legal argumentation

Modeling Legal Argument

Dr. Thomas F. Gordon

Fraunhofer FOKUS, BerlinApril, 2008

Outline

Introduction to Argumentation Theory

Argument from Ontologies

Argument from Rules

Argument from Cases

Schedule

Monday, May 16, 15:00-18:00 (3 hr) Introduction to Argumentation Theory (1.5 hr) Practice Session: Argument Reconstruction and Modeling (1.5 hr)

Tuesday Morning, May 17, 10:00-12:30 (2.5 hr) Argument from Ontologies (1.25 hr) Argument from Rules (1.25 hr)

Tuesday Afternoon, May 17, 14:00-16:00 (2 hr) Practice Session: Modeling Legislation (2 hr)

Wednesday, May 21, 16:00-18:00 (2 hr) Argument from Cases (1 hr) Practice Session: Case-Based Reasoning (1 hr)

Introduction to Argumentation Theory



Examples of Practical Problems, Small and Large

Deciding where to go for dinner.

Designing a legal knowledge representation language.

Deciding whether to republish Danish cartoons depicting Mohammed.

Deciding where to build a new airport.

Deciding how best to end the war in Iraq.

Deciding how to reduce global warming.

Legal Problems are Practical Problems

Legal Assessment

Deciding whether a citizen is entitled to social benefits Determining tax obligations Deciding a criminal case

Legal Planning

Estate planning Tax planning Drafting contracts Legislative policy development Legislative drafting

Conditions Typical During The Making of Practical Decisions

Both too much and not enough information available.

Resources are limited, e.g. time, persons, money.

Expected value of the outcome is not high enough to warrant the development of a computer program or knowledge-base. ad hoc problems.

Opinions differ about the truth, relevance or value of available information.

Arguments can be made both pro and con any proposed solution.

Reasoning is defeasible: further information can cause some alternative to become preferable.

Value judgments are at least as important as facts or knowledge. (ethical, legal, political, business, aesthetic issues). What makes one solution better than another?

Many persons are affected. Conflicts of interest are inevitable. Negotiation required.

Argumentation Research

Interdisciplinary field: philosophy, communications studies, computer science, artificial intelligence

Modern study of argumentation began with Stephan Toulmins Uses of Argument in 1958

Aims to provide a comprehensive, normative theory of logic, dialectic and rhetoric for practical reasoning

Also known as Informal Logic

Not because it does not use formal or computational models. But because the acceptability of some proposition at issue does not depend only on its logical

form

Rather, argumentation is contextual; acceptability depends on specific reasoning conventions of the application field or domain.

Philosophical Roots of Argumentation Theory

The ancient Greeks recognized and studied several normative sciences :

Logic the study of inference relations Rhetoric - the study of effective communication Dialectic - the study of the norms and methods for resolving

conflicting views, ideas and opinions

Logic was understood broadly, including what we now call, defeasible, nonmonotonic or presumptive reasoning, as well as deductive and inductive forms of inference.

The study of presumptive inference and dialectic has been largely neglected since then. In the first half the twentieth century, especially, the field of formal, mathematical logic focused on deductive inference relations.

Carneades (c. 213 - c. 128 B.C.)

Argumentation Tasks

Construct Arguments

Apply Schemas

Present/Visualize

Arguments

Participant

Moderator

Reconstruct Arguments

Moderate Dialogues

Evaluate & Compare

Arguments

Decide Issues

Authority

Logical Layer

Dialectical Layer

Apply Protocols

Rhetorical LayerSelect Moves

Manage Commitments

Manage Knowledge

(KBS)

A Dictionary Definition of Dialectic

1. the art of investigating or discussing the truth of opinions.

2. inquiry into metaphysical contradictions and their solutions.

the existence or action of opposing social forces, concepts, etc.

The ancient Greeks used the term dialectic to refer to various methods of reasoning and discussion in order to discover the truth. More recently, Kant applied the term to the criticism of the contradictions that arise from supposing knowledge of objects beyond the limits of experience, e.g., the soul. Hegel applied the term to the process of thought by which apparent contradictions (which he termed thesis and antithesis) are seen to be part of a higher truth (synthesis).

source: The New Oxford Dictionary (emphasis added)

Core Dialectical Idea: Opposition (Contradiction) and its Resolution

Opposing arguments (pro vs con)

Opposing interests (proponent vs. opponent)

Opposing ideas (e.g. thesis vs. antithesis, resolved by synthesis)

What is an Argument? (Informally)

Arguments link a set of premises to a conclusion. The conclusion and each premise are declarative statements. The premises (are intended to) support the conclusion; provide reasons for accepting or

believing the conclusion.

Examples:

Socrates is a man, therefore Socrates is mortal. John is 75 years old, therefore John is old.

