Argumentation Theory for Mathematical Argument
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Abstract
To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been used to analyse mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport’s structured proofs.
Keywords
Inference Anchoring Theory Mathematical practice Mathematical argument Structured proof1 Introduction
The representation of mathematical knowledge and inference in appropriate formal logical frameworks is wellunderstood and the subject of much research. Computational tools to support this through proof checking, automatic theorem proving, and computer algebra are wellestablished, though they require formal, computationally explicit, content as input. However, the existing mathematical literature, particularly informal mathematical dialogues, and expository texts, is opaque to such systems, which cannot currently handle the variety of activities typically involved in producing such knowledge and proofs, such as, for example, exposition and argument that concerns making conjectures, forming concepts, and discussing examples and counterexamples. Our goal is to bridge this gap through devising an expressive modelling language that is closely related to the way mathematics is actually done.
Our approach to modelling such content is inspired by the generalpurpose argument modelling formalism Inference Anchoring Theory (IAT), introduced by Reed and Budzynska (2010). As its name suggests, IAT anchors logical inferences in discourse. IAT has been applied to mediation (Janier and Reed 2017), debates (Budzynska et al. 2014b), and to paradoxes in ethotic argumentation (Budzynska 2013), along with other realworld dialogues (Budzynska et al. 2013). The Inference Anchoring Theory + Content (IATC) framework we introduce is based on IAT, but with several significant modifications. Most fundamentally, IATC is designed to bring to the surface the structural features inherent in mathematical content.

Our first example is a schoollevel challenge problem that was presented in a public lecture by the mathematician Timothy Gowers (Gowers and Ganesalingam 2012). The lecture aimed to motivate and contextualise a project, then beginning, to develop mathematical software that “operate[s] in a way that closely mirrors the way human mathematicians operate” (Ganesalingam and Gowers 2017, p. 255). The reasoning needed to solve the challenge problem remains beyond the scope of the computational method that Ganesalingam and Gowers ultimately published, but it is both sufficiently simple and sufficiently realistic to introduce the practical aspects of working with IATC.

Our second example is a question posed on the online Q&A forum MathOverflow, together with the ensuing dialogue. MathOverflow is part of the Stack Exchange network of community questionandanswer websites, which is particularly popular with software developers. The MathOverflow subsite is devoted to discussions about researchlevel questions in mathematics. Such discussions are very different from the textbookstyle proofs treated by Ganesalingam and Gowers (2017), and we discuss the considerations that such discussions would impose on computational modelling efforts.

MiniPolymath 1 through 4 were part of a series of experiments in collaborative online mathematics known as “Polymath projects” (Nielsen et al. 2009–2018). While other projects in the series tackled novel research, the problems in the MiniPolymath subseries were drawn from the Mathematical Olympiad, a premier competition for precollege students. Six problems are given, and the examination takes place over two days with three problems to be solved each day. Whereas individual Olympiad participants frequently fail to solve three challenge problems in the fourandahalf hours allotted for that purpose, all four of the collaborative MiniPolymath efforts generated a solution. However, it should be noted that some of these solutions took more than 24 hours to develop. IATC can help us understand how the proof efforts progressed, and can potentially help us understand why they were (mathematically) successful.
2 Background
In this section we state what we mean by argumentation, and survey previous research on argumentation in mathematics (Sect. 2.1). We then describe Inference Anchoring Theory (Sect. 2.2) and structured proof (Sect. 2.3), two landmarks that guide our effort.
2.1 Argumentation and Mathematical Arguments
Reed and Budzynska (2010) note that in everyday language the term “argument” is used to describe a particular kind of interaction as well as the shared understanding extracted from these interactions, as “evidence” or “proof.” The purpose of IAT is to make the links between discourse and reasoning explicit.[A]rguing can be interpreted as an illocutionary act that comes about as the result of a relation between uttering a premise and uttering a conclusion, thus mirroring the logical structure of inference[.] (Reed et al. 2017, p. 146)
Concerning argumentation in a mathematical context, Pedemonte (2007, p. 39) argues that “analysis of the ‘content’ is not sufficient to analyse all the cognitive aspects in the relationship between argumentation and proof.” A large part of mathematical discussion is in essence metadiscussion about metalevel objects, such as proof strategies that are suggested on the fly and debates about whether these strategies are likely to work as intended.
 argument from authority

“My bachelor thesis supervisor said that one can’t use the word cardinal if we talk about finite sets. One has to use the words ‘number of elements’” (Tao et al. 2011, 19 July, 9:46 pm).
 argument from analogy

“Let me check that I got the example correctly: is this ‘a point inside a regular polygon’? Isn’t it established in an early comment that the example of a point inside an equilateral triangle indeed visits all the points? Can you clarify the difference here?” (Tao et al. 2011, 19 July, 9:19 pm).

Published mathematical writing tends to be particularly explicit about reasons and conclusions (Dove 2009, p. 149).

Not only the Prover but also the Skeptic “has an important role to play, namely to ensure that the proof is persuasive, perspicuous, and valid” (Dutilh Novaes 2016, p. 2618).

On the way to a proof, degrees of confidence about the conclusions to be drawn may be discussed (Inglis et al. 2007, p. 17).

