.g. Carver is distinguishable because security measures, knew information confidential and unique product in Mason, but not Carver


Argument not a decision l.jpg
Argument, not a Decision

  • Except for the trumping, more on point, counter example, we may choose which side should win

  • We may reject the distinctions as unimportant

  • We may follow the cited case or the counter example


Slide27 l.jpg
CATO

  • Also in US Trade Secrets Law

  • Also uses 3 ply argument

  • But

  • Uses factors not dimensions

  • Organises factors into a hierarchy, allowing additional argument moves

  • Some additional rebutting moves


Factors l.jpg
Factors

  • No degree – factors either apply or do not apply

  • The presence of a factor always favours either the plaintiff or the defendant

    • Security measures – plaintiff

    • No security measures – defendant

    • Outsider disclosures restricted – plaintiff

    • Competitive advantage - plaintiff


Factor hierarchy l.jpg
Factor Hierarchy

Info Trade Secret -p

Info valuable -p

Efforts to maintain

Secrecy -p

Security

Measures p

No Security

Measures - d

Competitive

Advantage -p

Waiver of

Confidentiality - d


Emphasising and downplaying distinctions l.jpg
Emphasising and Downplaying Distinctions

  • Precedent: No security measures

  • Case 1: Waiver of Confidentiality

  • Case 2: Security Measures

  • Case1 and Case 2 can both be distinguished because no security measures is absent

  • Case 1 can downplay the distinction because there is an alternative argument against efforts to maintain secrecy

  • Case 2 can emphasise the distinction, because there is now no argument against efforts to maintain secrecy

    Confidentiality


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Argument Moves in CATO

  • Analogising a case to a past case with a favourable outcome

  • Distinguishing a case with an unfavourable outcome;.

  • Downplaying the significance of a decision;

  • Emphasising the significance of a distinction;

  • Citing a favourable case to emphasise strengths;

  • Citing a favourable case to argue that weaknesses

  • are not fatal;

  • Citing a more on point counterexample to a case cited

  • by an opponent;

  • Citing an as on point counter example to a case cited

  • by an opponent.


Arguments based on cases32 l.jpg
Arguments Based on Cases

  • Cases are compared according to common features

    • Features tend to be at a level of abstraction above facts (issues)

  • Arguments mainly based of differences between cases

  • And the significance of these differences


Arguments based on rules l.jpg
Arguments Based on Rules

  • In Law, rules often conflict

    • The person named in a will should inherit

    • A murderer should not inherit

  • Conflicting rules provide an argument for and an argument against

  • How do we resolve such arguments?


Types of conflict l.jpg
Types of Conflict

  • Rules may conflict in several ways:

    • Contradictory conclusions

      • If P then Q, If R then not Q

    • Denial of premises

      • If P then Q, if R then not P

    • Rule inapplicable

      • If P then Q, if R then not (if P then Q)


Resolution through general principles l.jpg
Resolution Through General Principles

  • Prefer most specific rule

    • Statutes are often written as general rule and exceptions

  • Prefer most recent rule

    • A recent case is preferred to an old one

  • Prefer most authoritative rule

    • Supreme court better than lower courts

  • These principles can conflict

  • No general ordering seems possible


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Weighing Reasons

  • We can see the antecedents as reasons for the conclusion

  • Some reasons may be stronger than others

  • We should prefer the stronger reasons to the weaker reasons


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Explicit Rule Priorities

  • We can simply state which of a pair of conflicting rules has priority over the other

  • Note: such priorities may themselves be the subject of debate


Dialogical justification l.jpg
Dialogical Justification

P Q

Proponent wins

R  ¬ Q

Opponent wins

S  ¬ R

Proponent wins

T  ¬ (S  ¬ R)

Opponent wins

Proponent wins

U  ¬ T


Reconstruction of hypo prakken and sartor l.jpg
Reconstruction of HYPOPrakken and Sartor

  • Cases are represented as 3 implications:

    • (i) if pro-plaintiff factors then plaintiff

    • (ii) if pro-defendant factors then defendant

    • (iii) (i) < (ii) if defendant won, else (ii) < (i)

