A survey of argumentation structures for intelligent decision support Andrew Stranieri and John Zeleznikow
School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Victoria, Australia, 3353 E-mail:
[email protected]; Phone (61) 3 53279440 ; Fax (61) 3 53279270 Database Research Laboratory, Applied Computing Research Institute, La Trobe University, Bundoora, Victoria, Australia, 3083 E-mail:
[email protected]; Phone: (61) 3 9479 1003; Fax: (61) 3 9479 3060
Abstract In practical reasoning, knowledge is often used to argue, in various ways, for or against an assertion. This perspective on knowledge has led a number of researchers to apply insights from argumentation theories to modelling reasoning. However, argumentation theories have been applied to the development of intelligent systems based in a variety of ways. For example, the most commonly used structure for representing knowledge in this way is the Toulmin Argument Structure (TAS) yet many variations to Toulmin’s original structure have been developed.
In this paper, we survey different argumentation
approaches including our own TAS variation in a family law system, Split Up, in order to conclude that there is unlikely to be one universally optimal argumentation framework but that each application requires a variation to suit the task.
1. Introduction Decisions regarding the representation of knowledge and the implementation of reasoning processes within an intelligent support system necessarily make assumptions about the nature of knowledge and reasoning.
A number of researchers in recent years have
assumed that knowledge is often used in arguing for or against an assertion and have therefore used argumentation theories to model reasoning. The use of argumentation in this way draws heavily on insights from philosophy. Over three thousand years ago, Aristotle presented two types of proofs that he called analytic and dialectic proofs.1 Dialectic proofs concern opinions that are adhered to with variable intensity. The objective of an exponent of this type of reasoning is to convince or persuade an audience to accept the claims advocated. In contrast, analytic proofs do not involve opinions and differ from dialectic proofs in that conclusions are reached by the application of sound inference rules to axioms. [Perelman and Obtrechts-Tytecta 1969] reflect that modern logic is almost exclusively concerned with analytic proofs. Dialectical proofs and rhetoric have been relegated to a subordinate, if not insignificant position. According to those authors, this has been due to 1
Aristotle in Topics. Book 1. cited from [Barnes, J. 1984]
the notion advanced by Descartes that science ought to ignore anything which is based on opinion and cannot be proven.2 [Perelman and Obtrechts-Tytecta 1969] resurrect the Aristotelian dialectics to the same status as that of analytic logic. Their treatise is entitled 'The New Rhetoric' and was originally published in French in 1958. In the same year, the philosopher Stephen Toulmin published a treatise in English that also sought to resurrect dialectics.
For Toulmin, dialectics
portrays human reasoning
processes in the vast majority of practical situations far more accurately than analytic reasoning.
Toulmin advanced a structure for arguments that was constant regardless of
the content of the argument.
His treatise focuses on demonstrating that Toulmin
argument structures (TAS) more completely capture the semantics of reasoning than analytic reasoning for most arguments. Figure 1 illustrates the structure of argument proposed by [Toulmin 1958] with an example that he uses.
DATA
MODALITY
X is a Saudi
BACKING
Saudi census data
CLAIM
probably
X is a Muslim
WARRANT
REBUTTAL
Most Saudi's are Muslim
X is an athiest
Figure 1 Toulmin argument structure
[Toulmin 1958] concluded that all arguments, regardless of the domain, have a structure which consists
of six basic invariants:
claim, data, modality, rebuttal, warrant and
backing. Every argument makes an assertion based on some data. argument stands as the claim of the argument.
The assertion of an
Knowing the data and the claim does not
necessarily convince us that the claim follows from the data. A mechanism is required to 2
[Descartes 1596-1650]The Principles of Philosophy, The Discourse on the Method Part 1.(tr) Veithch, J. Dent. London. page 165.
act as a justification for the claim. This justification is known as the warrant.
The
backing supports the warrant and in a legal argument is typically a reference to a statute or a precedent case.
The rebuttal component specifies an exception or condition that
obviates the claim. Treatises on argumentation proposed by philosophers including the structure proposed by Toulmin are useful as the basis for a knowledge representation within artificial intelligence for the following reasons: •
Argumentation reflects practical reasoning
•
Arguments capture many types of inferencing
•
Arguments are linked with explanations
•
Arguments capture plausible reasoning
•
Arguments combine to form a chain of reasoning
Argumentation can be seen to be have been used by artificial intelligence researchers in two distinct ways; to structure knowledge and to model dialectical reasoning. Dialectical approaches automate the construction of an argument and its counter argument typically with the use of a non-monotonic logic.