Premises may be of different kinds, play different roles. The classical theory of syllogism, e.g., distinguished major and minor premises:

major premise a generalization, e.g. all men are mortal. minor premise a specific fact, e.g. Socrates is a man.

Argument vs. Proof

The premises of an argument provide reasons to accept the conclusion, but the conclusion need not be a necessary logical consequence of the premises.

Arguments can be defeasible. The conclusion of an argument is not necessarily true, but may be only presumptively true. Adding premises to an argument can cause it to fail to support the conclusion. (cf. nonmonotonicity) Example:

In John is 75 therefore John is old, suppose we add the premise John is a tortoise.

Premises required to make the argument deductively valid may be missing or implicit. Example:

In Socrates is a man, therefore Socrates is mortal., the major premise, All men are mortal is implicit.

Inductive Arguments. It rained yesterday and today, therefore it will rain tomorrow.

Abductive Arguments. It is wet outside this morning, therefore it rained last night.

General Rules with Exceptions. The train leaves every day at 9:15, therefore it will leave today at 9:15. But today is a holiday.

Open Texture Concepts. Vehicles are not allowed in the park, therefore baby carriages are not allowed. But are baby carriages to be considered vehicles in this context?

None of these are deductively valid, but they may be good arguments nonetheless.

Further Examples

Argumentation Schemes

Generalize the concept of an inference rule to cover presumptive as well as deductive and inductive forms of argument.

Are conventional patterns of argument.

Come with a set of critical questions for evaluating and challenging arguments.

Useful for several purposes:

Recognizing, classifying or identifying an argument as an instance of some scheme; Critically evaluating an argument, using critical questions of the scheme; Methods for constructing, generating or inventing new arguments.

Many schemes are field dependent (domain specific)

Legal argumentation is legal because of its special purpose, legal argumentation schemes and procedures.

Catalog of Argumentation Schemes

About 60 argumentation schemes have been identified by Douglas Walton and his colleagues.

Work on classifying schemes is ongoing research. (Taxonomy or ontology)

Examples

Argument from Expert Opinion Argument from Popular Opinion Argument from Analogy Argument from Correlation to Cause Argument from Consequences Argument from Sign Argument from Verbal Classification

Theories of Validity

Relational Theories. The relationship between the premises and conclusion of an argument is sufficient for determining whether or not the argument is valid. Examples

Classical Logic: The argument (inference) is deductively valid iff the conclusion is a necessary (logical) consequence of the premises.

Nonmonotonic Logics: The argument is valid iff the conclusion is a defeasible consequence of the premises. (Nonmonotonic logics vary in how they define the defeasible consequence relation.)

Dialectical Theories. An argument is valid only if it furthers the goals of the dialogue in which it is used. Validity can depend on how the argument is used in a process.

Doug Waltons New Dialectic

Validity can depend on the context of the argument in a dialog

Who made the argument?

In what role?

When? (which move)

In what kind of dialog?

Reference: Walton, The New Dialectic, 1998

Reconstruction of Deductively Valid Arguments

Defendants of classical logic note that non-deductive arguments (often) can often be reconstructed as deductively valid arguments.

But many questions remain, such as:

1. May only deductively valid arguments be asserted in dialogue? If not, what norms do apply?

2. Which party has the burden to reveal hidden premises? When must this be done? Does this depend on the dialog type?

Waltons Dialogue Typology

Some Legal Dialog Types

Courts Pleading Trial Appellate Argument

Public Administration Claims processing Citizen consultation and participation in legislative

processes

Legislature Policy development Legislative drafting

Dialogs between attorneys and their clients Tax and estate planning Drafting contracts

The Process of Reconstructing and Evaluating Arguments

1.Select natural language texts to analyze

2.Create a key list of statements in the texts.

3. Identify arguments in the text, associating their premises and conclusions with statements in the key list.

4.Classify arguments, using argumentation schemes as patterns

5.Use these schemes to help reveal implicit premises in the arguments.

6.Assign burden of proof and proof standards to the statements at issue.

7.Accept or reject statements which are assumed to be true of false, respectively, and not at issue.

8.Evaluate the acceptability of statements at issue, using the proof assignments and standards.

Legal Knowledge Interchange Format (LKIF)

XML formats for representing and interchanging legal knowledge

Developed in the European ESTRELLA project

Covers

Terminology (ontologies) Rules Precedent cases Arguments

Statements

Form:

Examples

Sally is a woman. Sally has husband Joe. Joe is a man. Joe has wife Sally.

A set of such statements can be visualized as a directed graph (see figure).