    • May be broadened by omitting factors

    • May be distinguished

    • Are deployed in a dialogue game


Hypo trade secret example40 l.jpg

π = plaintiff

∆ = defendant

HYPO Trade Secret Example

CASE16 Yokana (∆)

F7 Brought-Tools (π)

F10 Secrets-Disclosed-Outsiders (∆)

F16 Info-Reverse-Engineerable (∆)

CASE30 American Precision (π)

F7 Brought-Tools (π)

F16 Info-Reverse-Engineerable (∆)

F21 Knew-Info-Confidential (π)

CASE Mason (?)

F1 Disclosure-in-Negotiations (∆)

F6 Security-Measures (π)

F15 Unique-Product (π)

F16 Info-Reverse-Engineerable (∆)

F21 Knew-Info-Confidential (π)

F10 (∆)

F9 (π)

Yokana(∆)

F7 (π)

F16 (∆)

American

Precision (π)

Mason (?)

F1 (∆)

F21 (π)

F6 (π)

F15 (π)

CFS

F19 (∆)

F18 (π)


Example l.jpg
Example

  • Yokana gives 3 rules

    • R1: F7  P

    • R2: F16 & F10  D

    • R3: R2 > R1

  • American Precision gives 3 rules

    • R4: F7 & F21  P

    • R5: F16  D

    • R6: R4 > R5


Rationales loui and norman l.jpg
Rationales – Loui and Norman

  • This records the progress of the dispute which may be important.

    • Consider a precedent which has F1 and F2 favouring plaintiff and F3 favouring the defendant and was won by plaintiff

    • Given a new case with only F1 it is unclear that the plaintiff should win

    • But suppose F2 was used to defeat F3: Now it can be seen that F1 can stand alone


Compare l.jpg

R4: F1  P

R2: F3  D

R5: F2  not (F3  D)

R6: R5 > R2

Now we can confidently apply R4

R1: F1 & F2  P

R2: F3  D

R3: R1 > R2

Not clear that

R4: F1  P

Compare

We need a record of the dispute to decide

which description is the right one


Argumentation frameworks l.jpg
Argumentation Frameworks

  • We can often view a legal dispute as a set of conflicting arguments

  • P.M. Dung has developed an elegant way of looking at and reasoning about sets of conflicting arguments


Dung s argument framework l.jpg
Dung’s Argument Framework

  • Introduced in AIJ 1995

  • Arguments at their most abstract

    • Only: which other arguments does an argument attack?

  • Attacks always succeed

    • We cannot accept an argument and its attacker


Definitions l.jpg
Definitions

An argumentation framework is a pair

AF = <AR, attacks>

  • Where AR is a set of arguments and attacks is a binary relation on AR, i.e. attacksAR  AR.

    An argument AAR is acceptable with respect to set of arguments S if:

    (x)((xAR) &(attacks(x,A))  (y)(y S) & attacks(y,x).

    A set S of arguments isconflict-freeif

    (x) (y)( xS) & (y S) & attacks(x,y).

    A conflict-free set of arguments S is admissible if

    (x)((xS)  acceptable(x,S).


Preferred extension l.jpg
Preferred Extension

  • A set of arguments S in an argumentation framework AF is a preferred extension if it isa maximal (with respect to set inclusion) admissible set of AR.

  • Preferred Extensions are interesting because they represent maximal coherent positions, able to defend themselves against all attackers

  • BUT: there may be multiple preferred extensions, and no way to choose between them


Odd cycle l.jpg
Odd Cycle

Preferred Extension

is the empty set

We can’t accept

Anything here

a

Akin to Paradoxes

b

c


Even cycle l.jpg
Even Cycle

We can accept

Either a and c

Or b and d

Two

Preferred Extensions

{a,c} and {b,d}

a

d

b

Akin to

Dilemmas

c


In general l.jpg
In general

  • Every AF has a preferred extension

    • Which may be the empty set

  • AFs do not have a unique preferred extension

    • Even cycles give rise to choices

  • An argument may be in every preferred extension (sceptically acceptable)

  • An argument may be in some preferred extensions (credulously acceptable)

  • An argument may be in no preferred extension (indefensible)


Decision problems and complexity l.jpg
Decision Problems and Complexity

Proofs of these results can be found in a series of

papers by Paul Dunne and myself.