Mathematical meanings need to be interpreted, and this tends to be a struggle (van Oers 2002, p. 360).
“Blog maths” (Barany 2010) and other online discussions, for example, on the questionandanswer site MathOverflow, can “tell us about mathematicians’ attitudes to working together in public” as well as the “kinds of activities that go on in developing a proof” (Martin 2015). In the process of creating a proof or mathematical theory, divergent understandings are negotiated using shared concepts, definitions, and standards for proof, even as the concepts evolve. Along these lines, Pease et al. (2017) used the methods of structured and abstract argumentation to formalise the theory of informal mathematics developed in Lakatos’s Proofs and Refutations (1976) as a set of rules for turntaking in a dialogue game. This work shows that formally specified and fully implemented argumentation tools can be brought together and applied to a specific, demanding, domain of human reasoning.^{1} Dauphin and Cramer (2018) produced a similar model of naturaldeduction style arguments, explanations, and the “prima facie laws of logic” such as may be debated in work on mathematical foundations. These prior efforts focus on developing rules that give a plausible codification of mathematical process. Our concern is different, but complementary. We are interested in a better understanding of what is actually said in mathematical arguments, and on the reasoning that is conveyed. Accordingly, we will adapt a generalpurpose argument modelling approach, Inference Anchoring Theory, which is described in the following section.
2.2 Inference Anchoring Theory
Inference Anchoring Theory (IAT) is used to model the logical relationships between the propositional contents of utterances made in dialogues (Budzynska and Reed 2011). As noted by Reed et al. (2017), the inspiration for developing IAT lies in earlier work on representing dialogue in the Argumentation Interchange Format.
In Figs. 1 and 2, below, “TA” stands for a default transition, “RA” stands for application of rule of inference, and “CA” stands for default conflict. That is to say, there is no explicit formal dialogue protocol attached to these two examples.(Budzynska et al. 2014a), emphasis added
 (i)
relations between locutions in a dialogue, called transitions;
 (ii)
relations between sentences (propositional contents of locutions); and
 (iii)
illocutionary connections that link locutions with their contents.
...directly from pragmadialectical analysis which views the speech act of assertion [...] as occurring at the ‘sentence’ level, and the speech act of argumentation as occurring at a ‘higher textual level.’ (Budzynska and Reed 2011)
In short, IAT studies “the way in which the rules of dialogue influence the construction of argument” (Budzynska et al. 2016).the connection between locutions in a dialogue has an inferential component beyond any that may hold between the contents of those locutions (Reed and Budzynska 2010).
If the conversation were to continue, Wilma would typically have the burden of justifying her rejection of ‘A’, which might be done with counterarguments that would dig into the details of ‘A’ looking for flaws (ibid., p. 67); in addition, she might begin to make a case for an alternative position, ‘B’. These considerations point to the direction we will be taking with IATC.[T]here is an asymmetry between the production of arguments, which involves an intrinsic bias in favor of the opinions or decisions of the arguer whether they are sound or not, and the evaluation of arguments, which aims at distinguishing good arguments from bad ones. (Mercier and Sperber 2011, p. 72)
We will describe the implications of this addition in detail in Sect. 3, along with some other adaptations to IAT that we have found useful in mathematical settings. One of the implications is that in the current work we do not need to emphasise transitions—of either the explicit or implicit variety—since a more explicit treatment of content gives us another way to manage context relationships.
 (iv)
a model of nonpropositional content, namely of the mathematical objects under discussion, and the relations between them.
2.3 Lamport’s Structured Proofs
Structured proofs, as described by Lamport (1995, 2012), inhabit the middle ground between formal and informal mathematics, and provide a useful point of reference for our work on IATC. Structured proofs offer a notational strategy that is a “refinement of [...] natural deduction” (Lamport 1995). While the proofs represented using this system are not required to be strictly formal, the language of structured proofs has evolved together with Lamport’s work on a formal language and corresponding proof checking system, the “Temporal Logic of Actions+” (TLA\(^{+}\)), which is used to model concurrent systems Lamport (1999, 2014).^{3} Structured proofs are, specifically, structured as a strict hierarchy of lemmas. An example appears later on in this paper, in Fig. 6, which we will use to illustrate the similarities and differences with IATC.
Unlike structured proofs, IATC is intended to express the typical processes by which proofs are generated in standard practice, rather than make the process of proving and reading proofs easier. It would nevertheless be compatible with our aims to include formal statements in TLA\(^{+}\) (or some other language) in IATC’s content layer.The proofs [...] are lengthy, and are presented in a style which I find very tedious. [...] My feeling is that informal proof sketches [...] to explain the crucial ideas in each result would be more appropriate.
3 Inference Anchoring Theory + Content
IATC has many things in common with IAT, but should not be seen as a strict addition to the earlier theory. Adding explicit models of content and discussions about content prompts several adaptations. In this section we describe these adaptations, and introduce the IATC modelling language.
Several important requirements arise from the features of the mathematics domain. As we saw above, IAT is concerned with anchoring propositions to utterances and with mapping the logical relationships that obtain between them. However, various mathematical objects—Larvor (2012) mentions “diagrams, notational expressions, physical models, mental models and computer models”—are more comfortably thought of as nonpropositional in nature. Discussions about proofs have been theorised formally using the notion of proof plans, which are constructed and transformed using explicit heuristics and tactics (Bundy 1988). However, Fiedler and Horacek (2007, pp. 63–64) have suggested that existing work with proof plans cannot be straightforwardly adapted from machineoriented to humanoriented contexts, because proof plans are, from a potential human reader’s perspective, overly detailed, with insufficient structural abstraction. By contrast, a language like IATC is charged with expressing “strategic arguments that are meaningful to humans” (Fiedler and Horacek 2007, p. 68). Nevertheless, as important as strategic reasoning is, lowlevel mathematical content seems to be even more fundamental.
Karttunen’s concept of “discourse referents,” illustrated in the quote above, underlies Discourse Representation Theory (Kamp and Reyle 1993) and its extensions. While the developers of IAT acknowledge the generality of Structured Discourse Representation Theory (SDRT), in particular, they criticise it for making “assumptions of contextindependent semantics” (Budzynska et al. 2016). Nevertheless, DRT has been successfully applied to model some aspects of mathematical discourse, and we will discuss that work further in Sect. 5, and contrast it with our orientation here....informal notational practise [sic] of mathematicians, who will write an existentially quantified formula (say, \((\exists e)(\forall x)(xe = ex = x)\), as one of a set of postulates for group theory) and thenceforth use the variable bound by the existential quantifier as if it were a constant as when they will write the next postulate (\(\forall x)(\exists x^{1})(xx^{1} = x^{1}x = e)\). [punctuation modified]
For now, we emphasise that IATC differs from IAT in its approach to context. Specifically, IATC sets the notion of dialogical relations to one side, and instead connects locutions to each other directly in the content and intermediate (metadiscussion) layers.
Here, rather than connecting ‘No’ to ‘A’ with a transition, we connect it directly to the previously modelled content, A, via a ‘Challenge’ illocution. From there, we continue to use the content and intermediate layers to explicitly model interconnections. For example, ‘B’ does not simply conflict with A, but rather presents a warrant for “not A”, modelled here using the twoparameter ‘implies’ relation.
With these changes in place, dialogue relations could in principle be reintroduced. For example, ‘Because B’ could be seen to ‘substantiate’ the previous utterance, ‘No’, as a communicated reason for rejecting A. Nevertheless, in the current work we continue to leave these links out, on the basis that we do not yet have a detailed theory of the norms of mathematical dialogue. The Lakatosian model developed by Pease et al. (2017), for example, only covers a limited subset of the rules and norms involved, specifically, those dealing with conjectures, lemmas, and the production and evaluation of counterexamples. By interconnecting contents in the content layer and through intermediate relations, we are able to make an explicit model of the logical structure of mathematical arguments. Such models could potentially inform a subsequent analysis of the associated dialogue structures.
For example, the longrange reform connection from A to \(A^\prime \) in our content analysis would suggest a corresponding longrange transition from Bob’s first to his last statement in the dialogue. However, that would still neglect Bob’s sofar implicit reasoning to the effect that \(A^\prime \) is (potentially) not vulnerable to objection B. If the dialogue continued from this point, detailed relationships between the constituent contents of ‘\(A^\prime \)’ and ‘B’ may need to be discussed, and an IATC analysis would be able to unpack these and account for the details.
Inference Anchoring Theory + Content, part 1: Performatives
Performatives (perf[...])  

Assert (s [, a ])  Assert belief that statement s is true, optionally because of a 
Agree (s [, a ])  Agree with a previous statement s, optionally because of a 
Challenge (s [, a ])  Assert belief that statement s is false, optionally because of a 
Retract (s [, a ])  Retract a previous statement s, optionally because of a 
Define (o, p)  Define object o via property p 
Suggest (s)  Suggest a strategy s 
Judge (s, v)  Apply a heuristic value judgement v to some statement s 
Query (s)  Ask for the truth value of statement s 
QueryE ({\(p_i\)(X)})  Ask for the class of objects X for which all the properties \(\{p_i\}\) hold 
Our method for producing this set of tags was as follows. Two of us (with first degrees respectively in Mathematics and Information Systems, both with more than 10 years experience studying argumentation and social machines) performed close content analysis (Klaus 2004) together on the first 100 comments in MiniPolymath 1. Our analyses resulted in an initial tag set, including both typical illocutionary performatives and mathematics specific performatives, like Define and QueryE, as needed (see “Appendix” for examples). Several of the typical illocutionary connections (Assert, Question, Challenge, Agree) could be carried over from the schemes commonly applied in IAT. Our initial tag set was discussed and iteratively developed over the same 100 comments by all coauthors, with any recurring differences discussed, allowing us to align our results. A third coauthor (with a first degree and Ph.D. in Mathematics) then further developed and refined the tag set by performing close content analysis on the entire MiniPolymath 3 conversation and on sections of MiniPolymath 1. Again, this was conducted alongside discussion with the other coauthors throughout the process. A fourth coauthor (with a first degree in Mathematics) later extended the tag set with additional informal logical relationships, such as analogy, and specific contentfocused relationships, such as sums, which played a role in the further examples we treated in Sect. 4. These extensions were again reviewed by all coauthors.
Inference Anchoring Theory + Content, part 2: inferential structure, heuristics and value judgments, reasoning tactics, and contentfocused relations
Inferential structure (rel[...])  
implies (s, t)  Statement s implies statement t 
equivalent (s, t)  Statement s implies statement t and vice versa 
not (s)  Negation of s 
conjunction (s, t, ...)  Conjunction of statements s, t, ... 
has_property (o, p)  Object o has property p 
instance_of (o, m)  Object o is an instance of the broader class m 
indep_of (o, d)  Object o does not depend on the choice of object d 
case_split (s, {\(s_i\)})  Statement s is equivalent to the conjunction of the \(s_i\)’s 
wlog (s, t)  Statement t is equivalent to statement s but easier to prove 
Heuristic value judgments (value[...])  
easy (s [, t])  Statement s is easy to prove; optionally, easier than statement t 
plausible (s)  Statement s is plausible 
beautiful (s)  Statement s is beautiful (or mathematically elegant) 
useful (s)  Statement s can be used in an eventual proof 
Reasoning tactics (meta[...])  
goal (s)  Used with Suggest to guide other agents to work to prove the statement s 
strategy (m, s)  Indicate that method m might be used to prove s 
auxiliary (s, a)  Statement s requires an auxiliary lemma a 
analogy (s, t)  Statement s and statement t should be seen as analogous in some way 
implements (s, m)  Statement s implements the method m from a previously suggested strategy 
generalise (m, n)  Method m generalises method n 
Contentfocused structural relations (struct[...])  
used_in (o, s)  Object o is used in statement (or object) s 
reform (s, t)  Statement s can be reformed into statement t 
instantiates (s, t)  Statement s schematically instantiates statement t 
expands (x, y)  Expression x expands to expression y 
sums (x, y)  Expression x sums to expression y 
cont_summand (x, y)  Expression x contains y as a summand 
Our performatives have slots, which are filled by statements or objects. Statements may be represented in various ways: in unparsed natural language, as symbolic tokens that serve as shorthand for such statements, or in some representation language. The other relations are clustered into segments treating Inferential Structure, Heuristics and Value Judgments, Reasoning Tactics, and ContentFocused Structural Relations. The associated grammatical categories are given the following abbreviations in our linear notation: ‘rel’, ‘value’, ‘meta’, and struct’. For example, the expression ‘perf[Assert](rel[has_property](o, p))’ denotes the assertion of the statement “object o has property p.” IATC allows direct, explicit, statements about objects, propositions, and statements. For example, ‘perf[Assert](used_in (o, s))’ denotes the assertion of the statement “object o appears in statement s.”

