Anthony Hunter

JAIR Journal 2025 Journal Article

A Graphical Formalism for Reasoning about Substitution in Resource Transforming Procedures

• Antonis Bikakis
• Fabio Aurelio D'Asaro
• Aissatou Diallo
• Luke Dickens
• Anthony Hunter
• Rob Miller

The ability to repurpose and substitute materials and resources when necessary is an important aspect of human reasoning and activity. In particular, substitution plays a vital role in resource consuming and artifact producing activities – purposeful, goal directed procedures that transform resources from raw materials into finished products, the descriptions of which we refer to here as recipes. To see this, consider how adaptable humans are when we encounter constraints, such as limited resources, when making, manufacturing and constructing. In spite of this there has been comparatively little work given to developing representations for substitution within such contexts in a formal reasoning framework. We address this gap by proposing a graphical formalisation that captures consumables and the actions on them in the form of labelled bipartite graphs. Using examples such as “do it yourself" (DIY) instructions, manufacturing processes and cooking recipes to illustrate, we then propose formal definitions for comparing recipes, for composing recipes from subrecipes, and for deconstructing recipes into subrecipes. We then introduce and compare two formal definitions for substitution which are required when there are missing consumables, or some actions are not possible, or because there is some need to change the final product. We illustrate how automated reasoning about recipes in this context may be achieved by implementing our definitions in answer set programming (ASP).

IJCAI Conference 2025 Conference Paper

A Logic-based Framework for Decoding Enthymemes in Argument Maps Involving Implicitness in Premises and Claims

• Victor David
• Anthony Hunter

Argument mining is a natural language processing technology aimed at identifying the explicit premises and claims of arguments in text, and the support and attack relationships between them. To better understand, and automatically analyse, the argument maps that are output from argument mining, it would be desirable to instantiate the arguments in the argument map with logical arguments. However, most real-world arguments are enthymemes (i. e. some of the premises and/or claim are implicit), which need to be decoded (i. e. the implicit aspects need to be identified). A key challenge is to decode enthymemes so as to respect the support and attack relationships in the argument map. addressing the problem of identifying the missing premises and/or claim, and discerning the relationships between them. To address this, we present a novel framework, based on default logic, for representing arguments including enthymemes. We show how decoding an enthymeme means identifying the default rules that are implicit in the premises and claims. We then show how choosing a decoding of the enthymemes in an argument map can be formalized as an optimization problem, and that a solution can be obtained using MaxSAT solvers.

KR Conference 2025 Conference Paper

An Axiomatic Study of a Modular Evaluation of Enthymeme Decoding in Weighted Structured Argumentation

• Jonathan Ben-Naim
• Victor David
• Anthony Hunter

An argument can be seen as a pair of premises and a claim they support. Human arguments are often approximate, with some premises left implicit, leading to an implicit inference of the claim, i. e. , forming enthymemes. To better understand and use them, we must decode these approximate enthymemes, typically by identifying missing premises to make the inference explicit, and, as we propose, by also removing irrelevant content to improve argument quality in specific contexts. Often, multiple decodings of an enthymeme are possible. However, no formal method has yet been proposed for identifying higher-quality decodings. To pave the way, we introduce six types of criteria for evaluating aspects of decodings. Then, we introduce the concept of a criterion measure, designed to evaluate decodings based on a specific criterion. In parallel, we define desirable properties for criterion measures, referred to as axioms, and we systematically evaluate our criterion measures with respect to them. Finally, we introduce the notion of quality measure that combine specific criterion measures to give an overall evaluation of the quality of decodings.

AAAI Conference 2025 Conference Paper

Germane Conflicts: Desirable Properties for Localising Inconsistency

• Glauber de Bona
• Anthony Hunter

Inconsistency is a common problem in knowledge, and so there is a need to analyse it. Inconsistency measures assess its severity, but there is a more basic question: "where is the inconsistency?". Typically, not all subsets of a knowledgebase are causing the inconsistency, and minimal inconsistent sets have been the standard way to localise the germane ones, even though there are shortcomings in some scenarios. Recently, ⋆-conflicts were proposed as a more suitable definition to localise inconsistency when considering a method to repair it. But in general there is no way to tell what is a sensible definition to capture the germane conflicts. This work provides a set of desirable properties to assess definitions for germane conflicts. Also, a new conflict definition, based on substitution, is presented and evaluated via the proposed properties, and the related computational complexity is analysed.