Most applications following this
approach represent knowledge as facts and rules, though contradictory rules are allowed. Typically, mechanisms are required to identify implausible arguments and to evaluate the better argument of two plausible ones. For example, [Farley and Freeman 1995] use a criteria of burden of proof to identify the best argument whereas [Prakken 1993] extends the notion proposed by [Poole 1988] that the best argument is one which is more specific. [Farley and Freeman 1995] and [Gordon 1995] model a dialogue between two parties as a series of arguments and counter arguments, rebuttals and agreements. Other authors that focus on the dialectical nature of argumentation include [Cohen 1985], [Fox 1986], [Vreeswijk 1993] and [Dung 1995].
The majority of researchers that use argumentation to structure knowledge and not to model dialectics, generally represent knowledge as Toulmin argument structures within their programs. This representation facilitated the organisation of complex legal knowledge for information retrieval by [Dick 1987], [Marshall 1989] and [Ball 1994]. [Clark 1991] represented the opinions of individual geologists as TAS so that his group decision support system could identify points of disagreement between experts. [Johnson et al 1993] identified different types of expertise using this structure, [Bench-Capon et al 1991] used TAS to explain logic programming conclusions, [Matthijssen 1995] represented user defined tasks with TAS, and the structure was used to engage users in a dialogue in [Bench-Capon 1998]. [Branting 1994] expands TAS warrants as a model of the legal concept ratio decidendi.
In the Split Up project we used TAS to represent
family law knowledge in a manner that facilitated rule/neural hybrid reasoning [Zeleznikow and Stranieri 1995]. Despite the immediate appeal of TAS as a convenient frame for representing knowledge, most researchers that use Toulmin structures vary the original structure. In the next section, we explore the benefits of the use of argumentation as a knowledge representation structure for the development of intelligent decision support systems. Following that, we survey different variations of the Toulmin structure including our own in order to suggest that the basic template is useful but variations are appropriate for different uses.
2. Benefits of argumentation theories to artificial intelligence Argumentation theories offer the field of artificial intelligence many benefits as outlined above. In this section we detail each benefit in turn: Argumentation reflects practical reasoning. Above, we introduced the distinction drawn by Aristotle between dialectic proofs which represent opinions that are adhered to with variable intensity and analytic proofs that represent conclusions reached by the application of sound inference rules. Philosophers Toulmin, Perelman and Obtrechts-Tytecta claim that analytic reasoning, while not in itself incorrect, does not represent the manner in which most people commonly reason. A
barrister does not prove that her client is not guilty in the same way a mathematician proves the correctness of his theorem. A barrister is obliged to create an argument. An opposing barrister may create an argument that attacks the argument or posits a new argument which makes a contrary assertion. A mathematician also creates an argument, but in his case, the argument is likely to be an instance of analytic reasoning. Axioms are advanced, and theorems proved by deduction from the axioms. First order predicate logic implements analytic reasoning. FOPL has been regarded as somewhat limited in that inferences are restricted to deduction. The logic is monotonic, uncertainty cannot be captured succinctly and propositions about propositions cannot be made. As [Prakken 1993] correctly highlights, it is an overreaction to dismiss all logic because of these limitations of first order predicate logic. We believe the limitations of FOPL are best seen, not as deficiencies of the FOPL formalism, nor as deficiencies at all. They are best seen as characteristics of analytic reasoning.