The speaker Asserts that the problem has an equivalent reformulation. “The following reformulation of the problem may be useful: Show that for any permutation s in \(S_n\), the sum\(a_s(1)+a_s(2)\ldots +a_s(j)\) is not in M for any \(j\le n\).”

The speaker Judges the reformulation to be (potentially) useful. “The following reformulation of the problem may be useful: [...]”

The speaker Suggests that the reformulation describes a goal that could be worth pursuing: “[...] Show that [...]”
The relations given in Tables 1 and 2 have been sufficient to describe the reasoning in a range of examples, however we do not claim that this list of relationships would treat all mathematical texts. Nor do these relationships describe mathematical texts at the level of formality found in proof checking systems, or the level of detail found in some other theorisations of discourse. Thus, in the future IATC should not be limited to the set of tags presented here. For example, we have found uses for the value judgments ‘easy’, ‘beautiful’, and ‘useful’, but it is quite plausible that future work would find use for values such as ‘efficient’, ‘generative’, or something else. Similarly, useful additions may be found in the other grammatical categories. The evidence from our examples in Sect. 4 is that these major grammatical categories—performatives, inferential relations, metalevel reasoning, value judgments, and content relations—are themselves stable.
We have described, and illustrated with simple examples, the way content and strategic relationships can be used to mediate contextual relationships, but context is also representable in IATC in another more explicit way. Although IATC does not require proofs to be structured in a treelike hierarchy, nested structure is introduced as follows. In general, language elements in Table 2 that have a statement slot can also have that slot filled by a (possibly disconnected) subgraph. In this way, structure corresponding to a “lemma” can be indicated. A lemma, in this sense, is understood to be the reasoning that ‘implements’ a ‘strategy’, or, alternatively, a specific section of reasoning that ‘implies’ some conclusion. This representation strategy is similar to the “partitioned networks” introduced by Hendrix (1975, 1979). An example will appear in Sect. 4.1.
To summarise, IATC resembles IAT in many ways, but with changes that are required when content, and discussions about content, are explicitly modelled. These features are necessary to express details of mathematical reasoning. For example, one proposition that can be extracted from the statement in Fig. 4 has the schematic form “The reformulation P is equivalent to the original question Q.” IAT would have no way to extract P and Q from the assertion, but IATC can do so: they are represented as ‘problem’ and ‘perm_view’ in the figure. Later moves can then connect to these pieces of content, and we already see such structure forming in our analysis of the above short excerpt.
IATC retains and extends IAT’s approach to modelling contents and inferences, by adding nonpropositional contents and more complex logical and heuristic relations. Illocutionary connections are also retained, with some mathematicsspecific additions. However, IATC sets aside the notion of transitions, not because we view dialogue norms as unimportant, but because they are difficult to model at this stage. In IAT, relations between propositional contents roughly mirror the norms involved. The corresponding notion for IATC would be heuristics that account for the production of new expressions, and which take preceding expressions and background knowledge into account. We will have more to say about such heuristics in Sect. 4, nevertheless, many considerations must be deferred to future work.
4 Examples

how IATC expresses the reasoning structures that arise in proof construction,

how it might be used to support computational models of mathematical reasoning,

and how it helps to uncover the salient elements of mathematical discourse.

Section 4.1: A carefully spelled out informal solution to a tricky but nontechnical mathematical problem serves to illustrate the thought processes involved in successful mathematical problem solving. The example shows how IATC captures this sort of thinking.

Section 4.2: A discussion of the relationships between, and merits of, different mathematical questions exhibits a level of abstraction above that needed in an individual proof. We explore the ramifications for explicit representations of the reasoning involved.

Section 4.3: A multiparticipant dialogue that develops a challenging but not highly technical proof casts light on processes of mathematical collaboration and mathematical reasoning. An analysis of this material using IATC allows us to explore the process of proofconstruction in detail.
4.1 Making the Reasoning Explicit in the Solution to a Challenge Problem
an alternative path of enquiry seeks to describe the heuristic process of proving theorems in more cognitively plausible terms. In particular, one relevant question to ask is how (human) mathematicians avoid large searches (Gowers 2017). IATC can contribute to the further development of this effort, by giving a uniform but expressive way to outline the process of developing proofs. Researchers working on mathematical software meant to exhibit humanstyle reasoning may find this expressiveness useful.reduce complex inferences, which are beyond the capacity of the human mind to grasp as single steps, to chains of simpler inferences, each of which is within the capacity of the human mind to grasp as a single transaction,
Our chosen example is a “magic leap” problem presented in a public lecture by Timothy Gowers, describing joint work with Mohan Ganesalingam (2012). The reasoning was communicated by a combination of speech and marks on a chalkboard, and is reproduced in Fig. 5. This example has been modelled in IATC by Corneli et al. (2017b). The problem initially appears difficult to solve without a computer algebra system, but a simple algebraic solution is available once the correct strategy is found. As such, an important part of the reasoning involved in solving the problem is to find the correct strategy. The steps involved in this part of the reasoning process are heuristic rather than deductive. We redescribe the analysis here.
For comparison with the IATC analysis, Fig. 6 reproduces the proof in Lamport’s style. Figures 7, 8, 9 and 10 present portions of the IATC tagging of the solution that was presented in Gowers’s lecture. Figure 7 illustrates an initial exploration of the question, and Fig. 8 establishes a ‘strategy’ based on that exploration (“The trick might be: it is close to something we can compute”). Figure 9 opens the door to applying the strategy. The central part of the proof that ‘implements’ the strategy is highlighted in Fig. 10.
The introduction to the proof, expanded in Fig. 7—and condensed into a "Proof sketch” in Fig. 6—contains interesting examples of heuristic reasoning. This part of the solution centres on the probing question “Can we do this for \(\mathfrak {X}\)?”, where \(\mathfrak {X}\) ranges over several examples: \(x+y\), e, and small rationals, and where ‘this’ denotes “find the 500th digit of \(\mathfrak {X}^{2012}\).” In the IATC representation, each tentative proposal to “do this...” stands in analogy with the original problem statement. Although Fig. 7 contains only Assert performatives, a more complete representation would also include Query performatives, since the analogies are not only proposed: their validity is also queried, much as we saw in the example treated in the previous section.
Indeed, nowhere in the explicitly communicated reasoning is the key strategy fully and explicitly stated. The basic strategy of the proof is that the quantity of interest may be sufficiently close to something we can compute. In the IATC representation (Fig. 8), this is understood to be Suggested by the following statements from the proof sketch, “And how about small perturbations of these? Maybe it is close to a rational?” Step 1 of the structured proof shows that rationals do, in fact, match the strategy’s preconditions. The IATC representation is less explicit on this point, since it sticks more closely to the reasoning expressed in the lecture. This example shows that even relatively explicit statements may need further interpretation to be represented meaningfully in IATC. Specifically, the way the proof progresses only makes sense if we recognise the ‘strategy’ implied by what might otherwise appear to be a throwaway comment early on.
Step 2 in the structured proof concerns another analogy. This time, a special one which, the IATC analysis notes, symbolically generalises the initial question (Fig. 8). That is, rather than considering \((\sqrt{2}+\sqrt{3})^{2012}\) we now consider \((\sqrt{2}+\sqrt{3})^{m}\). (NB. an edge connecting the ‘generalise’ node to the problem statement has been omitted.) However, the concept of generalisation remains implicit in the corresponding portion of the structured proof. Indeed, Step 2 is not a good match for the requirements of structured proof at all, since it is not a real lemma, and its “proof” fails (indicated by “*”). Including failed proof steps is not a problem for IATC. In Fig. 9 the process of solving the problem proceeds apace, without pausing to remark on a failed lemma, now that something more interesting has been discovered.
Meanwhile, Step 3 in the structured proof implements the main strategy for resolving a special case of our generalised problem, namely showing that \((\sqrt{2}+\sqrt{3})^{2}\) is close to an integer, establishing a pattern that leads to the conclusion. Again, Step 3.3 offers considerably more detail than was present in the original lecture.
Step 4 subsequently generalises the method that was used in Step 3, and applies it to the expression we were originally interested in. Figure 10 diagrams out the reasoning that underlies this step. The longrange dashed edge in this figure connects with the node “The trick might be: it is close to something we can compute” pictured in Fig. 8. The collection of nodes highlighted in red implement that strategy. Notice, though, that the computation is not done explicitly: it’s unimportant which integer the number of interest is close to. Collectively, the fact that \((\sqrt{2}+\sqrt{3})^{2012}+(\sqrt{3}\sqrt{2})^{2012}\) sums to “some integer” and the fact that \((\sqrt{3}\sqrt{2})^{2012}\) is sufficiently small implies the result. Step 5 shows the details of the final computational check.
4.2 Towards Computable Models of Mathematical Reasoning Via IATC: A Q&A Example
IATC allows the argumentation aspects of mathematical dialogues to be represented as explicit graphical structures, which gives a plausible basis from which to develop an explicit computational model of the reasoning steps that are implied in mathematical argumentation. Corneli et al. (2017a) showed how IATC could be used to create graphical models of the discussion that develops around a question posted on MathOverflow. Here we will remark further on implications for computational modelling. The question, which was given the title “Group cannot be the union of conjugates” (Chandrasekhar et al. 2010), is as follows:The presentation is often speculative and informal, a style which would have no place in a research paper, reinforced by conversational devices that are accepting of error and invite challenge. (Martin and Pease 2013)
“I have seen this problem, that if G is a finite group and H is a proper subgroup of G with finite index then \( G \ne \bigcup \nolimits _{g \in G} gHg^{1}\). Does this remain true for the infinite case also?”
 (P1)