ECAI Conference 2025 Conference Paper

RESPONSE: Benchmarking the Ability of Language Models to Undertake Commonsense Reasoning in Crisis Situation

• Aïssatou Diallo
• Antonis Bikakis
• Luke Dickens
• Anthony Hunter
• Rob Miller 0002

Commonsense reasoning is a key aspect of human intelligence. If we are to develop robust and deep intelligent systems, then we need to understand the diversity and complexity of commonsense reasoning across the gamut of human activities. An interesting class of commonsense reasoning problems arises when people are faced with natural disasters. To investigate this topic, we present RESPONSE, a human-curated dataset containing 1789 annotated instances featuring 6037 sets of questions designed to assess LLMs’ commonsense reasoning in disaster situations across different time frames. The dataset includes problem descriptions, missing resources, time-sensitive solutions, and their justifications, with a subset validated by environmental engineers. Through both automatic metrics and human evaluation, we compare LLM-generated recommendations against human responses. Our findings show that even state-of-the-art models like GPT-4 achieve only 37% human-evaluated correctness for immediate response actions, highlighting significant room for improvement in LLMs’ ability for commonsense reasoning in crises.

FLAP Journal 2025 Journal Article

We Love Inconsistency

• Anthony Hunter

Inconsistency is an important phenomenon for agents operating in the real- world. And it is now recognized as a key issue in many areas of artificial intelli- gence, and more broadly in computer science. Here I revisit a proposal by Dov Gabbay for switching from thinking about inconsistency as being necessarily bad, to something that may be desirable. This led to the idea that inconsis- tency is not something that we have to eliminate, but rather it is something that we need to act upon. Actions may range from isolating an inconsistency, seeking more clarification on the information involved in an inconsistency, through to exploiting an inconsistency. As an example of the latter, consider a researcher finding inconsistencies in the literature, which can then be exploited by con- structing valuable new research questions.

ECAI Conference 2024 Conference Paper

Compromises in Dialogical Argumentation: Aggregated Policies for Biparty Decision Theory

• Ivan Donadello
• Renan Lirio de Souza
• Anthony Hunter
• Mauro Dragoni

Automated persuasion systems (APS) are conversational agents that exchange arguments and counterarguments with users during dialogues to persuade them to believe in something. Such systems use strategies (or policies) to carefully select a sequence of arguments that are tailored to the user’s needs and will likely have a positive outcome, that is, changing the user’s belief in a certain argument. Biparty Decision Theory (BDT) is a framework that uses game theory to formalize a dialogue between an APS and a user, that is, an exchange of (counter) arguments during each turn of the APS or the user. During the APS turn, the BDT policy selects the best argument to maximize only the utility for the APS and neglects the utility of that argument for the user. This is a reasonable choice in games, but in a persuasive dialogue, it can result in arguments that have a high utility for the APS but a modest utility for the user. There the user may be less likely to be persuaded. This is crucial in settings where there are no arguments with good utilities for both the APS and the user and a compromise has to be found. To this extent, we define a new family of policies for BDT, called aggregated policies, that consider, during the decisions of the APS, an aggregation of the APS and user’s utilities. Such an aggregation considers both the APS and the user’s needs leading toward a sequence of arguments representing the best trade-off of utilities. We evaluate the approach using both a new synthetic dataset and a published dataset of utilities for dialogical argumentation. The results show the aggregated policies find better compromise arguments w. r. t. the classical policy of BDT.

AIIM Journal 2023 Journal Article

Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations

• Jetsun Whitton
• Anthony Hunter

Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain specialists must aggregate and extract information from thousands of publications, primarily of randomised controlled trial (RCT) results, into evidence tables. This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: named entity recognition, which identifies key entities within text, such as drug names, and relation extraction, which maps their relationships for separating them into ordered tuples. We focus on the automatic tabulation of sentences from published RCT abstracts that report the results of the study outcomes. Two deep neural net models were developed as part of a joint extraction pipeline, using the principles of transfer learning and transformer-based language representations. To train and test these models, a new gold-standard corpus was developed, comprising over 550 result sentences from six disease areas. This approach demonstrated significant advantages, with our system performing well across multiple natural language processing tasks and disease areas, as well as in generalising to disease domains unseen during training. Furthermore, we show these results were achievable through training our models on as few as 170 example sentences. The final system is a proof of concept that the generation of evidence tables can be semi-automated, representing a step towards fully automating systematic reviews.