If the reasoning to be
modelled is an instance of analytic reasoning then FOPL is ideal. The failure of FOPL to represent uncertainty is understandable in that neither Aristotle nor Descartes envisaged a place for uncertainty in analytic reasoning. Assertions in analytic reasoning are made on the basis of sound inferences and true premises, not on probable opinion. This has ramifications for the monotonicity of FOPL. Assertions never need to be retracted in analytic reasoning because they are always made on the basis of sound inference rules on true premises. Deduction is the only inference method used in analytic reasoning because modus ponens, modus tolens, And Introduction and And Elimination are sound inference rules.3 Induction, abduction and other forms of drawing inferences are not sound and hence are avoided in analytic reasoning. If we accept the Perelman/Olbrechts-Tyteca and Toulmin view that practical reasoning is essentially dialectical in nature and not analytic, then any attempt to model this reasoning using analytic tools such as FOPL is an error in matching the formalism to the task. To further blame the formalism by lamenting about its incapacity for dealing with uncertainty 3
With modus ponens we infer q is true knowing that p is true and the rule p->q is true. With modus tollens we infer p is false knowing that q is false and the rule p->q is true. With And Elimination we infer that p is true and q is true knowing that p AND q is true. With And Introduction we infer p AND q is true knowing that p is true and q is true.
or for its monotonicity is to unfairly blame the tool.
Ideally,
artificial intelligence
programs should be capable of performing both analytic reasoning and dialectical reasoning in accordance with the demands of a task at hand. Arguments capture plausible reasoning Not all arguments are made with the same force. An argument that asserts that smoking causes cancer can be made with substantial force. However, an antagonist may point out that the assertion has been reasoned by induction, an inference procedure which is not sound, and cannot be made with the force of absolute certainty. The force with which an argument is made reflects the notion that arguments are plausible to greater or lesser degrees. Arguments combine to form a chain of reasoning Often an argument is made which combines a number of other arguments. Figure 2 illustrates two arguments that follow examples put forward by [Toulmin 1958]. The assertion made by the argument on the left, “X is a Saudi” is used as a data item for the argument on the right. The rebuttal component can similarly be seen as the assertion of yet another argument. Complex series of arguments can thus be represented as a chain of reasoning. Judicial reasoning to determine a property distribution outcome in family law of Australia has been modelled using thirty-five arguments that feed into each other in this way. DATA
MODALITY
X was born in Saudi Arabia of Saudi parents
BACKING
probably
WARRANT
Saudi Citizenship statutes
Persons born of Saudi parents in Saudi Arabia are automatic citizens
CLAIM
X is a Saudi
REBUTTAL
X has become a citizen of another country
DATA
X is a Saudi
BACKING
Saudi census data
MODALITY
probably
WARRANT
Most Saudi's are Muslim
CLAIM
X is a Muslim
REBUTTAL
X is an athiest
Figure 2 Two arguments: claim of one argument acts as the data for next
Arguments capture many types of inferencing
An argument represents the inference of an assertion from premises. The reasoning may be performed by deduction, induction, analogy or by connecting as the following examples from [Jenson 1981] illustrate.
An argument may assert that Socrates is mortal by
deduction: All men are mortal. Socrates is a man. Therefore, (by modus ponens), Socrates is mortal. An argument that asserts that all birds fly does so by induction after observing a large number of birds, all of which fly.
An argument that asserts that artificial turf works well
in Stadium X does so by analogy by observing that Stadium X is much the same as Stadium Y and that artificial turf works well in Stadium Y. Arguments are linked with explanations. Explanations can be generated using Toulmin argument structures. A user, presented with the assertion, 'X is a Muslim' may ask, why ? The data element from the TAS is retrieved, because 'X is a Saudi'. Still unconvinced that these factors are important may ask, so what ? The TAS warrant is reproduced; 'Most Saudis are Muslim'. Unsure as to the validity of this statement the user may request evidence. The backing is reproduced; Saudi census data. If the data element, X is a Saudi is questioned by the user, the data elements from the argument which produced this assertion are reproduced and the explanation continues with that argument. [Bench-Capon 1991] report favourable user response from explanations generated in this way through the use of their logic programs. Although there are numerous advantages inherent in the use of argumentation for the development of intelligent systems that model or support practical reasoning, argumentation theories have not been consistently used by all researchers. In the next section we explore the informal nature of the Toulmin structure and the variation to TAS other researchers have utilised. Following that illustrate our variation .
