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Argumentation is applied in a range of areas, during this section some of the appli- cations of argumentation in AI will be reviewed.

Reed and Grasso [25] suggest that the role of argumentation in AI may be divided roughly into two categories: modelling with argument, and modelling of argument. The latter class employs argumentation theory to model natural language arguments that take place between humans, for example a legal debate (see Section 2.6.1 for more information). The former captures the idea of using argumentation to tackle problems like non-monotonic reasoning, i.e. areas that do not naturally contain arguments.

As NMR has been discussed in Section 2.2, this section will start with an explo- ration of AI & law, before progressing to more biologically relevant domains such as medicine (in Section 2.6.2) and science (Section 2.6.3).

2.6.1

AI & law

AI & law is one of the most active domains for argumentation research. Because law involves a series of arguments and persuasion dialogues, it provides an excellent play- ground for researchers [5]. Nevertheless, not all work in AI & law uses argumentation theory [97]. However, work has focused increasingly on the natural link between the practise of law and argumentation theory.

The idea of studying jurisprudence to improve the understanding of arguments first originated from Stephen Toulmin [70]. He presented a model of argument (the Toulmin scheme) that provides a useful model for interpreting and understanding natural language arguments, be they legal or otherwise.

One of the first argumentation-based systems was HYPO [98]. It models case- based reasoning (the application of precedence) which is central to U.S. law. HYPO is notable for the application of arguments within a dialogue. HYPO evolved into CATO [99] a system used to teach precedence based reasoning to legal students.

HYPO and CATO apply an informal theory of the method used to conduct legal reasoning; yet, formal accounts of reasoning exist. To handle the defeasible aspects of legal reasoning non-monotonic approaches were investigated, with argumentation theory providing one of the most popular. Dung’s framework [31] was first applied by Prakken and Sartor [71] to investigate the possibility of arguing over the preference order assigned to arguments. Dung’s work has been applied to case-based reasoning too, e.g. Prakken [100].

Gordon [101] produced a system, The Pleadings Game, which wraps a civil law dispute in a dialectical process. Humans create arguments, the computer evaluates them, and controls the game ensuring it is fair. In this way, access to an argumentation system is controlled through the dialogue. Evolutions of this work include TDG [102] and dialaw [103]. TDG uses the Toulmin scheme to improve the readability of its arguments.

make the next move may be able to use any one of multiple arguments, so which argument should he/she/it6 choose? This work was started by CABARET [104], a system that attempted to form strategies by classifying arguments and determin- ing how/when they could be best used or attacked. The classification of arguments according to their form is continued under the banner of argumentation schemes.

The most famous scheme is that created by Toulmin and discussed above. It is used in dialogue games, e.g. TDG [102], and tools that visualise arguments, e.g. PLAID [105]. Toulmin’s scheme is generic. Others, e.g. Walton [55], try to create specific schemes for specific types of argument some of which, e.g. the argument from expert opinion [106], directly target applications in law. Furthermore, Verheij approaches his work, e.g. [94, 48, 62], through law.

AI & law was one of the first domains to investigate the visualisation of arguments with Wigmore diagrams in the 1930s [83]. They deliver a mechanism to analyse all the information in a case, record the relationships (support, attack) between the infor- mation, and assign each piece of information a degree of belief (strength). Wigmore diagrams are used in the MARSHALPLAN system [107], which is designed to help visualise preliminary fact investigation. Subsequent work evolves the diagramming techniques, and now may implement some of Walton’s schemes, e.g. AVER [96]. AVER, a tool for crime scene investigators, goes beyond diagramming and provides methods to evaluate the argument displayed. More generic tools can be applied for diagramming, e.g. Araucaria [66], or diagramming and evaluating legal arguments, e.g. Carneades [95].

In summary, the domain of AI & law has been central to the development of computational argumentation and very influential in the wider world of argumentation theory.

2.6.2

Medicine

Medicine employs argumentation theory primarily in two main ways. The first is the application of argumentation for decision support and explanation. The second is the generation of natural dialogue for use in communication with the user.

One of the earlier instances of the latter category is from Grasso et. al. [11]; the authors avail themselves of schemes to generate arguments as part of a persuasive discussion in which the system tries to improve a human user’s eating habits. An obvious descendent of this work is by Mazzotta and de Rossi [108]. It exploits Walton’s notion of schemes to generate persuasive emotional arguments to convince someone to improve their diet. Related work by Alberg [109] tries to deal with malnutrition in elderly people. Day [110] applies similar ideas to try and persuade someone to improve their health by exercising.