AIJ Journal 2023 Journal Article

Syntactic reasoning with conditional probabilities in deductive argumentation

• Anthony Hunter
• Nico Potyka

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence.

AAAI Conference 2022 Conference Paper

Machine Learning for Utility Prediction in Argument-Based Computational Persuasion

• Ivan Donadello
• Anthony Hunter
• Stefano Teso
• Mauro Dragoni

Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the probability that an APS is successful in persuading a user, it can identify a global policy that will allow it to select the best arguments it presents at each stage of the dialogue whatever arguments the user presents. However, in real applications, such as for healthcare, it is unlikely the utility of the outcome of the dialogue will be the same, or the exact opposite, for the APS and user. In order to deal with this situation, games in extended form have been harnessed for argumentation in Bi-party Decision Theory. This opens new problems that we address in this paper: (1) How can we use Machine Learning (ML) methods to predict utility functions for different subpopulations of users? and (2) How can we identify for a new user the best utility function from amongst those that we have learned? To this extent, we develop two ML methods, EAI and EDS, that leverage information coming from the users to predict their utilities. EAI is restricted to a fixed amount of information, whereas EDS can choose the information that best detects the subpopulations of a user. We evaluate EAI and EDS in a simulation setting and in a realistic case study concerning healthy eating habits. Results are promising in both cases, but EDS is more effective at predicting useful utility functions.

AAAI Conference 2022 Conference Paper

Understanding Enthymemes in Deductive Argumentation Using Semantic Distance Measures

• Anthony Hunter

An argument can be regarded as some premises and a claim following from those premises. Normally, arguments exchanged by human agents are enthymemes, which generally means that some premises are implicit. So when an enthymeme is presented, the presenter expects that the recipient can identify the missing premises. An important kind of implicitness arises when a presenter assumes that two symbols denote the same, or nearly the same, concept (e. g. dad and father), and uses the symbols interchangeably. To model this process, we propose the use of semantic distance measures (e. g. based on a vector representation of word embeddings or a semantic network representation of words) to determine whether one symbol can be substituted by another. We present a theoretical framework for using substitutions, together with abduction of default knowledge, for understanding enthymemes based on deductive argumentation, and investigate how this could be used in practice.

AAAI Conference 2020 Conference Paper

Aggregation of Perspectives Using the Constellations Approach to Probabilistic Argumentation

• Anthony Hunter
• Kawsar Noor

In the constellations approach to probabilistic argumentation, there is a probability distribution over the subgraphs of an argument graph, and this can be used to represent the uncertainty in the structure of the argument graph. In this paper, we consider how we can construct this probability distribution from data. We provide a language for data based on perspectives (opinions) on the structure of the graph, and we introduce a framework (based on general properties and some specific proposals) for aggregating these perspectives, and as a result obtaining a probability distribution that best reflects these perspectives. This can be used in applications such as summarizing collections of online reviews and combining conflicting reports.

AIJ Journal 2020 Journal Article

Epistemic graphs for representing and reasoning with positive and negative influences of arguments

• Anthony Hunter
• Sylwia Polberg
• Matthias Thimm

This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine–grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context–sensitive. It also allows for better modelling of imperfect agents, which can be important in multi–agent applications.

ECAI Conference 2020 Conference Paper

Generating Instantiated Argument Graphs from Probabilistic Information

• Anthony Hunter

The epistemic approach to probabilistic argumentation assigns belief to arguments. To better understand this approach, we consider structured arguments. Our approach is to start with a probability distribution, and generate an argument graph containing structured arguments with a probability assignment. We construct arguments directly from the probability distribution, rather than a knowledgebase, and then consider methods for selecting the arguments and counterarguments to present in the argument graph. This provides mechanisms for managing uncertainty in argumentation, and for argument-based explanations of probability distributions (that might come from data or from beliefs of an agent).