3. Variations on Toulmin structures Toulmin proposed his views on argumentation informally and never claimed to have advanced a theory of argumentation. He does not rigorously define key terms such as warrant and backing. He only loosely specifies how arguments relate to other arguments and provides no guidance as to how to evaluate the best argument or identify implausible ones. Nevertheless [Dick 1991] illustrates how relevant cases for an information retrieval query can be retrieved despite sharing no surface features if the arguments used in case judgements are represented as Toulmin structures. Both [Marshall 1989] and [Ball 1994] have built hypertext based computer implementations that draw on knowledge organised as Toulmin arguments. Hypertext links connect an argument’s assertions with the warrants, backing and data of the same argument and also link the data of one argument with the assertion of other arguments. Complex reasoning can, in this way be represented succinctly enabling convenient search and retrieval of relevant information. Although the original TAS has been useful for hypertext based systems variations of the structure lead to more sophisticated models of reasoning. [Johnson et al 1993] first discerned a typology of backings and then map each backing category to a type of expertise. [Farley and Freeman 1995] extend the Toulmin warrants rather than the backings. Johnson’s TAS [Johnson et al 1993] extend the applicability of Toulmin argument structures for the representation of knowledge by discerning five distinct categories of backings. They relate each type of backing to a distinct category of expertise which yields significant insights into knowledge representation. [Johnson et al 1993] claim that any argument’s backing can be classified into one of five distinct types of backing which they label Type 1 to Type 5. Each type of backing
corresponds to a distinct type of expertise and also corresponds to a particular philosophical paradigm of reasoning as follows: Arguments which have backings characterised as Type 1 These arguments reflect axiomatic reasoning.
Data and claim for these arguments are analytic truths.
The
supporting evidence derives from a system of axioms such as Peano’s axioms of arithmetic. Examples of what Aristotle called analytic reasoning would be captured as Type 1 arguments by Johnson et al 1993] and not as a different type of proof. Arguments which have backings characterised as Type 2 An argument which asserts a particular medical diagnosis on the basis of empirical judgements that derive from a number of patients who have presented with similar symptoms in the past exemplifies a Type 2 argument. Arguments which have backings characterised as Type 3. These arguments are characterised by backings which reflect alternate representations of a problem. A medical diagnosis based on a model of the heart as a pump analyses symptoms to be consistent with that model. Arguments which have backings characterised as Type 4. These arguments differ from Type 3 arguments in that the alternate representations are conflicting. In this case the argument involves supporting evidence that is conflicting. An assertion is made by creating a composite representation. Arguments which have backings characterised as Type 5. Type 5 backings refer to paradigms used that reflect a process of inquiry. By discerning those arguments which are based on axiomatic backing from those based on dialectical and empirical evidence, [Johnson et al 1993] have demonstrated a mechanism that developers of intelligent reasoners can use to select an appropriate inferencing method. Though, those authors do not explicitly make this point it seems that those assertions within arguments which enjoy axiomatic backing can be inferred from data with the use of sound inference rules such as modus ponens. Assertions that are contained within arguments that represent empirical backing cannot so readily be made
with sound inference rules because uncertainty is necessarily at play with empirical concepts. That X is a Muslim is inferred from the data that X is a Saudi Arabian on the empirical basis that most Saudis are Muslim. The Freeman variation on Toulmin warrants [Freeman 1994] and [Farley and Freeman 1995] recognised the need to extend the warrant component in order to develop a model of dialectical reasoning more formal than that proposed by Toulmin. Their main objective was to develop a system that could model the burden of proof concept in legal reasoning. The burden of proof governs the extent to which evidence is required in order to draw a conclusion. This varies with the severity of the misdemeanour.
For example, in a murder trial in most jurisdictions, evidence must
satisfy the beyond reasonable doubt burden of proof whereas in many civil trials the burden of proof, balance of probabilities is not as stringent. [Freeman 1994] distinguished two types of warrants called wtype1 and wtype2. The first warrant type classifies the relationship between assertion and data as explanatory or sign. Causal links are examples of explanatory warrants because they explain an assertion given data. Fire causes smoke. The consequent is explained by recourse to a cause/effect link. Other types of
explanatory
property/attribute relationships.