John

Sally

wife

man

is ahusband

woman

is a

LKIF XML Grammar for Statements (Simplified)

Grammar

Statement = element s { attribute id { xsd:ID }?, attribute summary { xsd:string }? }

Example key list of statements in this format

LKIF XML Grammar for Arguments (simplified)

Argument = element argument { attribute id { xsd:ID }, attribute direction { "pro" | "con" }?, Premise*, Conclusion }

Premise = element premise { attribute polarity { "positive" | "negative" }?, attribute role { text }?, attribute statement { xsd:IDREF } }

Conclusion = element conclusion { attribute statement { xsd:IDREF } }

Example Argument in LKIF XML Format

Practice Session 1. Argument Reconstruction and Modeling

Reconstruct the arguments of the following dialog* and represent them in LKIF XML

Helen (1): A problem with tipping is that sometimes it is very difficult to know how much to tip. Bob (1): Its not so difficult. If youve got excellent service, give a tip. Otherwise dont tip. Helen (2): But how much should one tip? Bob (2): Just use your common sense. Helen (3): Common sense is often wrong, isnt it? What kind of criterion is that? Bob (3): Like most things in life, if you want to do something good, you have to use common sense. Helen (4): With tipping, common sense leaves too much open to uncertainty. Because of this uncertainty, both

the tipper and the receiver can be uncomfortable. It the tip is too low, the receiver is uncomfortable. It the tip is too high, the tipper is uncomfortable.

Bob (4): A lot of students depend on tips to help pay for their college education. A college education is a good thing. Discontinuing tipping would mean fewer students could afford college.

Helen (5): Thats no problem. All we need to do is raise the minimum wage. Bob (5): That might put a lot of restaurants out of business, resulting in job losses for students and others.

* source: Douglas Walton, Fundamentals of Critical Argumentation, 2006, pg. 3

Bring additional dialogs to use, in case we have time left. Legal examples?

Argument from Ontologies



What are Ontologies?

In philosophy, ontology is the study of conceptions of reality and being. That is, ontology, like biology, is a research field. Some questions addressed: What is existence? What is an object? How do objects retain their identify as they change?

In computer science, an ontology is an advanced kind of entity-relationship data model. Ontologies in CS are formal models of concepts and relations, including a set of logical formulas, called

terminological axioms.

Ontologies are used to standardize the semantics of data models, to facilitate the interchange of data among programs, abstracting away syntactic and other details.

Example Ontology: LKIF Core Legal Concepts

Utility of Ontologies

Legislation, regulations, precedent cases and other sources of norms are expressed in natural language, making use of legal (and nonlegal) terms.

Terms in natural language are overloaded: one term may be used to mean different concepts (or relations) in different contexts. Conversely, several terms may be used for the same concept (synonyms).

Formal ontologies provide a precise way to model concepts and relations and to associate natural language terms to their intended meanings.

Things in the Real World

Terms of Natural Language

Ontology

denote model(positivism)

agenttime

contract

offer house

boat boat contractthree

triangle

Abstract Ideas and Concepts

model(rationalism)

number freedom

corporation

housetime

contract

denote

systematize

geometry

Relation of Ontologies to Theories

Ontologies are part of a theory of some domain (e.g. law of contracts)

A theory of a domain is a (possibly infinite) set of statements about the domain.

An axiomatization of a theory consists of A formal language (L): A set of function and predicate symbols and syntactic rules for forming sentences from

these symbols.

A finite set of axioms (A): A finite set of statements from which the entire theory can be derived using some set of inference rules.

Types of Axioms (Examples)

Terminological axioms (T-Box): structures concepts and relations (ontology)

Assertional axioms (A-Box) : assert facts about instances of concepts.

Normative axioms: classify instances of concepts using legal concepts (permitted or obligatory actions, whether an agreement is a contract, etc.)

Interpretation axioms: map concepts from one ontology to another, e.g. nonlegal concepts to legal concepts. (cf. subsumption) Example: Is a skateboard a vehicle in the sense of traffic law?

Note: It is not always easy to classify axioms. Many ontologies include axioms which arguably are not terminological.

Theory (infinite)

Axioms

Ontology

Points to Remember

An ontology is but one part of the axiomatization of a theory (knowledge base)

Like all theories, ontologies can be critically evaluated or challenged: Consistent? Validity, with respect to the concepts of the relevant community? Coherence? Practical utility?

Example Ontology Argumentation Scheme

Scheme for Argument from Verbal Classification [Walton 2006, pg. 129]

Premises Individual Premise. x has property F Classification Premise. For all y, if y has property F, then y has property G

Conclusion x has property G

Critical Questions Is the classification premise based on a definition that is subject to doubt?