KR Conference 2020 Conference Paper

Reasoning with Inconsistent Knowledge using the Epistemic Approach to Probabilistic Argumentation

• Anthony Hunter

Structured argumentation involves drawing inferences from knowledge in order to construct arguments and counterarguments. Since knowledge can be uncertain, we can use a probabilistic approach to representing and reasoning with the knowledge. Individual arguments can be constructed from the knowledge, with the belief in each argument determined just from the belief in the formulae appearing in the argument. However, if the original knowledgebase is inconsistent, this does not take into account the counterarguments that can be constructed. We therefore need a wider perspective that revises the belief in individual arguments in order to take into account the counterarguments. To address this need, we present a framework for probabilistic argumentation that uses relaxation methods to give a coherent view on the knowledge, and thereby revises the belief in the arguments that are generated from the knowledge.

JAIR Journal 2019 Journal Article

Classifying Inconsistency Measures Using Graphs

• Glauber de Bona
• John Grant
• Anthony Hunter
• Sebastien Konieczny

The aim of measuring inconsistency is to obtain an evaluation of the imperfections in a set of formulas, and this evaluation may then be used to help decide on some course of action (such as rejecting some of the formulas, resolving the inconsistency, seeking better sources of information, etc). A number of proposals have been made to define measures of inconsistency. Each has its rationale. But to date, it is not clear how to delineate the space of options for measures, nor is it clear how we can classify measures systematically. To address these problems, we introduce a general framework for comparing syntactic measures of inconsistency. It is based on the notion of an inconsistency graph for each knowledgebase (a bipartite graph with a set of vertices representing formulas in the knowledgebase, a set of vertices representing minimal inconsistent subsets of the knowledgebase, and edges representing that a formula belongs to a minimal inconsistent subset). We then show that various measures can be computed using the inconsistency graph. Then we introduce abstractions of the inconsistency graph and use them to construct a hierarchy of syntactic inconsistency measures. Furthermore, we extend the inconsistency graph concept with a labeling that extends the hierarchy to include some other types of inconsistency measures.

AAMAS Conference 2018 Conference Paper

Learning and Updating User Models for Subpopulations in Persuasive Argumentation Using Beta Distributions

• Emmanuel Hadoux
• Anthony Hunter

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. It is an important and multifaceted human facility both in professional life (e. g. , a doctor persuading a patient to give up smoking) and everyday life (e. g. , some friends persuading another to join them in seeing a film). Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, they cannot efficiently model uncertainty on the belief of individuals and cannot represent populations. We propose to use mixtures of beta distributions and apply them on real data gathered by linguists. We show that we can represent the belief and its uncertainty using beta mixtures and that we can predict the evolution of this belief after an argument is given. We also present examples of how to use the mixtures in practice to replace general belief update functions.

AAAI Conference 2018 Conference Paper

Towards a Unified Framework for Syntactic Inconsistency Measures

• Glauber de Bona
• John Grant
• Anthony Hunter
• Sébastien Konieczny

A number of proposals have been made to define inconsistency measures. Each has its rationale. But to date, it is not clear how to delineate the space of options for measures, nor is it clear how we can classify measures systematically. In this paper, we introduce a general framework for comparing syntactic inconsistency measures. It uses the construction of an inconsistency graph for each knowledgebase. We then introduce abstractions of the inconsistency graph and use the hierarchy of the abstractions to classify a range of inconsistency measures.

KR Conference 2018 Conference Paper

Updating Belief in Arguments in Epistemic Graphs

• Anthony Hunter
• Sylwia Polberg
• Nico Potyka

Epistemic graphs are a recent generalization of epistemic probabilistic argumentation. Relations between arguments can be supporting, attacking, as well as neither supporting nor attacking. These interdependencies are represented by epistemic constraints, and the semantics of epistemic graphs are given in terms of probability distributions satisfying these constraints. We investigate the behaviour of epistemic graphs in a dynamic setting where a given distribution can be updated once new constraints are presented. Our focus is on update methods that minimize the change in probabilistic beliefs. We show that all methods satisfy basic commonsense postulates, identify fragments of the epistemic constraint language that guarantee the existence of well-defined solutions, and explain how the problems that arise in more expressive fragments can be treated either automatically or by user support. We demonstrate the usefulness of our proposal by considering its application in computational persuasion.