warrants include definitional relationships or A sign
relationship represents a correlational link
between data and assertion. The second warrant type, wtype2, represents the strength with which the assertion can be drawn from data. Examples of this type of warrant proposed by Freeman represent the strength with which the consequent can be drawn from the antecedent. Default type warrants represent default relationships such as birds fly. Evidential warrants are less certain. Sufficient warrants are certain and typically stem from definitions. In addition to the discernment of the two types of warrant, Freeman explicitly represents reasoning methods. Her model incorporates four reasoning types, modus ponens, modus tolens, abduction and contra positive abduction. Some reasoning types are stronger than others according to heuristics she devised. Modus ponens and modus tolens are assigned
a strong link qualification if used with sufficient warrants, whereas the same reasoning types are assigned a credible qualification if used with evidential warrants. Reasoning types interact with warrant types to control the generation of arguments according to reasoning heuristics. For example, modus ponens/abduction combinations are not permitted for two explanatory warrants unless both are evidential. [Freeman 1994]
demonstrates a capacity her model has for dialectical reasoning. An assertion is
initially argued for with the use of heuristics she defined. Then, an alternate argument is compared with the initial argument constructed and support for it is ascertained.
The
comparisons require the notion of level of proof which include beyond reasonable doubt, scintilla of evidence and preponderance of evidence. Freeman’s model is a sophisticated extension to the Toulmin structures which displays impressive dialectical reasoning results.
She advances types of relationships between
consequents and antecedents (wtpye1) and assigns the link a strength (wtype2). The discernment of two types of warrant is essential for her because her model of burden of proof relies on it. In contrast, [Bench-Capon 1998] is not intent on modelling burden of proof in legal reasoning but on implementing a dialogue game that engages players in constructing arguments for and against assertions initially made by one party. Bench-Capon's variation [Bench-Capon 1998] does not distinguish types of backing as [Johnson et al 1993] does or types of warrant as [Farley and Freeman 1995] do, but introduces an additional component to the TAS. The presupposition component of a TAS represents assumptions made that are necessary for the argument but are not the object of dispute so remain outside the core of the argument. In our view a presupposition for Toulmin's Saudi argument illustrated in Figure 1 would indicate that the Saudi Arabia in the context of that argument is the modern state. This is illustrated in Figure 3.
DATA
X is a Saudi
BACKING
Saudi census data
MODALITY
probably
WARRANT Most Saudi's are Muslim
CLAIM
X is a Muslim
REBUTTAL
X is an athiest
PRESUPPOSITION Saudi state in this context is the post ww 2 state
Figure 3 TAS with presupposition component Making explicit presuppositions in the argument structure are important for the use [Bench-Capon 1998] makes of TAS. A program that plays the part of one or both players in
dialogue game is often exposed to utterances in discourse that represent
presuppositions and are not central to the discussion at hand.
The presuppositions can
become critical if parties to a game do not share them. Representing the context of the reasoning is not as critical for our use of TAS in family law. We were interested in adapting TAS as an architecture for hybrid systems that could explain conclusions even when inferences were made with neural networks.
4. Our variation on Toulmin warrants Like [Farley and Freeman 1995], we discern two types of warrants. However, our two types stem from different roles we see a warrant can play in an argument. The statement “Most Saudis are Muslim” is a warrant that convinces us that the assertion “X is a Muslim” follows from the knowledge that “X is a Saudi”. However, this warrant communicates two distinct meanings. On the one hand the warrant indicates a reason for the relevance of the fact “X is a Saudi”. On the hand the warrant can be interpreted as a
rule which, when applied to the fact that “X is a Saudi” leads us to infer that “X is a Muslim”. These two apparent meanings are best perceived as different roles the warrant has in the structure of an argument. Drawing the distinction between the two roles a warrant has in an argument, leads us to explicitly identify three features that are left implicit in the Toulmin formulation: •
an inference procedure, algorithm or method used to infer an assertion from datum
•
reasons which explain why a data item is relevant for a claim
•
reasons that explain why the inference method used is appropriate
Figure 4 illustrates the variation on the Toulmin argument structure that underlies knowledge representation in Split Up. This representation enables the development of a rule/neural hybrid in family law and, we believe will facilitate the development of a dialectical model. We shall explore each aspect of the variation in depth in order to illustrate these benefits. inference procedure DATA
CLAIM
neural network: percentage split
H has contributed X relative to the wife H has Y resources relative to the wife The marriage is of Z wealth
Husband is likely to be awarded P percent of assets
WARRANT TYPE 2
WARRANT TYPE 1 Data item
Why DATA is relevant
BACKING
H has contributed X relative to the wife
H has Y resources relative to the wife
The marriage is of Z wealth
Statute makes this relevant
Statute makes this relevant
Precedent cases
Section 79(4)
Section 75(2)
Lee Steere, Brown
Network trained with appropriate examples
Network is trained with a proven learning rule:back-propag ation of errors
Why INFERENCE procedure is appropriate
Sample of over one hundred family law cases
Studies cited by Haykin
BACKING
Figure 4 Toulmin structure variation underlying Split Up
Explicating the reason for the relevance of data items
The reason that the data item “H (husband) has contributed X (much more, more, about the same, less, much less) to the marriage” is relevant in the percentage split argument within Split Up is that Section 79(4) of the Family Law Act specifically obliges a decision maker to take past contributions into account. The hair colour of the judge is considered irrelevant because domain experts can think of no reason that would make this feature relevant.