Bickmore and Sidner [111] combine the ideas of communication and decision sup- port to propose a system that takes advantage of a dialogue framework to improve communication between medics and patients resulting in an improved treatment plan. Schultz and Rubinelli [112] aim for a similar outcome; however, they advocate starting by trying to understand the communication mechanism between medics and patients. To this end they apply IL techniques to a set of documented medic-patient consulta- tions.

Hunt et. al. [113] show that decision-support systems in medicine can be very helpful for medics. The exceptions are systems targeted at diagnosis which universally fail. Examples of decision support include the work of the advanced computation labo- ratory at cancer research UK (now COSSAC7_{). They commenced with the creation of}

an argument-based logic for reasoning under uncertainty, the logic of argumentation (LoA) [74]. This is employed within a model of clinical guidelines, PROforma [114], which allows the guidelines to capture reasons for and against performing a partic- ular treatment. PROforma is utilised within a series of tools such as REACT [115] (decision support for medical planning by a single medic), and LISA [116] (a clinical information and decision support system for Leukaemia treatment). These systems, and others, have been evaluated and shown to be successful [117].

In addition to work using LoA, COSSAC built an implementation of a Dung- style framework through their involvement in the ASPIC project [7]. Williams and Williamson [81] use the Dung inspired framework of Prakken and Sartor [71] to gen- erate arguments that act as explanations of reasoning performed by other means.

Other research teams have brought argumentation to bear in order to provide

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explanations for clinical decision support systems. Shankar et. al. [118] operate the Toulmin model of argument to generate arguments that explain a decision made by the athena decision support system [119]. In Tolchinsky et. al. [120] a framework utilising argumentation to decide on the viability of a human organ for transplant is discussed. Many agents (software and human) create arguments, which are sent to, and evaluated by, a mediator agent. The mediator uses argumentation to make a final decision: critical questions are employed to assess the arguments sent, then an implementation of Dung’s framework evaluates the status of those arguments.

Part of the reasoning undertaken by Tolchinsky et. al. [120] is case-based, which provides an overlap with the legal domain. Dolins and Kero [121] suggest that this form of reasoning is important for medicine, and next-generation medical informatics systems. Another common link between law and medicine is the Toulmin scheme, which is exploited by both communities to represent arguments in a natural way. An example from medicine is Green [12], where it forms part of a natural language generation system designed to help create pamphlets for patients engaging in genetic counselling.

2.6.3

Science

Despite the prominence of argumentation in medical informatics, it is still relatively untried in main stream science (chemistry, biology, and physics). One noticeable exception from biology is the work of Jeffreys et. al. [9]. It employs a simple model of argumentation to evaluate the output of a bioinformatics tool. Their work shows that argumentation is as effective as other mechanisms such as decision trees. Furthermore, Jeffreys et. al. intimate that argumentation frameworks and Bayesian networks are not directly comparable because they are designed to tackle different problems.

Argumentation’s relationship to science seems to be mainly in the field of peda- gogy, where it is used to help students learn to create good scientific arguments, e.g. McNeil and Pimentel [122]. As such, the natural language aspects of the field are used, as opposed to the computational elements focused on in this thesis.

It should be remembered that argumentation theory has roots in the fields of psychology, e.g. Voss and Van Dyke [123], and cognitive science, e.g. Erduran and Jim´enez-Alexiandre [124]. Consequently these disciplines both use and contribute to

the study of argumentation theory.

2.6.4

General applications

Throughout this chapter a number of application areas in addition to medicine, law, and science have been discussed. These include: agent communication, e.g. Rahwan et. al. [8]; collaborative working, e.g. de Moor and Aakhus [34]; and pedagogy, e.g. McNeil and Pimentel [122]. Furthermore, argumentation is being applied to related areas such as e-democracy, e.g. Cartwright and Atkinson [125]; practical reasoning, e.g. Gilbert [126]; and decision support, e.g. Bury et. al. [116]. In reality, argumentation may be applied to any area in which information is uncertain, because in such situations there is room for debate. As an example of how far argumentation has spread, Trojahn et. al. [6] demonstrate that it can be used to combine different techniques for ontology matching.