AIJ Journal 2017 Journal Article

Localising iceberg inconsistencies

• Glauber de Bona
• Anthony Hunter

In artificial intelligence, it is important to handle and analyse inconsistency in knowledge bases. Inconsistent pieces of information suggest questions like “where is the inconsistency? ” and “how severe is it? ”. Inconsistency measures have been proposed to tackle the latter issue, but the former seems underdeveloped and is the focus of this paper. Minimal inconsistent sets have been the main tool to localise inconsistency, but we argue that they are like the exposed part of an iceberg, failing to capture contradictions hidden under the water. Using classical propositional logic, we develop methods to characterise when a formula is contributing to the inconsistency in a knowledge base and when a set of formulas can be regarded as a primitive conflict. To achieve this, we employ an abstract consequence operation to “look beneath the water level”, generalising the minimal inconsistent set concept and the related free formula notion. We apply the framework presented to the problem of measuring inconsistency in knowledge bases, putting forward relaxed forms for two debatable postulates for inconsistency measures. Finally, we discuss the computational complexity issues related to the introduced concepts.

JAIR Journal 2017 Journal Article

Probabilistic Reasoning with Abstract Argumentation Frameworks

• Anthony Hunter
• Matthias Thimm

Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning.

AAAI Conference 2017 Conference Paper

Strategic Sequences of Arguments for Persuasion Using Decision Trees

• Emmanuel Hadoux
• Anthony Hunter

Persuasion is an activity that involves one party (the persuader) trying to induce another party (the persuadee) to believe or do something. For this, it can be advantageous for the persuader to have a model of the persuadee. Recently, some proposals in the field of computational models of argument have been made for probabilistic models of what the persuadee knows about, or believes. However, these developments have not systematically harnessed established notions in decision theory for maximizing the outcome of a dialogue. To address this, we present a general framework for representing persuasion dialogues as a decision tree, and for using decision rules for selecting moves. Furthermore, we provide some empirical results showing how some well-known decision rules perform, and make observations about their general behaviour in the context of dialogues where there is uncertainty about the accuracy of the user model.

KR Conference 2016 Conference Paper

On Partial Information and Contradictions in Probabilistic Abstract Argumentation

• Anthony Hunter
• Matthias Thimm

We provide new insights into the area of combining abstract argumentation frameworks with probabilistic reasoning. In particular, we consider the scenario when assessments on the probabilities of a subset of the arguments is given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalize this scenario by also considering inconsistent assessments, i. e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistent-tolerant reasoning.

ECAI Conference 2016 Conference Paper

Two Dimensional Uncertainty in Persuadee Modelling in Argumentation

• Anthony Hunter

When attempting to persuade an agent to believe (or disbelieve) an argument, it can be advantageous for the persuader to have a model of the persuadee. Models have been proposed for taking account of what arguments the persuadee believes and these can be used in a strategy for persuasion. However, there can be uncertainty as to the accuracy of such models. To address this issue, this paper introduces a two-dimensional model that accounts for the uncertainty of belief by a persuadee and for the confidence in that uncertainty evaluation. This gives a better modeling for using lotteries so that the outcomes involve statements about what the user believes/disbelieves, and the confidence value is the degree to which the user does indeed hold those outcomes (and this is a more refined and more natural modeling than found in [19]). This framework is also extended with a modelling of the risk of disengagement by the persuadee.

IJCAI Conference 2015 Conference Paper

Modelling the Persuadee in Asymmetric Argumentation Dialogues for Persuasion

• Anthony Hunter

Computational models of argument could play a valuable role in persuasion technologies for behaviour change (e. g. persuading a user to eat a more healthy diet, or to drink less, or to take more exercise, or to study more conscientiously, etc). For this, the system (the persuader) could present arguments to convince the user (the persuadee). In this paper, we consider asymmetric dialogues where only the system presents arguments, and the system maintains a model of the user to determine the best choice of arguments to present (including counterarguments to key arguments believed to be held by the user). The focus of the paper is on the user model, including how we update it as the dialogue progresses, and how we use it to make optimal choices for dialogue moves.

IJCAI Conference 2015 Conference Paper

Optimization of Probabilistic Argumentation with Markov Decision Models

• Emmanuel Hadoux
• Aur
• eacute; lie Beynier
• Nicolas Maudet
• Paul Weng
• Anthony Hunter

One prominent way to deal with conflicting viewpoints among agents is to conduct an argumentative debate: by exchanging arguments, agents can seek to persuade each other. In this paper we investigate the problem, for an agent, of optimizing a sequence of moves to be put forward in a debate, against an opponent assumed to behave stochastically, and equipped with an unknown initial belief state. Despite the prohibitive number of states induced by a naive mapping to Markov models, we show that exploiting several features of such interaction settings allows for optimal resolution in practice, in particular: (1) as debates take place in a public space (or common ground), they can readily be modelled as Mixed Observability Markov Decision Processes, (2) as argumentation problems are highly structured, one can design optimization techniques to prune the initial instance. We report on the experimental evaluation of these techniques.