Before illustrating the pragmatic benefits that ensue if the reason for the
relevance of data items is made explicit,
we digress to examine the semantics of
relevance. [van Dijk 1989] notes that the notion of relevance has recently given rise to a class of modal logics broadly described as ‘relevance logics’. One aspect of relevance that van Dijk elucidates is the requirement that propositions within the same assertion are expected to be relevant to each other. The following incongruous examples from [van Dijk 1989] illustrate this: •
Peter has a headache and Nixon will resign.
•
If Harry comes to the party, the grass will be green
•
The film was terrible but the spring was early this year.
A superficial solution to the apparent incongruity of the examples above is to require that propositions share a common concept before they can be used in the same assertion. That is, propositions within an assertion must share a common concept in order to be considered relevant. [van Dijk 1989] points out that this is too simplistic as the assertion “If it has rained, the grass will be green” seems perfectly acceptable even though no concept is shared between the two propositions. Given the limitations of a concept sharing definition for relevance, [van Dijk 1989 p29] explores an intuitive notion based on referential meaning: “Two propositions are relevant to each other if they are about the same thing” The propositions in the sentence “The king was beheaded, so he is dead” are relevant because they both refer to the same person. However, referential identity is still
inadequate to explain the relatedness of the proposition “It is spring, so the trees get new leaves”. [van Dijk 1989] contends that while shared concepts or shared reference cannot explain relevance, arguments can be made for the grounding of relevance in the pragmatics of natural language. He says: “two facts are related if a speaker considers them to be related by uttering a sentence expressing a connection between propositions denoting those facts”. [van Dijk 1989 p30] He points out that well formedness is a concept central to syntax, truth or meaningfulness is a concept central to semantics, but the concept central to pragmatics is a appropriateness. Two propositions are relevant if a speaker considers their connection appropriate in a particular pragmatic context. A thorough exposition of the concept of appropriateness is beyond the scope of this paper.
In keeping with our pragmatic goals, to build an artificial reasoner for
discretionary domains, we have adopted a view on relevance that is perhaps primitive in theoretical terms but is sufficient for our purposes. Our view of relevance is encapsulated in the following way: A data item is relevant to an argument if a sentence expressing the reason for the relevance can be uttered and appears comprehensible. Thirty five arguments conforming to the structure indicated in Figure 4 make up the Split Up system. The claim of some arguments are the data items for others thus the thirty-five arguments model a chain of reasoning.
For example, data item,“H (husband) has
contributed X (much more, more, about the same, less, much less) to the marriage” is the claim of another argument. The chain (tree) of arguments and the reasons for relevance of each item were elicited from domain experts. The second point of departure from the original TAS our variation entails involves making explicit the inference method used to infer a claim value from data item values.
Explicating the inference method
Explicitly representing the inference method enables the use of a variety of artificial intelligence inferencing procedures. For example, the method used to infer an assertion in Split Up is a rule for some arguments and a neural network for others. Thus, the assertion in the Split Up percentage split argument in Figure 4 “The husband is likely to receive 60% of the property” was inferred with the use of a neural network. The assertion in the other Split Up arguments were inferred with the use of rules. It is conceivable that other inferencing techniques can similarly be represented and incorporated into this modified Toulmin structure. Explicitly representing the inference method used in an argument enables us to be clear about which type of inference has been used. An argument’s claims can follow from data by deduction, induction or analogy. The original Toulmin formulation does not permit that specification of the type of inference in use within a particular argument. Knowing the type of inference is important in our efforts to accept or rebut an argument. For example, claims drawn from data by deduction are more likely to be sound than assertions inferred by induction.