Argument from Ontology as Kind of Argument from Theory

Argument from ontology is a special kind of argument from theory, using only the terminological axioms of the theory

Deductive Inference The deduction of a proposition p, from the axioms of a theory. Denoted: T p The deduction is valid if and only if p is necessarily true when all of the axioms in T are true. (logical entailment)

Denoted: T p

Argument from theory derivability premise: T p theory premise: all the propositions in T are true in the intended domain. conclusion: p

Critical Questions Even though p is necessarily true if T is true, the argument can be challenged by questioning the minor premise. Is

the theory T true, coherent, etc. Thus the conclusion of a deductive argument is, like all arguments, only conditionally and presumptively true.

Relation of Ontologies to Rules

Recall: ontologies are the terminological axioms of some theory.

Theories are axiomatized using logic.

There are many logics to choose from: propositional logic, predicate logic, deontic logic, etc.

The language of most logics has some kind of conditional (if then ) statement. In propositional and predicate logic, e.g., the material conditional, denoted A B.

Such conditionals are often called rules. Rules of this kind can be used to formalize ontologies.

But laws and regulations are rules of another kind. (more on this later)

Formalizing Ontologies

Any logic can be used, but ...

Since only the terminological axioms of a theory need to be represented, special purpose description logics have been developed for this purpose.

Possible Advantages Decidability Efficiency (more formally, in some cases tractability) Ease of use Similar to familiar data modeling methods in software engineering: Object-oriented programming, Entity-

Relationship models, Unified Modeling Language (UML).

Can be visualized graphically

Description Logic Overview (Simplified)

Description Logic Predicate Logic Meaning Example

C Dwhere C and D are classes (concepts)

D(x) C(x) Cs are Ds. C is a subclass of D.

Peguins are birds.

Q Pwhere Q and P are properties (roles)

P(x,y) Q(x,y) Qs of x are Ps of x.Q is a subproperty of P.

The mother of a person is a parent of the person.

R . C C(y) R(x,y) Every R of x is a C.The range of R is C.

The mother of a person is a woman.

C R . D C(x) R(x,y) D(y) Objects which have an R which is a D is a C.

Persons who own a home in Bel Air are wealthy.

C D E E(x) C(x) D(x) Instances of both C and D are also instances of E.

Anything which is male and human is a man.

C D E D(x) C(x)E(x) C(x)

Instances of C are also instances of both D and E.

Every woman is human and female.

Services Provided by Description Logic Reasoners

Satisfiability Can any object be an instance of some concept C? Is the concept consistent? Is it logically possible for some object to be

an instance of this concept?

Subsumption Is every C necessarily a D? Not the same as subsumption in the law, which ask whether the facts of a case can be subsumed under some legal

term. Example: Is a baby carriage a vehicle in the sense of the traffic code?

Instance Checking Is some object an instance of a given concept. Is x a C?

Retrieval Find all objects which are instances of some given concept. What are the members of C?

Realization For a given object, find all concepts it instantiates. What is x?

LKIF Core Ontology of Legal Concepts Module Dependencies

Top Mereology

Time

Place

Process Action Role

Expression

LegalRole

LegalAction

Rules

Norm

TimeModification

Core

http://www.estrellaproject.org/lkif-core/lkif-core.owl

Protg Demo

Argument from Rules



Many Kinds of Rules

Law & Ethics Regulations, Statutes Principals Morals, Conventions

Logic

Material Implications. For example: x. man(x) mortal(x) Inference rules. For example:

P Q, P Q

Computer Science Production rules Rewrite rules Grammar rules

(modus ponens)

Here, we mean rules in the legal sense.

Features of Rules

Rules are reified objects with properties, e.g. date of enactment.

Rules are subject to exceptions.

Rules can conflict.

Some conflicts can be resolved using rules about rule priorities, e.g. lex superior.

Rules can be excluded from being applicable by other rules

Rules can be invalid. Deleting invalid rules from the KB is not an option.

There is much consensus in AI and Law about these features [Gordon 1993; Hage 1993; Prakken & Sartor, 1996]

Scheme for Arguments from Rules

Premises r is a legal rule with conditions a1, , an and conclusion c. Each ai in a1, ..., an is presumably true.

Conclusion c is presumably true.

Critical Questions Does some exception to r apply? Is some assumption of r not met? Is r a valid legal rule? Does some rule excluding r apply in this case? Does some conflicting rule of higher priority apply in this case?

LKIF XML Grammar for Statements (Full Version)

Grammar

Statement = element s { attribute id { xsd:ID }?, attribute summary { xsd:string }?, attribute src { xsd:anyURI }?, ((text* & Statement*)*)? }

Examples

movable ?x

goods ?x

Example of a rule with an exception

9-105h states that movable things are good, except for money.