“If G is a finite group, H is a subgroup of G and the index \([G \mathop {:} H]\) is finite, then G is not equal to the union of \(gHg^{1}\)”; and,
 (P2)

“If G is an infinite group, H is a subgroup of G and the index \([G \mathop {:} H]\) is finite, then Gis not equal to the union of \(gHg^{1}\).”

(P1) is true

(P2) is similar to (P1)

Therefore, (P2) is (potentially) true as well
 (P2\(^\prime \))

“If G is an infinite group, H is a proper finite index subset of G and the index \([G \mathop {:} H]\) is infinite, then G is not equal to the union of\(gHg^{1}\).”
The dialogue is an interesting example of mathematical reasoning in which proof certainly plays a role, but is nevertheless of secondary interest compared with asking interesting questions, and thinking about how different questions relate to each other. What would be necessary to represent this sort of dialogue computationally? Expressing propositions like (P1) in IATC is straightforward, though, as we noted, the content layer is not directly modelled in this representation language. The following expression represents this proposition in IATC, introducing additional invented pseudocode representations (in italics) in the content layer.
Processing such expressions to build a model of a dialogue will require adding numerous stanzas like this one, each rooted on an IATC performative, into one graph database that records the relationships between the statements and their constituent parts. Individual expressions like the implies relationship would need to be addressable, in order for an analogy between two implications to be proposed. Definitions for predicates like finite_group and special constructions like union_over could be supplied in an accompanying knowledge base. In further rounds of computational processing, the analogies between (P1) and (P2), and between (P1) and (P2\(^\prime \)), could be checked using graphprocessing methods described by Sowa and Majumdar (2003). New heuristics would be needed if the aim was to demonstrate the truth or falsity of the various propositions, not just to recreate the surface analogies. Moreover, as we’ve seen, mathematical dialogues are not just concerned with verifying statements, but may also consider the qualities that make a particular question interesting in a given context. Heuristics that can be used to select interesting problems are not prevalent in current mathematical software.
As a limited proof of concept showing the plausibility of adding a computational deduction and verification layer on top of IATC representations, Corneli et al. (2017b) give a detailed expansion of one step of a mathematical proof using simple rules for transforming the underlying graph structures. It is worth emphasising that the representations of reasoning afforded by language elements in Tables 1 and 2do not themselves encode the metalevel reasoning associated with such graph transformations.
4.3 MiniPolymath Revisited
The data that underlie this section were generated in a series of online experiments in collaborative problem solving convened by mathematician Terence Tao (2009; 2011). We use IATC to expand on a previous analysis of this data presented by Pease and Martin (2012), showing how IATC can advance the theory of mathematical argument through the detailed analysis of real world examples, as per Carrascal (2015).
In their 2012 paper, Pease and Martin analysed the third MiniPolymath project in broad strokes, with each blog comment comprising a single unit to be tagged. They developed a typology of five intuitive comment types, based on the mathematical content of each comment: examples, conjectures, concepts, proofs, and other.
In order to assign comments to these categories, both authors performed close content analysis on all comments posted between the time Tao posted the problem to his blog (8pm, UTC on July 19th, 2011) and the time he announced that a solution had appeared (9.50pm, UTC on July 19th, 2011). The discussion comprised 147 comments over 27 threads. Ten comments were assigned to more than one category.
Our present IATC analysis of the same data is designed to give a more complete picture of the linguistic, dialectical, and inferential structure of the comments that fall within the five intuitive categories mentioned. There are three main differences between the two analyses. First, in comparison with the earlier broadstroke analysis, the IATC analysis is richly detailed, with a unit defined as any quantum of commentary with taggable content. Secondly, our focus in the earlier analysis was purely on mathematical content, and on the type of mathematical content in particular. This contrasts with our present analysis, in which we provide a more finegrained representation of mathematical content in the taggable units, and furthermore take into account linguistic, dialectical, and inferential structure. Third, the IATC analysis takes into consideration the entire MiniPolymath 3 conversation, including the comments that came after Tao had announced that a proof had been found.
The new analysis, accordingly, adds depth to our earlier analysis. Crucially, the new perspective will be more relevant to argumentation theorists, and supports a detailed understanding of what went on in the process of constructing the collaborative proof. The earlier typology provided an initial way to sort the content, whereas the IATC tag set developed along with our analysis via the iterative, discursive method discribed in Sect. 3. Though they cover the same data and show some correlations, as described below, the latter categorisation was not derived from the earlier one.
Figure 11 presents an excerpt from the MiniPolymath 1 dialogue (MPM1) as it originally appeared on Tao’s blog. Figure 12 and Table 3 give the IATC analysis of this excerpt in diagrammatic and textual form. The first portion of Fig. 12 repeats the contents of Fig. 4. The longer excerpt shown here illustrates complex contextual interconnections forming in the content layer.
Our main example in this section is MiniPolymath 3 (MPM3), which we tagged into IATC in its entirety. (This work was carried out by one coauthor with a first degree and PhD in Mathematics, in consultation with others as described in Sect. 3.) As an indicative sample, the first three comments and their tags are shown in Fig. 13. Figure 14 shows how tags from IATC’s five grammatical categories were distributed over time. Thus, for example, we see ‘value’ tags used early in the discussion as strategies are being considered, and again later in the discussion when solutions are being vetted. Figure 15 gives another view of the timeline, showing how the comments were categorised into the 5part typology from Pease and Martin. In the initial categorisation developed for that paper, comments were allowed to be in multiple categories at once. Here, to facilitate a clean mapping to IATC, we redid the categorisation with the requirement that each comment should fit into exactly one main category. We arrived at a nearly equal division of comments among the five categories: example (20.3%) conjecture (21.2%), concept (19.5%), proof (19.5%), and other (19.5%). (This replication work was carried out independently by one of the coauthors with a first degree in Mathematics.)
IATC analysis of MPM1 excerpt (text form). (Color table online)
One might suspect that Suggest should be used only within conjectures, but in the current categorisation it is used somewhat more frequently along with concepts. This is partly explained by the fact that Suggest can be used to introduce either a goal or a strategy. Sometimes goals represent conceptual tidying, as in “I guess there is an odd/even number of point distinction to do” (Tao et al. 2011, July 19, 9:31 pm).
Furthermore, despite our selfimposed constraint to map each comment only to the most salient of the five categories, in practice a comment may simultaneously introduce a concept along with a conjecture that applies that concept. For example the straightforward concept of “restriction[s] on how the next pivot is chosen” appears along with the more speculative conjecture “Can we start with a complete graph and all cycles on that graph and just discard the ones that don’t follow the restrictions to converge on the ones that do?” (Tao et al. 2011, July 19, 8:56 pm). The need to introduce concepts also applies in the case of more outlandish conjectures, such as “It might be fun to use projective duality” (Tao et al. 2011, July 19, 8:23 pm). However, a concept may suggest a vague method without raising a conjecture as such, e.g., “I’m thinking spirograph rather than convex hull” (Tao et al. 2011, July 19, 8:44 pm).
5 Conclusion

