A Wigmorean analysis is given for a simple, invented case. A boy, Bill, is charged with having disobeyed his mother by eating sweets without her permission. The envelopes of the sweets have been found strewn on the
floor of Bill’s room. Dad is helping in Mom’s investiga- tion, and his evidence appears to exonerate Bill, based on testimony that Dad elicited from Grandma. 1. Bill disobeyed Mom.
2. Mom had instructed Bill not to eat sweets unless he is given permission. In practice, when the children are given permission, it is Mom who is granting it.
3. Bill ate the sweets.
4. Many envelopes of sweets are strewn on the floor
of Bill’s room.
5. Mom is a nurse, and she immediately performed a blood test on Bill and found an unusually high level of sugar in his bloodstream, which suggests he ate the sweets.
6. Bill was justified in eating the sweets.
7. Bill rang up Dad, related to him his version of the situation, and claimed to him that Grandma had come on visit, and while having some sweets herself, instructed Bill to the effect that both Bill and Molly should also have some sweets, and Bill merely complied.
8. Dad’s evidence confirms that Bill had Grandma’s
permission.
9. Dad rang up Grandma, and she confirmed that
she gave Bill the permission to take and eat the sweets.
Figure 2 shows a Wigmore chart for the argumen- tational relationship among these propositions.
Circles are claims or inferred propositions. Squares
are testimony. An infinity symbol associated with a
circle signals the availability of evidence whose sen- sory perception (which may be replicated in court) is other than listening to testimony. An arrow reaches the factum probandum (which is to be demonstrated) from the factum probans (evidence or argument) in support of Figure 1. Toulmin’s structure of argument
DATA: QUALIFIER: CLAIM:
Lift Doors Open Never Lift in Motion Except for Technicians Dangerous Situation Accident Data REBUTTAL: BACKING WARRANT:
it, or possibly from a set of items in support (in which case the arrow has one target, but two or more sources). A triangle is adjacent to the argument in support for the item reached by the line from the triangle. An open
angle identifies a counterargument, instead.
comPutatIonal models of
argumentatIon
Models of argumentation are used sometimes in mul- tiagent systems (Sycara & Wooldridge, 2005). Parsons and McBurney (2003) have been concerned with ar- gumentation-based communication between agents in multiagent systems. Paglieri and Castelfranchi (2005) deal with an agent revising his beliefs through contact with the environment.
Models for generating arguments automatically have also been developed by computational linguists concerned with tutorial dialogues (Carenini & Moore, 2001), or with multiagent communication. Kibble (2004, p. 25) uses Brandom’s inferential semantics and Habermas’ theory of communicative action (oriented to social constructs rather than mentalistic notions), “in
order to develop a more fine-grained conceptualiza- tion of notions like commitment and challenge in the context of computational modeling of argumentative dialogue.”
ABDUL/ILANA simulated the generation of adver-
sary arguments on an international conflict (Flowers,
McGuire, & Birnbaum, 1982). In a disputation with adversary arguments, the players do not actually expect
to convince each other, and their persuasion goals target observers. Persuasion arguments, instead, have the aim of persuading one’s interlocutor, too. Persuasive politi- cal argument is modeled in Atkinson, Bench-Capon, and McBurney (2005). AI modelling of persuasion in court was discussed by Bench-Capon (2003).
Philosopher Ghita Holmström-Hintikka (2001) has applied to legal investigation, and in particular to expert witnesses giving testimony and being interrogated in court, the Interrogative Model for Truth-seeking that had been developed by Jaakko Hintikka for use in the philosophy of science.
Within AI & Law (AI as applied to law), models of argumentation are thriving, and the literature is vast. A good survey from which to start is Prakken and Sartor (2002), which discusses the role of logic in computa- tional models of legal argument. “Argumentation is
one of the central topics of current research in Artificial
Intelligence and Law. It has attracted the attention of both logically inclined and design-oriented research-
ers. Two common themes prevail. The first is that legal
reasoning is defeasible, [that is], an argument that is acceptable in itself can be overturned by counterargu- ments. The second is that legal reasoning is usually performed in a context of debate and disagreement. Accordingly, such notions are studied as argument moves, attack, dialogue, and burden of proof” (p. 342). “The main focus” of major projects in the “design”
strand “is defining persuasive argument moves, moves
which would be made by ‘good’ human lawyers. By contrast, much logic-based research on legal argument has focused on defeasible inference, inspired by AI research on nonmonotonic reasoning and defeasible argumentation” (p. 343).
In the vast literature on computational models of argumentation within AI & Law, see, for example, Ashley (1990) on the HYPO system, which mod- eled adversarial reasoning with legal precedents, and which was continued in the CABARET project (Rissland & Skalak, 1991), and the CATO project (Aleven & Ashley 1997). Books include Prakken (1997) and Ashley (1990). Paper collections include Dunne and Bench-Capon (2005); Reed and Norman (2003); Prakken and Sartor (1996); Grasso, Reed, and Carenini (2004); Carenini, Grasso, and Reed (2002); and Vreeswijk, Brewka, and Prakken (2003). See, for example, Prakken (2004); Loui and Norman (1995); Freeman and Farley (1996); and Zeleznikow (2002). Walton (1996a) dealt with argumentation schemes. An Figure 2. An example of aWigmore Chart
1 3 2 4 5 6 7 8 9 ∞
A
appealing formal model is embodied in Gordon and Walton’s (2006) tool Carneades. (Also see Walton, 1996b, 1998, 2002).
David Schum was the first one who combined
computing, legal evidence, and argumentation (with Wigmore Charts). Later on, Henry Prakken has done so: he did, at a time when a body of published research started to emerge, about AI techniques for dealing with legal evidence (it emerged, mainly in connection with mostly separate organizational efforts by Ephraim Nissan, Peter Tillers, and John Zeleznikow, who have
launched that unified discipline). Until Prakken’s
efforts, the only ones who applied argumentation to computer modeling of legal evidence were Schum, and Gulotta and Zappalà (2001): The latter explored two criminal cases by resorting to an extant tool for argumentation, DART, of Freeman and Farley (1996), as well as other tools. Prakken’s relevant papers include Prakken (2001); Prakken and Renooij (2001); Prakken et al. (2003); and Bex et al. (2003).
Work on argumentation by computer scientists may even have been as simple as a mark-up language for structuring and tagging natural language text according to the line of argumentation it propounds: Delannoy (1999) suggested his own argumentation mark-up was unprecedented, but he was unaware of Nissan and Shimony’s TAMBALACOQUE model (1996).
recommended aPProach
Explicit representation of arguments in a variety of contexts, within information technology tools, has much to recommend it. Consider expert systems from the 1980s, for diagnosis: quantitative weights for competing hypotheses were computed, yet argument structure was rather implicit. Making it explicit adds
flexibility. Also think of decision-support systems, and
of data visualization. Arguments, too, can be usefully visualized.
Moreover, as both logic-based and ad hoc systems for generating arguments or responding to them have become available, it is becoming increasingly feasible to incorporate argumentation modules within architec- tures. An example for this could be in the design of networks, if the communication within the network is modelled by using multiagent technology: the com- munication among agents can be usefully set in terms of argumentation.
Multimedia technology can enhance how human us- ers can grasp argumentation. The blooming of research into argumentation and the foreseeable increase in its application call for the development of new generations of tools that visualize argument structure. These can be either general-purpose, or specialized: MarshalPlan (Schum, 2001) is applied in a judicial or investigative context. Wigmore Charts deserve widespread knowl- edge among computer scientists.