Example set of cases l.jpg
Example Set of Cases

  • Pierson: Plaintiff is hunting a fox on open land. Defendant kills the fox.

  • Keeble: Plaintiff is a professional hunter. Lures ducks to his pond. Defendant scares the ducks away

  • Young: Plaintiff is a professional fisherman. Spreads his nets. Defendant gets inside the nets and catches the fish.


Ghen vs rich l.jpg
Ghen vs Rich:

  • Ghen harpooned a whale, lost it. Ellis found it, sold it to Rich, who processed it.

  • Found for Ghen.

    • “the iron holds the whale”

  • Whaling is governed by conventions which the court respects


Conti vs aspca l.jpg
Conti vs ASPCA

  • Chester, a talking parrot used by ASPCA for educational purposes, escaped. Conti found it and kept it as a pet. ASPCA reclaimed it.

  • Found for ASPCA

  • Chester was domesticated, and so ferae nauturae did not apply


Burros cases l.jpg
Burros Cases

  • New Mexico vs Morton

  • Kleepe vs New Mexico

    • Unbranded burros straying from state lands

    • Showed that:

      • Branding established possession

      • Presence on land had to be more than accidental straying


Representing keeble l.jpg
Representing Keeble

  • A: Pursuer had a right to the animal

  • B: Pursuer not in possession

  • C: Owns the land (so owns the animals)

  • D: Wild animals not confined

  • E: Efforts made to secure animals

  • F: Pursuer has right to pursue livelihood unmolested


Keeble as af l.jpg

Two ways

to win

Keeble as AF

A

B

C

F

D

E

Preferred extension is {A,C,E,F}


Pierson as af l.jpg
Pierson as AF

  • {A, B,E} as in Keeble

  • I, M: Pursuit is not enough

  • J: Hypothetical: the animal was taken

  • K: Hypothetical: animal was wounded

  • L: Hypothetical Certain control is enough

  • O: Reasonable prospect of capture

  • P : Reasonable prospect too uncertain

  • R: Reasonable prospect encourages desirable activity

  • G: Not relevant: Interferer was trespassing

  • H: Not relevant: Pursuer was trespassing

  • Q: The land was open

Situations

which would

establish right

values

Excludes

some past

cases


Pierson as af59 l.jpg

Two cycle

Pierson as AF

A

G

M (P)

O (R)

H

B

Q

I

L

J

K

E

Preferred extensions are {B,I,M,P,Q} and {A,E,O,Q,R}


Young as af l.jpg
Young as AF

  • Arguments in Pierson are all relevant– but now L is applicable and P is not

  • F from Keeble is present

  • S: Defendant was in competition with the plaintiff

  • T: The competition was unfair

  • U: Not for the court to rule on what is unfair competition.


Young as af trespass omitted l.jpg
Young as AF(Trespass omitted)

A

F

M (P)

O (R)

B

U

I

L

T

S

J

K

E

Preferred extension is {B,L,S,U} Argument U breaks the 4 cycles


Ghen versus rich l.jpg
Ghen Versus Rich

  • New Argument V:

    • The iron holds the whale is a convention throughout the whaling industry

  • Attacks U: establishes what is unfair competition is whaling

  • Attacks B: Establishes what counts as possession in whaling


Ghen as af trespass omitted l.jpg
Ghen as AF(Trespass omitted)

A

F

M (P)

O (R)

V

B

U

I

L

T

S

J

K

E

Preferred extension is {A,E,F,K,T,V}


Conti and burros cases l.jpg
Conti and Burros Cases

  • Add some special cases

    • W: Domestication sufficient

    • X: Unbranded animals go to the owner of the land

    • Y: Branding sufficient

    • Z: Animals must live on the land: straying on to someone’s land does not affect title