IATC offers a more faithful representation of everyday mathematical practice than does, e.g., Lamportstyle structured proof.

IATC has the potential to support computational reasoning about mathematics by bringing structural relationships between pieces of mathematical content to the surface.

IATC can recover salient elements of discourse within comments, as well as the way these contents connect across comments.

IATC does not yet handle everything that is said in mathematical dialogues. We saw above that IATC nevertheless helps disambiguate the “other” category bracketed by Pease and Martin (2012).

There are places where IATC representations remain bulky, pushing much of the actual reasoning into whatever representation system handles the content layer.

One related limitation is that implications and assumptions that mathematicians consider “obvious” are typically elided from their discourse, often for valid expository reasons, and that, therefore, unpacking the contextual relationships between statements typically requires a mathematically trained annotator.

We introduced a graphical way to segment dialogues, but IATC does not currently have the ability to express context shifts – although it can compare contexts with ‘analogy’.
In Sect. 3, we mentioned that Discourse Representation Theory (DRT) has informed several earlier efforts to model mathematical discourse. We are aware of three PhD theses—by Clauss Zinn (2004), Mohan Ganesalingam (2013), and Marcos Cramer (2013)—which have made use of somewhat similar mathematicsspecific interpretations of DRT. Zinn and Cramer focused on proof checking, while Ganesalingam looked at mathematical communication from a linguist’s perspective. However, he opted to focus exclusively on mathematics in the “formal mode,” leaving informal communication about matters such as “interestingness” to one side, because they bring with them a host of additional complications (Ganesalingam 2013, pp. 7–8). From a linguistic point of view, DRT is useful in a mathematical setting, in the first instance, because of its core ability to express “legitimate antecedents for anaphor” (Ganesalingam 2013, p. 50). In Ganesalingam’s work, this basic feature is extended to allow sidelong references to definite descriptions (such as ‘the set of natural numbers’) by “introducing generalised anaphors which can have presuppositional material attached to them” (Ganesalingam 2013, pp. 25, 237). Specifically, this allows one to infer from statements such as “x is prime” that x is in fact a member of the set of natural numbers (p. 25).
The associated requirement of combining semantics and pragmatics (van der Sandt 1992, p. 336) is reminiscent of our treatment of unspoken assertions and unstated features of content in our IATCbased analyses. To continue the comparison, Ganesalingam’s adaptations of DRT overcame limitations, having to do with quantifier scoping, that constrained earlier typetheoretic analyses (Ganesalingam 2013, pp. 81–82). This is broadly similar to our use of nested structure in Sect. 4.1. Indeed, Sowa (2000) shows that several different approaches to nested structure (including DRT) are all mutually equivalent from a logical point of view. As indicated by van der Sandt (1992), pragmatics is relevant for DRTbased models because it can inform the contextspecific resolution of Discourse Representation Schemes. This is related to the question we highlighted in Sect. 4.2: how to model the transitions between discourse moves in mathematics? IAT accounts for similar issues by making reference to dialogue norms, but we have seen that for mathematical dialogues, detailed content and contextspecific issues need to be taken into consideration at each stage. The models of content evolution used by Ganesalingam and Gowers (2017) to keep track of proof generation were structurally similar to the DRTbased models developed by Ganesalingam (2013): in this case, the evolution was governed by a limited set of reasoning tactics. Our work with IATC highlights features of mathematical reasoning, like analogy, that more general heuristics will need to account for.
There are other resources available which could further expand IATC’s offerings in this regard. For example, a recent special issue of Argument & Computation (Harris and Marco 2017) includes papers detailing the usefulness of rhetorical structures for argument mining. Mitrović et al. (2017), in that volume, indicate the SALT Rhetorical Ontology (Groza 2012) as relevant prior work. SALT contains three categories—coherence relations, argument scheme relations, and rhetorical blocks—each of which unfolds with considerable further detail. These three categories can be seen as somewhat analogous to IATC’s grammatical categories. Mitrović et al. (2017) and Lawrence et al. (2017) point to foundational work of Fahnestock (1999, 2004) on the argumentative function of rhetorical figures, particularly in science writing. IATC might be profitably connected to such analyses. Furthermore, the integration of rhetoric into argument mining highlights the relevance of structures that are rather different from the IATstyle transitions that have been used in work summarised by Budzynska et al. (2015). White’s (1978, p. 6) pithy assertion that “logic itself is merely a formalization of tropical strategies” can serve as an additional provocation to develop structural analyses of this sort.
Nevertheless, whether mathematical content is modelled using ideas from logic, rhetoric, or other sources, considerable further work will be required to effectively describe the processes that are employed in forming and responding to mathematical arguments. A small case study included as an appendix to Pease et al. (2017) (and, incidentally, based on MiniPolymath 3) illustrates the plausibility of Lakatos’s model—however that model is clearly far from complete as a theory of mathematical production. Pease et al. were concerned with mathematical content only insofar as it fills slots for some 20 dialogue moves that are based on Lakatos’s strategy for arguing about lemmas and counterexamples. For example, \( MonsterBar (m, c, r)\) gives a reason r, contradicting the justification m for the counterconjecture notc. At no point does this theory touch the supposed mathematical ground of axioms and rules of inference. That the reason r, for example, may have been formed inductively, or deductively, or in some other way, goes undiscussed. IATC would allow us to expand the structure that appears within statements like r. Whereas Pease et al.’s formalisation of Lakatosian reasoning as a dialogue game offers a computational model of certain dynamical patterns in mathematics, our current work has focused on kinematics. The efforts can be seen as complementary: Bundy (2013) has argued that the right representation can considerably simplify reasoning.
One promising approach to modelling process combines argumentation and multiagent systems (Modgil and McGinnis 2007; Maghraby et al. 2012; Robertson 2012). However, most approaches to modelling specifically mathematical agents have had significant limitations. Thus, for example, Fiedler and Horacek (2007) have described the difficulty of squaring argumentationtheoretic work with the methods of formal proof. Ganesalingam and Gowers’s (2017) project aimed at simulating a solitary individual rather than a population. However, Furse (1990) had already called into question the robustness of approaches to modelling mathematical creativity that only model a solitary creative individual. Pease et al. (2009) describe an implementation effort that made use of a multiagent approach, drawing on argumentation theory concepts and a Lakatosian model of dialogue. However, the mathematical applications of that system were limited to straightforward computational aspects of number theory and group theory, which suggests a “knowledge bottleneck” (SaintDizier 2016; Moens 2018).
