The generic/actual argument model of practical reasoning
Introduction
In this paper, we present a formal description of the generic/actual argument model (GAAM) and develop from this some of its characteristics, practical advantages and disadvantages. The GAAM is intended as a model for decision support for decision making by individuals within a group or discursive community. It can be used by individuals without inference support, by individuals with varying degrees of inference support or as a fully computational system. The GAAM has been used to model reasoning in copyright law by Stranieri and Zeleznikow [46], predict judicial decisions regarding a property split following divorce by Stranieri et al. [44], support refugee status decision makers by Yearwood and Stranieri [54], facilitate interactive e-commerce by Yearwood et al. [56], implement multi-agent negotiation by Avery and Yearwood [4], and in determining eligibility for government funded legal aid by Stranieri and Zeleznikow [47]. Two shell programs that implement GAAM ideas are described in Stranieri and Zeleznikow [47] and Yearwood and Stanieri [55].
The GAAM was developed as a framework for modelling discretionary reasoning and has been used to develop practical decision support systems over the last 5 years. The objective of this paper is to provide a description of the GAAM in terms of:
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identifying its basic set of propositions and how they are combined
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identifying the elements that formally control or represent the structure of reasoning
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its inference mechanisms and how propositions are derived
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the extent to which derived propositions are valid and accepted
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the way in which it supports discretionary decision making
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setting out its capabilities as a non-dialectical model upon which a dialectical model can be built.
The remainder of this paper is organised as follows: Section 2 provides a brief review of Toulmin argument structures. Section 3 sets out how the elements of the GAAM relate to Toulmin argument structures and discusses inferences and the separation of inference from the structure of reasoning. Section 4 presents the GAAM more formally and in detail. Section 5 discusses some of the characteristics of the model, exploring deducibility and possible notions of argument strength and validity. Section 6 compares the model with other approaches.
Section snippets
Toulmin argument structures
Toulmin [48] concluded that most arguments, regardless of the domain, have a structure that consists of six basic invariants: claim, data, modality, rebuttal, warrant and backing. Every argument makes a claim based on some data. The argument in Fig. 1 is drawn from reasoning regarding refugee status according to the 1951 United Nations Convention relating to the Status of Refugees (as amended by the 1967 United Nations Protocol relating to the Status of Refugees) and relevant High Court of
The generic/actual argument model
Often reasoning occurs in the context of a small group of stakeholders involved in dialogue who would like to reach agreement on some issue. Whilst there is much anecdotal evidence for this it is also true that most organizations like to see a team approach to the solution of problems but are keen to have frameworks that permit a range of views. In general, we can distil the following characteristics of small group reasoning:
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Membership of the discursive community is usually well defined
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Members
Defining the GAAM
The GAAM is a means of specifying generic argument structures to model reasoning within a domain.
Syntax and deducibility
The propositions of a GAS for a domain modelled by the GAAM are the claims that can be formed within claim slots. So, this is the finite set of expressions of the form CpCvkCs. All logical connectives, if needed, are encoded in the inference procedures of inference slots. Propositions can only be combined if they occur together attached to inward arcs of the same inference slot. Their combination then has to be with any other claim slots that occur in the domain of this inference slot.
There is
Other approaches
Our work here has focused on the use of argumentation to structure reasoning (i.e. a non-dialectical emphasis) rather than on the use of argumentation to model discourse (i.e. a dialectical emphasis). Argumentation has usually been associated with defeasible reasoning and many have approached defeasible reasoning from the point of view of developing formal logics. For example, Nute [34] describes defeasible reasoning as:
When some new fact causes us to reject a prior conclusion, we will say that
Conclusion
In this paper, we have described the generic/actual argument model. The model derives exibility and power from: nodes whose generality is efficient in capturing many instance arguments that essentially have the same structure: a clear layout of the structure of reasoning, a clear delineation of inferences, capturing dialectical positions within a common structure. We have:
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identified its basic set of propositions and how they are combined
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identified the elements that formally control or represent
Acknowledgement
This research was supported by the Australian Research Council.
References (57)
- et al.
Argument-based explanation of logic programs
Knowledge Based Systems
(1991 (September)) On the acceptability of arguments and its fundamental role in non-monotonic reasoning, logic programming and n-person games
Artificial Intelligence
(1995)- et al.
Non-monotonic logic
Artificial Intelligence
(1980) Defeasible reasoning and decision support systems
Decision Support Systems
(1988)A logical framework for default reasoning
Artificial Intelligence
(1988)- et al.
Capturing consensus knowledge from multiple experts
A categorical approach to the semantics of argumentation
Mathematical Structures in Computer Science
(1996)- Aristotle, The Works of Aristotle, i. logic, translated by W. A. Pickard-Cambridge, chapter Topica, Oxford, 1928, p....
- et al.
An argumentation based multi-agent system for etourism dialogue
Using virgil to analyse public policy arguments: a system based on Toulmin's informal logic
Social Science Computer Review
(1994 (Spring))
Procedural Justice. Allocating to Individuals
A computational model of ratio decidendi
Artificial Intelligence and Law: An International Journal
Reasoning with Rules and Precedents—A Computational Model of Legal Analysis
Argument-based applications to knowledge engineering
Knowledge Engineering Review
An essay on discretion
Duke Law Journal
Heuristic Reasoning About Uncertainty: An Artificial Intelligence Approach
gIBIS: a hypertext tool for exploratory policy discussion
ACM Transactions on Office Information Systems
Conceptual retrieval and case law
Logische Studien zur Gesetzesanwendung
Burden of proof in legal argumentation
Knowledge, decision making and uncertainty
Arguing about beliefs and actions
Default reasoning and the logic of theory perturbation
The pleadings game: an exercise in computational dialectics
Artificial Intelligence and Law
The Zeno argumentation framework
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