Effect on af l.jpg
Effect on AF

A

W

B

C

F

X

Y

Z

D

E


Argumentation framework for animals cases l.jpg

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

O

[R]

E

I

Q

S

J

K

L

T

U

V

Argumentation Framework for Animals Cases

Analysis taken

from

Bench-Capon 2002

Jurix 2002

N


Some cycles here l.jpg
Some cycles here

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

O

[R]

E

I

Q

N

S

J

K

L

T

U

V


Argument schemes l.jpg
Argument Schemes

  • In Dung’s framework anything can count as an argument, and anything can count as an attack

  • Argument schemes suggest a form that arguments should have

  • Argument schemes prescribe what will count as an attack


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Modus Ponens as Argument Scheme

  • Form:

    • If Antecedent then Consequent and

    • Antecedent: therefore

    • Consequent

  • Attacks:

    • Consequent is not the case

    • Antecedent is not true

    • Consequent does not follow from Antecedent

Compare: The three kinds of conflict for rule systems


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Witness Testimony

  • Form:

    • Witness 1 says that A and

    • Witness 1 is an a position to have observed A; therefore

    • A

  • Attacks:

    • Witness 2 says A is not the case

    • Witness 1 not in position to have observed A

    • Witness 1 is mistaken

    • Witness 1 is lying


Toulmin argument schema l.jpg
Toulmin Argument Schema

  • One general Argument Schema that has been much used in AI and Law derives from Stephen Toulmin.

  • Introduces

    • Modal Qualification

    • Backing

    • Rebuttal


Toulmin s argument scheme l.jpg
Toulmin’s Argument Scheme

Data

Claim

Modal

Rebuttal

Warrant

Backing


Toulmin example l.jpg
Toulmin Example

John is

82

John is

old

Normally

Over 80

Is old

John is a

Tortoise

Demographic

Data


Useful l.jpg
Useful

  • To identify different roles for premises:

    • Basic data

    • General Rules

    • Justification

    • Degree of support

    • Exceptions

  • Used

    • in explanation,

    • to organise the presentation of an argument

    • as the basis of dialogue games


Systems using toulmin l.jpg
Systems Using Toulmin

  • Toulmin’s schema (sometimes adapted) has been used by

    • Marshall (1989) – organisation of legal argument

    • Lutomski (1989) – presentation of expert testimony

    • Stoors (1991) – organisation of policy argument

    • Bench-Capon (1985) – explanation

    • Bench-Capon (1998) – dialogue game

    • Zeleznikow and Stranieri - explanation


Argument schemes76 l.jpg
Argument Schemes

  • Potentially a very fruitful area of study

    • Especially particular schema (such as witness testimony)

  • As yet rather under researched


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Disagreement and Persuasion

  • In the remainder of the talk I will look at some of my current work

  • The starting point is why do people disagree? And when they do, how do they persuade one another?

  • I will look at

    • an extension to Dung’s framework,

    • an argument scheme for persuasive argument


Perelman says l.jpg
Perelman says:

  • If men oppose each other concerning a decision to be taken, it is not because they commit some error of logic or calculation. They discuss apropos the applicable rule, the ends to be considered, the meaning to be given to values, the interpretation and characterisation of facts.


Taxation debate l.jpg

Lower taxes to

promote enterprise

Raise taxes to

promote equality

Taxation Debate

Brown sees force in both arguments

– but what Brown does depends on (reveals?) whether Brown

prefers equality or enterprise at a given time


To allow for rational disagreement l.jpg
To allow for rationaldisagreement

  • We must distinguish attack from defeat

  • We can accept arguments which are attacked, AND their attackers, provided the attacks fail

  • Dung’s framework is too abstract to allow such talk – we need to be able to discuss value as well as conflict


Value based argumentation framework l.jpg

Set of Possible

Audiences

As for

Standard AF

Set of

values

Function

Mapping

Elements of AR

To Elements of V

Value-basedArgumentation Framework

A value-basedargumentation

framework (VAF) is a 5-tuple:

VAF = <AR, attacks,V,val, P>


Audiences l.jpg
Audiences

  • Following Perelman we want to use the notion of an audience

  • Audiences will have different preferences between values

  • We individuate audiences by their ordering on values

  • There are as many audiences as there are value orderings


Audience specific vaf l.jpg

Valprefa is the value

preferences

of audience a

As for

Standard AF

Set of

values

Function

Mapping

Elements of AR

To Elements of V

Audience Specific VAF

An audience specific VAF (AVAF)

is a 5-tuple:

AVAF = <AR, attacks,V,val, Valprefa>


Defeat in avaf l.jpg
Defeat in AVAF

An argument A  AF defeatsa an argument

B AF for audience a if and only if both attacks(A,B) and

not valprefa(val(B),val(A)).

Note: An argument is defeated by an attacker with the same value

Defeat is always relative to an audience

If there is only one value in V we have a standard argumentation framework


Definitions for avaf l.jpg
Definitions for AVAF

  • An argument AAR is acceptable to audience a with respect to set of arguments S, if:

    (x)((xAR & defeatsa(x,A))  (y)((y S) & defeatsa(y,x))).

  • A set S of arguments is conflict-free for audience a if

    (x) (y)(( xS & y S) 

    (attacks(x,y)  valpref(val(y),val(x)  valprefa))).

  • A conflict-free set of arguments S is admissible for audience a if

    (x)(xS  acceptablea(x,S)).


Preferred extension of an avaf l.jpg
Preferred Extension of an AVAF

  • A set of arguments S in an value-based argumentation framework is a preferred extension for audience a if it isa maximal (with respect to set inclusion) admissible for audience a set of AR.


Objective acceptance l.jpg
Objective Acceptance

  • An argument is objectively acceptable if it is in the preferred extension for every audience

  • An argument if subjectively acceptable if it is in the preferred extension for some audience

  • An argument is indefensible if it is no preferred extension of any audience


Two valued three cycle l.jpg

Note: b is in the preferred extension whatever

the value order

Two Valued Three Cycle

If blue > red, preferred

extension is {a,b}

If red > blue, preferred extension is {b,c}

a

b

c


Two valued four cycle connected colours l.jpg
Two Valued Four CycleConnected Colours

If blue > red, preferred

extension is {a,c}

If red > blue, preferred

extension is {a,c}

a

d

b

Preferred extension

is unique, AND independent

of value order

c


Some technical results on vafs l.jpg
Some Technical Results on VAFS

  • If there are no cycles with a single value, then there is a unique preferred extension

    • Efficient algorithm to find the preferred extension

  • Cycles can give rise to objective acceptance

    • Odd cycles with more than one value

    • Some configurations of even cycles

  • Possibilities to prune lines of argument with repeating values

  • Heuristics to select attacks

Note: what causes

difficulties without

values is a source of

Objective Acceptance

with them


Recall that there were cycles in our animals cases l.jpg
Recall that there were Cycles in our Animals Cases

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

O

[R]

How does

considering

values help?

E

I

Q

N

S

J

K

L

T

U

V


Pierson l.jpg

Blue: Need clear law

A: Pierson Had A Right

To the Animal

Orange: Encourage useful activity

Pierson

B: Pierson had

No possession

E: Pierson was in

full pursuit

W

A

M and O

form a

2-cycle:

resolved

by Value

Y

Z

F

B

C

X

I: Pursuit not

Enough

G

H

D

M

[P]

O: Seizure not

necessary (we

want to

encourage socially

useful activity)

O

[R]

E

So A is

Subjectively

acceptable

I

Q

N

S

J

K

L

T

U

V

M: we must insist on

possession for clear law


Keeble i l.jpg

C: owns the land so

possesses the animals

Green: Protect property rights

Keeble I

D: Animals not confined

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

X: Unbranded

animals belong

to landowner.