As indicated in a report of the National Research Council (2014, p. 90), “knowledge extraction and structuring in the context of mathematics” is in demand on an increasingly industrial scale. IATC allows methods of argumentation to interface with those of knowledge representation; both aspects are relevant to knowledge extraction. Formalisation of IATC would assist in its applicability: “IKL Conceptual Graphs” defined by Sowa (2008) would provide a natural foundation. IKL, the IKRIS Knowledge Language (Hayes 2006; Sowa 2008), deals elegantly with context and has been used as a representational formalism in a project with aims comparable to our own: the Slate project (Bringsjord et al. 2008), which centred on an argumentation tool that could support a mixture of deductive and informal reasoning.^{4} Previous work on mathematical usage can also inform future efforts in knowledge modelling with IATC (Trzeciak 2012; Wells 2003; Wolska 2015; Ginev 2011).
Mathematical Knowledge Management, particularly in the “flexiformal” understanding developed by Kohlhase (2012) and Kohlhase et al. (2017), presents another paradigm that could eventually be integrated with IATC. Flexiformality combines strict formalisations of those parts of mathematics for which that makes sense with opaque representations of constants, objects, and informal theories. Iancu (2017) built on Kohlhase’s work, and focused on “corepresenting both the narration and content aspects of mathematical knowledge in a structure preserving way” (pp. 3–4). However, modelling narrative in Iancu’s sense is more relevant to the “frontstage” presentation of mathematics in a single authorial voice than to the “backstage” production of mathematics (cf. Hersh 1991)). Section 4.2 illustrated one such example from backstage: mathematicians need to be able to choose between different mathematical problems.
IATC offers a step forward for research into both the communication and production of mathematics, and can play a role in future work on knowledge extraction and simulation. Potential applications include, among others, the development of a new generation of mathematics tutoring software and digital assistants that engage their users in thoughtprovoking dialogues.
Footnotes
 1.
The dialogue game defines ordered operations on a shared information state represented in the Argument Interchange Format (AIF) (Lawrence et al. 2012), which is then interpreted by The Online Argument Structures Tool (TOAST) (Snaith and Reed 2012) and passed on to DungOMatic (Snaith et al. 2010) to calculate the grounded extension, which in this case represents the currently accepted, collaboratively constructed, proof or theory under discussion.
 2.
In such a setting the formal argumentationtheoretic techniques and tools mentioned in Footnote 1 can be applied, though IAT models are not required to be fully formal in this regard.
 3.
Only a few of the keywords available in the latest version of TLA\(^{+}\) appear in the structured proof notation. Per Lamport (2015), the full list of TLA\(^{+}\) keywords is as follows. Those which are also used in structured proofs are decorated with underlining: assume ... prove ..., boolean, by, case, choose, constant (synonymously, constants), corollary, def, define, domain, else, except, extends, have, hide, if, instance, lambda, lemma, let ... in ..., new, omitted, pick, proposition, recursive, subset, suffices, take, theorem, unchanged, union, use, variable, witness.
 4.
Notes
Acknowledgements
Our anonymous reviewers offered comments that improved the paper. The authors also thank Katarzyna Budzynska, Alan Bundy, Pat Hayes, Raymond Puzio, and Chris Reed for helpful discussions, and acknowledge the support of fellow researchers in the ARGTech group at the University of Dundee, and the DReaM group at the University of Edinburgh. Figures were drawn using IHMC CmapTools, the Python libraries matplotlib and plotly, and the TikZ package for LaTeX. Funding was provided by Engineering and Physical Sciences Research Council (Grant No. EP/R03169X/1).
References
 Aberdein, A., and I.J. Dove. 2013. Introduction. In The argument of mathematics, ed. A. Aberdein and I.F. Dove, 1–8. Dordrecht: Springer. https://doi.org/10.1007/9789400765344_1.CrossRefGoogle Scholar
 Aliseda, A. 2003. Mathematical reasoning versus abductive reasoning: a structural approach. Synthese 134(1–2): 25–44.CrossRefGoogle Scholar
 Barany, M. 2010. ‘[B]ut this is blog maths and we’re free to make up conventions as we go along’: Polymath1 and the modalities of ‘massively collaborative mathematics’. In: Ayers P, Ortega F (eds) Proceedings of the 6th international symposium on Wikis and open collaboration, ACM.Google Scholar
 Botting, D. 2015. Inferences and illocutions. Argument & Computation 6(3): 246–264.CrossRefGoogle Scholar
 Bringsjord, S., J. Taylor, A. Shilliday, M. Clark, K. Arkoudas, M. Schoelles, M. Destefano, and J. Wodicka. 2008. Slate: an argumentcentered intelligent assistant to human reasoners. In Grasso F, Green N, Kibble R, and Reed C (eds) Proceedings of the 8th international workshop on computational models of natural argument (CMNA 2008), pp 1–10.Google Scholar
 Budzynska, K. 2013. Circularity in ethotic structures. Synthese 190(15): 3185–3207.CrossRefGoogle Scholar
 Budzynska, K., and C. Reed. 2011. Speech acts of argumentation: inference anchors and peripheral cues in dialogue. In Grasso F, Green N, and Reed C (eds) Computational models of natural argument: papers from the 2011 AAAI workshop. http://www.aaai.org/ocs/index.php/WS/AAAIW11/paper/view/3940.
 Budzynska, K., M. Janier, C. Reed, and P. SaintDizier. 2013. Towards extraction of dialogical arguments. In Grasso F, Green N, and Reed C (eds) Proceedings of 13th international conference on computational models of natural argument (CMNA 2013).Google Scholar
 Budzynska, K., M. Janier, J. Kang, C. Reed, P. SaintDizier, M. Stede, and O. Yaskorska. 2014a. Towards argument mining from dialogue. In Parsons S, Oren N, Reed C, and Cerutti F (eds) Computational models of argument: proceedings of COMMA 2014, IOS Press, Frontiers in artificial intelligence and applications, vol 266, pp 185–196. http://comma2014.arg.dundee.ac.uk/res/pdfs/19budzynska.pdf.
 Budzynska, K., M. Janier, C. Reed, P. SaintDizier, M. Stede, and O. Yaskorska. 2014b. A model for processing illocutionary structures and argumentation in debates. In Calzolari N, Choukri K, Declerck T, Loftsson H, Maegaard B, Mariani J, Moreno A, Odijk J, and Piperidis S (eds) Proceedings of the ninth international conference on language resources and evaluation (LREC2014), Reykjavik, Iceland, May 26–31, 2014, pp 917–924. http://www.lrecconf.org/proceedings/lrec2014/summaries/77.html.
 Budzynska, K., M. Janier, J. Kang, B. Konat, C. Reed, P. SaintDizier, M. Stede, and O. Yaskorska. 2015. Automatically identifying transitions between locutions in dialogue. In Mohammed D and Lewinski M (eds) Argumentation and reasoned action: proceedings of the 1st European conference on argumentation, Lisbon 2015, Volume II, College Publications, no. 63 in Studies in Logic and Argumentation, pp 311–328.Google Scholar
 Budzynska, K., M. Janier, C. Reed, and P. SaintDizier. 2016. Theoretical foundations for illocutionary structure parsing. Argument & Computation 7(1): 91–108.Google Scholar
 Bundy, A. 1988. The use of explicit plans to guide inductive proofs. In Lusk E, Overbeek R (eds) 9th International conference on automated deduction, Argonne, Illinois, USA, May 23–26, 1988. Proceedings, Springer, pp 111–120.Google Scholar
 Bundy, A. 2013. The interaction of representation and reasoning. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 469(2157), http://rspa.royalsocietypublishing.org/content/469/2157/20130194.