In LKIF XML:

movable ?x money ?x goods ?x

Rule Grammar (LKIF XML)

Rule = element rule { attribute id { xsd:ID }?, attribute strict { "no" | "yes" }?, (Literal+ | Implies)}

Literal = Statement | NotNot = element not { Statement }Implies = (Head, Body) | (Body, Head) Head = element head { Literal+ }Body = element body { Or | Condition+ }Or = element or { (Condition | And)+ }And = element and { Condition+ }

Condition = Literal | element if { attribute role { text }?, Literal } | element unless { attribute role { text }?, Literal } | element assuming { attribute role { text }?, Literal }

Statements about Statements

The obligation of X to support Y is excluded from 1601 BGB

excluded s1601-BGB obligated-to-support ?x ?y

1601 BGB applies to the obligation of John to support Susan

applies s1601-BGB obligation-to-support John Susan

9-105-h-i excludes money from the definition of goods in 9-105h

money ?x excluded s9-105-h goods ?x

Exclusionary Rules

A repealed rule is not valid.

repealed ?r1 valid ?r1

Example of negation and reasoning about validity

The principal of lex posterior states that later rules have priority over earlier rules.

enacted ?r1 ?d1 enacted ?r2 ?d2 later ?d1 ?d2 prior ?r2 ?r1

Resolving rule conflicts with priority rules

Practice Session 2. Modeling Legislation

Model the following family relations in OWL, using Protege father, mother, parent, grandparent, ancestor, descendent, relative, brother, sister, sibling

Model the following rules, roughly based on German family law, in LKIF XML. 1601 BGB (Support Obligations) Relatives in direct lineage are obligated to support each other. 1589 BGB (Direct Lineage) A relative is in direct lineage if he is a descendent or ancestor. For example, parents, grandparents and

great grandparents are in direct lineage.

1741 BGB (Adoption) For the purpose of determining support obligations, an adopted child is a descendent of the adopting parents.

1590 BGB (Relatives by Marriage) There is no obligation to support the relatives of a spouse (husband or wife), such as a mother-in-law or father-in-law.

1602 BGB (Neediness) Only needy persons are entitled to support by family members. A person is needy only if unable to support himself.

1603 BGB (Capacity to Provide Support) A person is not obligated to support relatives if he does not have the capacity to support others, taking into consideration his income and assets as well as his own reasonable living expenses.

1611a BGB (Neediness Caused by Own Immoral Behavior) A needy person is not entitled to support from family members if his neediness was caused by his own immoral behavior, such as gambling, alcoholism, drug abuse or an aversion to work.

91 BSHG (Undue Hardship) A person is not entitled to support from a relative if this would cause the relative undue hardship.

Argument from Cases


Fraunhofer FOKUS, BerlinMay, 2008

Arguments from Cases

TAXMAN II [McCarty & Sridharan 1981] First to model argument from theories, using prototypes and deformations of concepts in cases.

HYPO [Ashley & Rissland, 1990] Modeled arguments from analogy with factor comparison

CABARET [Skalak & Rissland, 1991] Used cases to broaden and narrow the interpretation of rules

GREBE [Branting 1991] - Used rules to match cases and cases to satisfy open-textured concepts in rules.

CATO [Aleven & Ashley 1997] - Introduced factor hierarchies to support arguments from downplaying and emphasizing case distinctions.

Bench-Capon & Sartor [2003] used social values to construct theories of cases.

HYPO [Ashley & Rissland 1990]

Represented cases as sets of factors and dimensions. Factor: boolean property Dimension: scalar property (e.g. Degree to which a trade secret has been revealed.) Dimensions were dropped in later models based on HYPO, e.g. CATO

Modeled 3-Ply Arguments:

Move 1. Proponent. Argument 1. Cite analogous case.

Move 2. Respondent. Argument 2. Distinguish cited case.

Argument 3. Cite more on point counterexample case.

Move 3. Proponent

Argument 4. Distinguish the counterexample.Kevin Ashley and Edwina Rissland

HYPO Argumentation Schemes

Cite Analogous Case

premise. The precedent case C1 and the current case C2 have factors favoring party P in common.

premise. C1 was decided in favor of party P. conclusion. C2 should be decided in favor of party P.