Not needed:

Useless if blue

greater than green

Unnecessary else

O

[R]

E

I

Q

N

S

J

K

L

T

U

V


Keeble ii l.jpg

Red: Promote economic activity

F: Keeble was pursuing

his livelihood

Keeble II

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

O

[R]

E

I

Q

N

S

J

K

L

T

U

V


Young l.jpg

Purple: Restrictive view of role of courts

S: Defendant in

Competition

Young

T: Competition was

Unfair

U breaks

the even

cycle

BTSEB

Without U

B is

defeated

by its

position

in the

even

cycle

Note:

4 cycle

BTSEB

TE objectively

acceptable

W

A

Y

Z

F

B

C

X

G

H

D

M

[P]

U: Not for the

Court to rule

on what is unfair

competition

O

[R]

E

I

Q

N

S

J

K

L

T

U

V


Schema for persuasive argument l.jpg
Schema For Persuasive Argument

  • To consider individual arguments we need to look inside the nodes to see what an argument looks like and how it can be attacked

  • We have developed a general schema for persuasive argument in practical reasoning

  • This schema can be applied to reasoning with legal cases


Form of justification of an action l.jpg
Form of Justification of An Action

  • It was right to do action A

  • In those circumstances R

  • To bring about these new circumstances S

  • Which realised this goal G

  • Which promoted this value V


Schematically l.jpg

A

R  S  G  V

Schematically

The value is the purpose

for which we wanted to

realise the goal

The goal is the subset

of S that we wanted to

bring about, the reason we

did A

Effect of an

action depends

on the situation

We refer to a justification of this

form as a position


Attacking a position l.jpg
Attacking A Position

  • A position can be attacked in a variety of ways:

    • Denial of an element

      • E.g. A will not produce S from R

    • Contradiction of an element

      • E.g. G promotes W not V

    • Alternatives

      • E.g. B will also produce S from R

    • Side Effects

      • E.g. G demotes W as well as promoting V

We have identified 15 possible attacks –

some with variants


Law as practical reasoning l.jpg
Law As Practical Reasoning

  • We need to choose one of two actions

    • Decide for the plaintiff

    • Decide for the defendant

  • Circumstances are the case facts + a record of the decision

  • Goals are subsets of the facts + a record of the decisions

  • Values are behaviours to encourage and discourage

Note: we see the judgement as a choice of action

Not the identification of a property of the case


To illustrate l.jpg
To Illustrate

F1 F2 F3 … Fn Pwins Dwins

Undecided Case

1 0 0 1 0 0

Deciding for P produces

F1 F2 F3 … Fn Pwins Dwins

Decided Case

1 0 0 1 1 0

selectionfrom

decided case

Goal

F1 F2 F3 … Fn Pwins Dwins

0 1 1 0

Encourages potential plaintiffs to realise Fn and not F2


In this representation l.jpg
In this Representation

  • 7 of the 15 attacks are not possible

  • 2 pairs of attacks are identical

    • Only two actions

    • Actions always achieve the same result

    • Goals straightforward consequences of decided case

    • Distinct actions realise distinct goals

  • One attack has two distinct variants

  • Thus we can look for seven distinct forms of attack


Attacks and argument moves l.jpg
Attacks and Argument Moves

  • Two challenge the representation

    • Factors used to represent a case

    • Values associated with factors

  • Four are variants of distinguishing a case

  • One seems to be in neither HYPO nor CATO: disagreement as to which value is promoted in this context


Four types of distinction l.jpg
Four Types of Distinction

  • Precedent stronger: can be downplayed

  • Current case weaker: can be downplayed

  • Precedent stronger: can be emphasised

  • Current case weaker: can be emphasised

A single move

in HYPO

Two moves in

CATO


Counter examples l.jpg
Counter Examples

  • A different position based on another precedent justifying the other action

    • Can be attacked in the same ways as the original position

  • A rebuttal of the choice of goal

    • Same factors as G, but different outcome

    • Can only be met by reformulating the goal

    • Like a trumping counter example in HYPO


Summary l.jpg
Summary

  • We have seen

    • How arguments differ from proof

    • Two systems for case based argument in AI and Law

    • Arguments based on conflicting rules

    • Reasoning about sets of arguments

    • Argument schemes

    • How notions of value and purpose can be used