 Carrascal, B. 2015. Proofs, mathematical practice and argumentation. Argumentation 29(3): 305–324. https://doi.org/10.1007/s1050301493440.CrossRefGoogle Scholar
 Chandrasekhar, et al. 2010. Group cannot be the union of conjugates. http://mathoverflow.net/q/34044.
 Corneli, J., U. Martin, D. MurrayRust, and A. Pease. 2017a. Towards mathematical AI via a model of the content and process of mathematical question and answer dialogues. In Geuvers H, England M, Hasan O, Rabe F, and Teschke O (eds) Intelligent computer mathematics 10th international conference, CICM 2017, Edinburgh, UK, 2017, Proceedings.Google Scholar
 Corneli, J., U. Martin, D. MurrayRust, A. Pease, R. Puzio, and G. Rino Nesin. 2017b. Modelling the way mathematics is actually done. In Sperber M, Bresson J, Santolucito M, and McLean A (eds) 2017 InternationalWorkshop on functional art, music, modelling and design (FARM 2017), ACM.Google Scholar
 Corneli, J., A. Pease, and D. Stefanou. 2018. Social aspects of concept invention. In Concept invention: foundations, implementation, social aspects and applications, ed. R. Confalonieri, A. Pease, M. Schorlemmer, T. Besold, O. Kutz, E. Maclean, and M. KaliakatsosPapakostas. Computational Synthesis and Creative Systems: Springer.Google Scholar
 Cramer, M. 2013. Proofchecking mathematical texts in controlled natural language. Ph.D. thesis, MathematischNaturwissenschaftlichen Fakultät der Rheinischen FriedrichWilhelmsUniversität Bonn.Google Scholar
 Dauphin, J., and M. Cramer. 2018. ASPICEND: structured argumentation with explanations and natural deduction. In Black E, Modgil S, and Oren N (eds) Theory and applications of formal argumentation 4th international workshop, TAFA 2017, Melbourne, VIC, Australia, August 19–20, 2017, Revised Selected Papers, Springer, no. 10757 in LNAI, pp 51–66.Google Scholar
 Dove, I.J. 2009. Towards a theory of mathematical argument. Foundations of Science 14(1–2): 137–152.CrossRefGoogle Scholar
 Dufour, M. 2013. Arguing around mathematical proofs. In The argument of mathematics, ed. A. Aberdein and I.J. Dove, 61–76. Dordrecht: Springer. https://doi.org/10.1007/9789400765344_5.CrossRefGoogle Scholar
 Dutilh Novaes, C. 2016. Reductio ad absurdum from a dialogical perspective. Philosophical Studies 173(10): 2605–2628.CrossRefGoogle Scholar
 Epstein, S.L. 2015. Wanted: collaborative intelligence. Artificial Intelligence 221: 36–45.CrossRefGoogle Scholar
 Fahnestock, J. 1999. Rhetorical figures in science. Oxford: Oxford University Press.Google Scholar
 Fahnestock, J. 2004. Figures of argument (OSSA 2005 Keynote Address). Informal Logic 24(2).Google Scholar
 Fiedler, A., and H. Horacek. 2007. Argumentation within deductive reasoning. International Journal of Intelligent Systems 22(1): 49–70.CrossRefGoogle Scholar
 Furse, E. 1990. Why did AM run out of steam? Tech. rep., CS904, Department of Computer Studies, University of Glamorgan.Google Scholar
 Ganesalingam, M. 2013. The language of mathematics: a linguistic and philosophical investigation, LNCS, vol 7805. Springer.Google Scholar
 Ganesalingam, M., and W. Gowers. 2017. A fully automatic theorem prover with humanstyle output. Journal of Automated Reasoning 58: 253–291.CrossRefGoogle Scholar
 Gasteren, A.J. 1990. On the shape of mathematical arguments, vol. 445., Lecture Notes in Computer Science Berlin: Springer.CrossRefGoogle Scholar
 Ginev, D. 2011. The structure of mathematical expressions. Master’s thesis. Bremen, Germany: Jacobs University.Google Scholar
 Gowers, W. 2017. How do human mathematicians avoid big searches? https://www.newton.ac.uk/seminar/20170728133014301, Lecture, Fri, July 28, 13:30–14:30, Isaac Newton Institute, University of Cambridge.
 Gowers, W., and M. Ganesalingam. 2012. Modelling the mathematical discovery process. Maxwell Institute Lecture, Fri, November 2, 4pm – 5pm, James Clerk Maxwell Building, University of Edinburgh.Google Scholar
 Groth, P., and L. Moreau. 2013. PROVoverview. An overview of the PROV family of documents. In World Wide Web consortium. https://www.w3.org/TR/provoverview/.
 Groza, T. 2012. Advances in semantic authoring and publishing, Studies on the Semantic Web, vol. 13. Heidelberg: IOS Press.Google Scholar
 Harris, R., and C. D. Marco. 2017. Argument & Computation 8(3) [Special Issue on Rhetorical figures, arguments, computation.]Google Scholar
 Hayes, P. 2006. IKL guide. http://www.ihmc.us/users/phayes/IKL/GUIDE/GUIDE.html.
 Hendrix, GG. 1975. Partitioned networks for the mathematical modeling of natural language semantics. Ph.D. thesis, The University of Texas at Austin, also archived as Tech. Report NL28, Department of Computer ScienceD.Google Scholar
 Hendrix, G. G. 1979. Encoding knowledge in partitioned networks. In Associative networks: representation and use of knowledge by computers, ed. N. V. Findler, 51–92. New York: Academic Press.CrossRefGoogle Scholar
 Hersh, R. 1991. Mathematics has a front and a back. Synthese 88(2): 127–133.CrossRefGoogle Scholar
 Iancu, M. 2017. Towards flexiformal mathematics. Ph.D. thesis, Jacobs University, Bremen, DE.Google Scholar
 Inglis, M., J.P. MejiaRamos, and A. Simpson. 2007. Modelling mathematical argumentation: the importance of qualification. Educational Studies in Mathematics 66(1): 3–21.CrossRefGoogle Scholar
 Janier, M., and C. Reed. 2017. Towards a theory of close analysis for dispute mediation discourse. Argumentation 31(1): 45–82.CrossRefGoogle Scholar
 Kamp, H., and U. Reyle. 1993. From discourse to logic introduction to modeltheoretic semantics of natural language, formal logic and discourse representation theory, studies in linguistics and philosophy, vol 42. Springer.Google Scholar
 Karttunen, L. 1976. Discourse referents. In Syntax and semantics, vol 7, ed. J.D. McCawley. Notes from the Linguistic Underground, Academic Press, pp 363–386D.Google Scholar
 Klaus, K. 2004. Content analysis: an introduction to its methodology, 2nd ed. Thousand Oaks: Sage Publications.Google Scholar
 Kohlhase, M. 2012. The flexiformalist manifesto. In Voronkov A, Negru V, Ida T, Jebelean T, Petcu D, Watt S, and Zaharie D (eds) Symbolic and numeric algorithms for scientific computing (SYNASC 2012), 14th international symposium on, IEEE, pp 30–35.Google Scholar
 Kohlhase, M., T. Koprucki, D. Müller, and K. Tabelow. 2017. Mathematical models as research data via flexiformal theory graphs. In Geuvers H, England M, Hasan O, Rabe F, and Teschke O (eds) Intelligent computer mathematics 10th international conference, CICM 2017, Edinburgh, UK, July 17–21, 2017, Proceedings, Springer, LNAI, vol 10383, pp 224–238.Google Scholar
 Lakatos, I. 1976. Proofs and refutations: the logic of mathematical discovery. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
 Lamport, L. 1995. How to write a proof. The American Mathematical Monthly 102(7): 600–608.CrossRefGoogle Scholar
 Lamport, L. 1999. Specifying concurrent systems with TLA\(^{+}\). NATO Science Series, III: Computer and Systems Sciences 173(173): 183–247.Google Scholar
 Lamport, L. 2012. How to write a 21st century proof. Journal of Fixed Point Theory and Applications 11(1): 43–63.CrossRefGoogle Scholar
 Lamport, L. 2014. TLA\(^{+2}\): a preliminary guide. http://lamport.azurewebsites.net/tla/tla2guide.pdf.
 Lamport, L. 2015. The TLA\(^{+}\) hyperbook. http://lamport.azurewebsites.net/tla/hyperbook.html.
 Larvor, B. 2012. How to think about informal proofs. Synthese 187(2): 715–730.CrossRefGoogle Scholar
 Lawrence, J., F. Bex, C. Reed, and M. Snaith. 2012. AIFdb: infrastructure for the argument web. In Verheij B, Szeider S, and Woltran S (eds) Computational models of argument: proceedings of COMMA 2012, IOS Press, Frontiers in artificial intelligence and applications, vol 245, pp 515–516.Google Scholar