Distinguish Analogous Case (Example)

premise. F, a factor favoring P in the precedent case C1, is not in the current case C2. premise. C1 was decided in favor of party P conclusion. The precedent case C1 does not apply to the current case C2

HYPO Trade Secrets Example

= plaintiff = defendant

CASE Yokana () F7 Brought-Tools ()F10 Secrets-Disclosed-Outsiders ()F16 Info-Reverse-Engineerable ()

CASE American Precision ()F7 Brought-Tools ()F16 Info-Reverse-Engineerable ()F21 Knew-Info-Confidential ()

CASE Mason (CFS, Undecided)F1 Disclosure-in-Negotiations () F6 Security-Measures ()F15 Unique-Product ()F16 Info-Reverse-Engineerable ()F21 Knew-Info-Confidential ()

Moves:1. Cite Yokana2. Distinguish Yokana, Cite American Precision3. Distinguish American Precision

Mason (?) AmericanPrecision ()

F21 ()

F6 ()

F15 ()

Yokana ()

F16 ()

CFS

F9 ()F10 ()

F7 ()

F18 ()F19 ()

F1 ()

HYPO Preference Order on Arguments On Pointedness

A precedent C1 is more on point than a precedent case C2 if and only if C1 has more factors in common with the current case than C2

Let F1 be the factors of C1 F2 be the factors of C2 and F3 be the factors of the current case.

Then C1 is more on point than C2 iff F1 F3 F2 F3.

HYPO Claim Lattice

CABARET [Skalak & Rissland, 1991]

Uses cases to reinterpret rules.

Broadening Scheme. Broadens a rule by removing an antecedent. premise. Rule R states If S1, S2 Sn then S.. premise. In case C the antecedents S2 Sn, but not S1, were held to be true. premise. S was held to be true in case C. conclusion: Rule R is (actually) If S2 Sn then S.

Narrowing Scheme. Narrows a rule by adding an antecedent. premise. Rule R states If S2 Sn then S. premise. In case C all of S2 Sn were held to be true. premise. But S was held not to be true in case C. premise. S1 is not true in case C. conclusion. Rule R is If S1, S2 Sn then S. David Skalak

GREBE [Branting 1991]

Uses rules to match cases and cases to satisfy open-textured concepts in rules. Modeled cases using semantic networks. (cf. ontologies, description logic).

The rationales for case decisions (ratio decidendi) were represented as reduction graphs. Only the arguments pro the decision of a case were modeled.

GREBE could construct arguments from these rationales, as well asparts of rationales.

Karl Branting

GREBE Argumentation Schemes

Case Elaboration. (Uses a rule to increase the number of matching cases) premise. Rule R1 states If S1 then S2.. premise. S1 is true in current case C1 (but perhaps not in S2). premise. S2 is true in the precedent case C2. conclusion. S2 is true in both C1 and C2. (partial match)

Term Reformulation. (Uses a precedent case to prove a rule condition.)

premise. Rule R1 states If S1 then S2.. (S1 uses an open-textured predicate.) premise. S1 is true in precedent case C1. premise. C1 is similar to the current case C2. (by matching semantic networks) conclusion. S1 is true in the C2 ... and thus also S2, using R1.

CATO [Aleven & Ashley, 1997]

Used factor hierarchies to model facts of cases.

Factor hierarchies enabled HYPO to be extended with models of two additional argumentation schemes:

Downplaying a distinction Emphasizing a distinction

Vincent Aleven

Example: Downplaying a Distinction

Distinguishing an Analogous Case (Reminder) premise. F, a factor favoring P in the precedent case C1, is not in the current case. premise. C1 was decided in favor of party P. conclusion. C1 does not apply to the current case.

Downplaying a Distinction

premise. F1, a factor favoring P in the precedent case C1, is not in the current case. premise. C1 was decided in favor of party P. premise. F2 is a factor in the current case. premise. F1 and F2 both have parent F3, favoring P, in the factor hierarchy. conclusion. C1 does apply to the current case.

Example: C1 involved bribery. C2 involved deception. Both involved illegal means.

Emphasizing a Distinction

Basic idea: not only does the current case have a factor not in the precedent, but this factor can be shown, using the factor hierarchy, to provide a reason for not following the precedent.

Emphasizing a Distinction

premise. C1 was decided in favor of party P. premise. F1 is a factor in the current case but not C1. premise. F1 favors the opponent of P. conclusion. The current case should be decided in favor of the opponent of P.

Arguments from Rationales

Rationale - The reasons or arguments grounding a decision, such as a decision to enact a legal rule or decide a legal case in particular way.

Representing and Reusing Explanations and Legal Precedents [Branting, 1989]

Rationales and Argument Moves [Loui & Norman, 1995]

Case-Based Reasoning in the Law A Formal Analysis of Reasoning by Case Comparison [Roth, 2003]

Rationales and Argument Moves [Loui & Norman, 1995]

Idea

Use rationales of rules and case decisions to expose implicit assumptions and open these assumptions up to challenge.

Example

Let R1 be the rule: if vehicle then not allowed in park

If the rationale of R1 is:

[if vehicle then (normally) privately used vehicle and if privately used vehicle then not allowed in park]

and we know if tank then not privately used vehicle

then conclude R1 does not apply to tanks. (undercutter)

Case-Based Reasoning in the Law A Formal Analysis of Reasoning by Case Comparison [Roth 2003]

Modeled the rationale of case decisions as argument graphs (pros and cons).