The inference method is critical in the automation of reasoning. Claim values are inferred from datum values by invoking the inference method associated with an argument. However, we would expect to be able to justify our decision to use a particular inferencing method. The third variation to the standard Toulmin structure enables the representation of reasons for the use of the inference method selected.
Explicating the reason for the appropriateness of an inference procedure
The representation of a reason that explains why an inference procedure is appropriate is a form of warrant that contributes to an explanation of why a claim follows from data. To make the distinction between the appropriateness of the inference procedure, the reason for relevance of a data item and the actual inference procedure clear, we introduce a
hypothetical argument that asserts that fire arms are prohibited from university grounds. This is illustrated in Figure 5. Whether a person X is prohibited from the university grounds or not can be inferred from the data items by use of the rule. However, the reason that the data item “X has a firearm” is relevant in this context is that “firearms are dangerous” according to ballistics evidence. The reason the other data item, “X is in university grounds” is relevant is that, according to the University Mission statement, the University should be a safe haven for learning. The reason the rule has_firearm(X) and in_university(X) -> prohibited(X) is appropriate is that the rule derives directly from a university ordinance. The supporting evidence is the ordinance reference. Another reason that the rule is appropriate is that it is an instance of modus ponens. The supporting evidence, or backing for the appropriateness of modus ponens is that this inference rule is demonstrably sound. Our variation to the standard Toulmin structure enables any inference procedure to be encoded within an argument structure thus facilitating the development of hybrid reasoning systems.
Furthermore, the reasons for relevance of data items and
appropriateness of inference procedure are integral to the explanation facilities. On addition to this, the warrant and backing components can be seen to be at a higher level than the other components. The notion of levels within an argument representation bestows a number of potential advantages including the development of a model of dialectical reasoning. Arguments that have different data items but similar reasons for relevance can be combined to form a new argument. This form of reasoning is the subject of continuing research and is beyond the scope of this paper.
Figure 5 Firearms argument including reasons for inference procedure
This leads us to suggest that argumentation can have a larger impact on artificial intelligence research than it currently does if dialectical models are developed which utilise argumentation to structure knowledge and to control reasoning.
5. Conclusion Argumentation provides a conceptualisation that addresses a number of concerns raised within artificial intelligence. Argumentation captures practical reasoning. It necessarily involves uncertainty.
Many different types of inferencing are encapsulated within an
argumentation based conceptualisation and explanations for inferences are intimately linked with argumentation. Recent research on argumentation in artificial intelligence can be seen to be segmented into two camps.
Broadly speaking, in one camp are researchers who use Toulmin
argument structures to succinctly represent knowledge. These approaches have typically led to the development of hypertext based systems useful as decision support systems. The other camp includes researchers that aim to develop models of dialectical reasoning. In these approaches, argumentation is used to control reasoning in an attempt to create new arguments for and against assertions.
Many of these systems are necessarily
concerned with the identification of a best argument from a number of competing or plausible arguments. Split Up does not attempt a model of dialectical reasoning and only uses the TAS to structure knowledge. Our approach differs from other work in the first mentioned camp in that we use the TAS as a structure that includes a place for an inferencing method. In doing so, we take a small step away from the use of TAS as passive knowledge representation frames toward a their use as more active structures capable of inferring an assertion. This step is in the direction of a model of dialectical reasoning but stops far short of it.
However, we believe that the Toulmin structures as initially proposed
informally must be modified if a model of dialectical reasoning based on these structures is to eventuate. [Freeman 1994] has noted that a variation on the original Toulmin formulation was necessary for the dialectical model developed by her.
[Bench-Capon 1998] added a
component to the original Toulmin structure in order to suit his dialogue game system. [Johnson et al 1993] suggest five different types of backings can be used to classify expertise. We vary the original structure by making explicit the inference procedure, reasons for relevance of data items and reasons for the appropriateness of an argument’s inference procedure. The conclusion we draw is that argumentation is a useful framework for the development of intelligent systems.
The structure proposed by Toulmin has been widely used to
structure knowledge in intelligent systems. However, many applications of the Toulmin structure have departed from the original in order to suit particular objectives.
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