 Lawrence, J., J. Visser, and C. Reed. 2017. Harnessing rhetorical figures for argument mining. Argument & Computation 8(3): 289–310.CrossRefGoogle Scholar
 Maghraby, A., D. Robertson, A. Grando, and M. Rovatsos. 2012. Automated deployment of argumentation protocols. In Verheij B, Szeider S, and Woltran S (eds) Computational models of argument: proceedings of COMMA 2012, IOS Press, Frontiers in artificial intelligence and applications, vol 245, pp 197–204.Google Scholar
 Martin, U. 2015. Stumbling around in the dark: lessons from everyday mathematics. In Automated deduction—CADE25, ed. A. P. Felty and A. Middeldorp, 29–51. Cham: Springer. https://doi.org/10.1007/9783319214016_2.CrossRefGoogle Scholar
 Martin, U., and A. Pease. 2013. What does mathoverflow tell us about the production of mathematics? In Novak J and Jaimes A (eds) SOHUMAN, 2nd international workshop on social media for crowdsourcing and human computation, at ACM Web Science 2013, May 1, 2013, Paris. https://arxiv.org/abs/1305.0904.
 Mercier, H., and D. Sperber. 2011. Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences 34(2): 57–74.CrossRefGoogle Scholar
 Mitrović, J., C. OReilly, M. Mladenović, and S. Handschuh. 2017. Ontological representations of rhetorical figures for argument mining. Argument & Computation 8(3): 267–287.CrossRefGoogle Scholar
 Modgil, S., and J. McGinnis. 2007. Towards characterising argumentation based dialogue in the argument interchange format. In Argumentation in multiagent systems: 4th international workshop, ArgMAS 2007, Honolulu, HI, USA, May 15, 2007, ed. I. Rahwan, S. Parsons, and C. Reed, 80–93. Revised Selected and Invited Papers: Springer.Google Scholar
 Moens, M. F. 2018. Argumentation mining: How can a machine acquire common sense and world knowledge? Argument & Computation 9(1).Google Scholar
 National Research Council. 2014. Developing a 21st century global library for mathematics research. National Academies Press.Google Scholar
 Nielsen, M. et al. 2009–2018. Polymath wiki. http://michaelnielsen.org/polymath1/index.php?title=Main_Page.
 Pease, A., and U. Martin. 2012. Seventy four minutes of mathematics: an analysis of the third MiniPolymath project. In Larvor B and Pease A (eds) Proceedings of AISB/IACAP 2012, symposium on mathematical practice and cognition II. http://homepages.inf.ed.ac.uk/apease/papers/seventyfour.pdf.
 Pease, A., A. Smaill, S. Colton, and J. Lee. 2009. Bridging the gap between argumentation theory and the philosophy of mathematics. Foundations of Science 14(1): 111–135.CrossRefGoogle Scholar
 Pease, A., J. Lawrence, K. Budzynska, J. Corneli, and C. Reed. 2017. Lakatosstyle collaborative mathematics through dialectical, structured and abstract argumentation. Artificial Intelligence 246:181–219. http://www.sciencedirect.com/science/article/pii/S0004370217300267.
 Pedemonte, B. 2007. How can the relationship between argumentation and proof be analysed? Educational Studies in Mathematics 66 (1): 23–41. https://doi.org/10.1007/s106490069057x.CrossRefGoogle Scholar
 Reed, C., and K. Budzynska. 2010. How dialogues create arguments. In van Eemeren F, Garssen B, Godden D and Mitchell G (eds) Proceedings of the 7th conference of the international society for the study of argumentation (ISSA 2010).Google Scholar
 Reed, C., K. Budzynska, R. Duthie, M. Janier, B. Konat, J. Lawrence, A. Pease, and M. Snaith. 2017. The argument web: an online ecosystem of tools, systems and services for argumentation. Philosophy & Technology 30(2): 137–160. https://doi.org/10.1007/s1334701702608.CrossRefGoogle Scholar
 Robertson, D. 2012. Lightweight coordination calculus for agent systems: retrospective and prospective. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 84–89. https://doi.org/10.1007/9783642291135_7.
 Robinson, J.A. 1965. A machineoriented logic based on the resolution principle. Journal of the ACM (JACM) 12(1): 23–41.CrossRefGoogle Scholar
 SaintDizier, P. 2016. Argument mining: the bottleneck of knowledge and language resources. In Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, and Piperidis S (eds) 10th International conference on language resources and evaluation (LREC 2016), pp 983–990.Google Scholar
 Snaith, M., and C. Reed. 2012. TOAST: online ASPIC+ implementation. In Verheij B, Szeider S, and Woltran S (eds) Computational models of argument: proceedings of COMMA 2012, IOS Press, Frontiers in artificial intelligence and applications, vol 245, pp 509–510.Google Scholar
 Snaith, M., and C. Reed. 2016. Dialogue grammar induction. In Mohammed D and Lewinski M (eds) Argumentation and reasoned action, volume I: proceedings of the 1st European conference on argumentation, Lisbon 2015, Studies in Logic and Argumentation, vol 62, College Publications.Google Scholar
 Snaith, M., J. Devereux, J. Lawrence, and C. Reed. 2010. Pipelining argumentation technologies. In Baroni P, Cerutti F, Giacomin M, and R S. G. (eds) Computational models of argument: proceedings of COMMA 2010, IOS Press, Frontiers in artificial intelligence and applications, vol 216, pp 447–454.Google Scholar
 Sowa, J. F. 2000. Knowledge representation: logical, philosophical, and computational foundations. MIT PressGoogle Scholar
 Sowa, J. F. 2008. Conceptual graphs. In Handbook of knowledge representation, ed. van Harmelen F, Lifschitz V, Porter B, chap 5, pp 213–237. Elsevier.Google Scholar
 Sowa, J. F., and A. K. Majumdar. 2003. Analogical reasoning. In Aldo A, Lex W, and Ganter B (eds) Conceptual structures for knowledge creation and communication: 11th international conference on conceptual structures, ICCS 2003, Dresden, Germany, July 21–25, 2003, Proceedings, Springer, no. 2746 in LNAI, pp 16–36.Google Scholar
 Tanswell, F. 2015. A problem with the dependence of informal proofs on formal proofs. Philosophia Mathematica 23 (3): 295. https://doi.org/10.1093/philmat/nkv008.CrossRefGoogle Scholar
 Tao, T., et al. 2009. IMO 2009 Q6 as a minipolymath project. https://wp.me/p3qzPEf.
 Tao, T., et al. 2011. Minipolymath3 project: 2011 IMO. https://wp.me/pAG2F41.
 Trzeciak, J. 2012. Mathematical English usage. A dictionary. http://www.emis.de/monographs/Trzeciak/biglist.html.
 van der Sandt, R.A. 1992. Presupposition projection as Anaphora resolution. Journal of Semantics 9(4): 333–377. https://doi.org/10.1093/jos/9.4.333.CrossRefGoogle Scholar
 van Oers, B. 2002. Fruits of polyphony: a commentary on a multiperspective analysis of mathematical discourse. Journal of the Learning Sciences 11(2–3): 359–363. https://doi.org/10.1080/10508406.2002.9672143.CrossRefGoogle Scholar
 Visser, J., F. Bex, C. Reed, and B. Garssen. 2011. Correspondence between the pragmadialectical discussion model and the argument interchange format. Studies in Logic, Grammar and Rhetoric 23(36): 189–224.Google Scholar
 Walton, D., C. Reed, and F. Macagno. 2008. Argumentation schemes. New York: Cambridge University Press.CrossRefGoogle Scholar
 Wells, C. 2003. A handbook of mathematical discourse. West Conshohocken: Infinity Publishing.Google Scholar
 White, H.V. 1978. Tropics of discourse: essays in cultural criticism. Baltimore: Johns Hopkins University Press.Google Scholar
 Wolska, M. A. 2015. Students’ language in computerassisted tutoring of mathematical proofs. No. 40 in Saarbrücken Dissertations in Language Science and Technology, Universaar.Google Scholar
 Zack, V., and B. Graves. 2001. Making mathematical meaning through dialogue: “once you think of it, the Z minus three seems pretty weird”. Educational Studies in Mathematics 46(1): 229. https://doi.org/10.1023/A:1014045408753.CrossRefGoogle Scholar
 Zinn, C. 2004. Understanding informal mathematical discourse. Ph.D. thesis, Institut fur Informatik, Universitat ErlangenNurnberg.Google Scholar
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