Brantings GREBE model, by comparison, modeled only the reasons pro the decision of a case in its reduction graphs.

Like GREBE, Roths system modeled argumentation schemes from case rationales, as well as parts of case rationales.

But by including both pro and con arguments in the rationales, Roth was able to identify and model several additional cased-based argumentation schemes.

Roths model generalizes CATO [Aleven 1996] by supporting arguments about whether a factor is present in the current case and whether or not some factor is a reason pro or con some other factor.Both of these are fixed in the CATO model and not subject to debate.

Argument From Theories

A theory is a set of generalizations which explains some set of cases.

A case is pair , where F is a set of propositions representing the facts of the case and D is a set of propositions representing conclusions or decisions of the case to be explained by the theory.

A theory T explains a case if for every d D it is the case that T F d.

Scheme for Argument from Theory

premise. T is theory of a set of precedent cases.premise. F are the facts of the current case.premise. T F pconclusion. p

If there are multiple, competing theories, prefer the most coherent theory.

TAXMAN II [McCarty 1981]

Modeled the arguments of the majority and dissenting opinions of a famous US Supreme Court case, Eisner v. Macomber, about whether a stock dividend is taxable income.

Since concepts are open-textured, they are not defined, but modeled by a prototype, i.e. a typical example, and deformations, i.e. changes to the prototype.

Arguments were generated using theory construction, as follows:

Given a set of precedent cases, C, construct a sequence of deformations of the prototype, through the positive examples in the precedent cases, to the facts of the current case.

Note: The theory of the precedent cases is represented by the prototype and this sequence of deformations, not by generalizations or rules.

Thorne McCarty

Goal: To find a theory represented as a sequence of deformations of the facts of a prototype case, C1, in which some goal proposition, p, was decided to hold, to the facts of the current case, C4.

The respondent will try to construct a competing theory for p, by deforming a prototype case in which p was held.

C1Prototype

C3Case 2

C2Case 1

C4Current Case

Prototypes and Deformations Model of Theory Construction

Using Social Values to Construct Theories [Bench-Capon & Sartor 2003]

Giovanni Sartor and Trevor Bench-Capon, with Carole Hafner

Structure of Theories in Bench-Capon & Sartors Model

A theory is a six-tuple , where:

Cfds is a set of case models, where each case is modeled as a set of factors (as in HYPO),

Fds is a set the factors used to model the cases.

R is a set of rules.

Rpref is a partial order on rules (modeling rule preferences).

V is a set of values.

Vpref is a partial order on sets of values (modeling value preferences).

Theory Construction Operators Examples

Include a case

Include a factor

Merge factors

Broaden a rule

Rule preferences from cases

Rule preferences from value preferences

Ordering Theories by their Coherence

Idea: When multiple theories explain some body of cases, prefer the most coherent.

Coherence factors

Consistency with precedents Explanatory power (completeness of coverage of precedents) Simplicity (cf. Occams razor) Yield acceptable results, given value preferences Lack of arbitrariness (e.g. unjustified preferences) Difficulty of application and administration. (cf. preference for bright-line rules)

Few computational models of coherence yet, but see:

Bench-Capon & Sartor: A Quantitative Approach to Theory Coherence, 2001.

Argument Schemes for Legal Case-Based Reasoning

Adam Z Wyner and Trevor JM Bench-Capon

Department of Computer Science

University of Liverpool

Overview

Case Comparison Partitioning factors with respect to a pair of cases

Argument schemes for reasoning from a precedent Schemes Assumptions Counter examples

Comparing Cases by Partitioning Factors

Partitions:

P1. common P factors

P2. common D factors

P3. P factors in CC but not in PC

P4. D factors in PC but not in CC

P5. D factors in CC but not in PC

P6. P factors in PC but not in CC

pro-plaintiff pro-defendant

P1 P2

P3 P5

P6 P4

newcase

precedent

Using Partitions to Analogize, Distinguish and Downplay

P1 and P2 factors are used to match cases and argue by analogy.

P5 and P6 factors are used to distinguish the PC from the CC, and weaken the argument by analogy.

P3 and P4 factors are used to downplay distinctions based on P5 and P6 factors

pro-plaintiff pro-defendant

P1 P2

P3 P5

P6 P4

newcase

precedent

P4 Factors

AS6

P3 Factors

AS5

AS1

Find for P

AS2

Preference for P1 over P2

AS3

PC Stronger

Exception

AS4

CC Weaker

ExceptionP1 Factors P2 Factors

P1 Factors P2 Factors Outcome

Substituting

P4 Factors

Exception

P5 Factors

Cancelling

P3 Factors

Exception

Substituting

P3 Factors

Exception

P6 Factors

Cancelling

P4 Factors

Expcetion

AS1: Claiming a Preference

P Factors Premise: P1 are reasons for P.

D Factors Premise: P2 are reasons for D.

Factors Preference Premise: P1 was preferred to P2 in PCi.

CC Weaker Exception: The priority in PCi does not decide CC.

Claim Decide CC for P.







Factors in Current case








Shown with AS2








Shown with AS2

One type of DistinctionExtra D factor in CCShown with AS4

AS2: Citing a Precedent



Outcome Premise: PCi was decided for P.

PC Stronger Exception: The preference is not established in Pci.

Claim P1 was preferred to P2 in PCi.







Factors in Past case








Past case favors P








Past case favors P

Another type of DistinctionExtra P factor in PC shown with AS3

AS3: Distinguishing P Factors

P6 Factors premise P factors in PC not in CC

Substituting Factors Exception P Factors in CC with the same parent

Canceling Factors Exception D Factors in PC with same parent

Claim The preference is not established in Pci

AS3: Distinguishing P Factors

P6 Factors premise P factors in PC not in CC

Substituting Factors Exception P Factors in CC with the same parent

Canceling Factors Exception D Factors in PC with same parent

Claim The preference is not established in Pci

Down playing

AS4: Distinguishing D Factors

P5 Factors premise D factors in CC not in PC

Substituting Factors Exception D Factors in PC with the same parent

Cancelling Factors Exception P Factors in CC with same parent

Claim The priority in PC does not decide CC

AS4: Distinguishing D Factors

P5 Factors premise D factors in CC not in PC

Substituting Factors Exception D Factors in PC with the same parent

Cancelling Factors Exception P Factors in CC with same parent

Claim The priority in PC does not decide CC

Downplaying

AS5 and AS6 Arguments from Factors Strengthening the Analogy

Factors for the P in CC but not the PC (Partition P3) and factors for the D in the PC but not the CC (Partition P4), strengthen, a fortiori, the argument from the analogy to the PC.

AS5: Unused P factors in CC but not PC

AS6: Unused D factors in PC but not CC

Assumptions

We have shown the schemes only with premises and exceptions. But there are also some assumptions that are being made.

Applicability That the precedent is applicable to the current case. This may depend on jurisdiction, level of court etc.

Equal Strength of Factors This is required to justify substitution and cancellation.

Counter Examples

A counter example is a different precedent which argues for the defendant. There are two types.1. The same P and D factors are common, but the outcome is different. Argument con the preference: attacks claim

of AS2 that P factors of CC outweigh D factors of CC.

2. Different P and D factors are in common and the outcome is different. Argument con the decision: attacks claim of AS1 that case should be decided for P.

Additional plaintiff precedents may provide counter examples to these counterexamples

Carneades Demo

Practice Session 3. Case-Based Reasoning

Given the P and D factors of a set of cases (following)

1. Construct the partitions (P1 to P6) for some selected pairs of cases.

2. Select some case from the set to be the current case, e.g. Gardner. Consider the remaining cases to be precedents.

3. Use the CBR argumentation schemes (AS1 to AS6) to construct arguments from the precedents for the plaintiff and the defendant in the current case.

Table of Case Factors

FactorId

Factor Name Side Parent

F1 Disclosure in Negotiations D Efforts to Maintain Se-crecy

F2 Bribed Employee P Questionable MeansF10 Secrets Disclosed to Outsiders D Info Known and Avail-

ableF12 Outsider Disclosures Re-

strictedP Info Known and Avail-

ableF15 Unique Product P Valuable ProductF25 Information Reverse Engi-

neeredD Questionable Means

F26 Used Deception P Questionable MeansF27 Disclosure in Public Forum D Info Known and Avail-

able

1

Table of Cases

Case Name P Factors D FactorsGardner F15 F1Hafner F2, F15 F1McCarty F15, F26 F1Verheij F15 F1, F10Prakken F12, F15 F1, F10Ashley F2, F15 F1, F25Bench-Capon F15 F1, F25Wyner F15 F1, F27

Task 1. Construct partitions by completing this table

CC/PCi P1 P2 P3 P4 P5 P6Hafner/Gardner F1 - - -Gardner/Bench-Capon F1 - F25 - -Verheij/Gardner F15 - - -Gardner/Hafner F1 - - - F2McCarty/Hafner F26 - - F2Gardner/Hafner2 F15 F1 - -Prakken/Gardner F1 - F10 -Verheij/Wyner F15 - F10 -

legal argumentation

Documents

hr argument

hr introduction

argumentation theory

hr practice session

ontologies argument

rules argument

legal problems

argument reconstruction