JELIA Conference 2014 Invited Paper

Opportunities for Argument-Centric Persuasion in Behaviour Change

• Anthony Hunter

Abstract The aim of behaviour change is to help people overcome specific behavioural problems in their everyday life (e. g. helping people to decrease their calorie intake). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e. g. through questionnaires or game playing) or to remember to follow a behaviour change plan (e. g. diaries and email reminders). So explicit argumentation with consideration of arguments and counterarguments are not supported with existing persuasion technologies. With recent developments in computational models of argument, there is the opportunity for argument-centric persuasion in behaviour change. In this paper, key requirements for this will be presented, together with some discussion of how computational models of argumentation can be harnessed.

ECAI Conference 2014 Conference Paper

Probabilistic Argumentation with Incomplete Information

• Anthony Hunter
• Matthias Thimm

We consider augmenting abstract argumentation frame-works with probabilistic information and discuss different constraints to obtain meaningful probabilistic information. Moreover, we investigate the problem of incomplete probability assignments and propose a solution for completing these assignments by applying the principle of maximum entropy.

AIIM Journal 2012 Journal Article

Aggregating evidence about the positive and negative effects of treatments

• Anthony Hunter
• Matthew Williams

Objectives Evidence-based decision making is becoming increasingly important in healthcare. Much valuable evidence is in the form of the results from clinical trials that compare the relative merits of treatments. In this paper, we present a new framework for representing and synthesizing knowledge from clinical trials involving multiple outcome indicators. Method The framework generates and evaluates arguments for claiming that one treatment is superior, or equivalent, to another based on the available evidence. Evidence comes from randomized clinical trials, systematic reviews, meta-analyses, network analyses, etc. Preference criteria over arguments are used that are based on the outcome indicators, and the magnitude of those outcome indicators, in the evidence. Meta-arguments attacks arguments that are based on weaker evidence. Results We evaluated the framework with respect to the aggregation of evidence undertaken in three published clinical guidelines that involve 56 items of evidence and 16 treatments. For each of the three guidelines, the treatment we identified as being superior using our method is a recommended treatment in the corresponding guideline. Conclusions The framework offers a formal approach to aggregating clinical evidence, taking into account subjective criteria such as preferences over outcome indicators. In the evaluation, the aggregations obtained showed a good correspondence with published clinical guidelines. Furthermore, preliminary computational studies indicate that the approach is viable for the size of evidence tables normally encountered in practice.

ECAI Conference 2012 Conference Paper

Executable Logic for Dialogical Argumentation

• Elizabeth Black
• Anthony Hunter

Argumentation between agents through dialogue is an important cognitive activity. There have been a number of proposals for formalizing dialogical argumentation. However, each proposal involves a number of quite complex definitions, and there is significant diversity in the way different proposals define similar features. This complexity and diversity has hindered analysis and comparison of the space of proposals. To address this, we present a general approach to defining a wide variety of systems for dialogical argumentation. Our solution is to use an executable logic to specify individual systems for dialogical argumentation. This means we have a common language for specifying a wide range of systems, we can compare systems in terms of a range of standard properties, we can identify interesting classes of system, and we can execute the specification of each system to analyse it empirically.

AIJ Journal 2011 Journal Article

Instantiating abstract argumentation with classical logic arguments: Postulates and properties

• Nikos Gorogiannis
• Anthony Hunter

In this paper we investigate the use of classical logic as a basis for instantiating abstract argumentation frameworks. In the first part, we propose desirable properties of attack relations in the form of postulates and classify several well-known attack relations from the literature with regards to the satisfaction of these postulates. Furthermore, we provide additional postulates that help us prove characterisation results for these attack relations. In the second part of the paper, we present postulates regarding the logical content of extensions of argument graphs that may be constructed with classical logic. We then conduct a comprehensive study of the status of these postulates in the context of the various combinations of attack relations and extension semantics.

IJCAI Conference 2011 Conference Paper

Measuring the Good and the Bad in Inconsistent Information

• John Grant
• Anthony Hunter

There is interest in artificial intelligence for principled techniques to analyze inconsistent information. This stems from the recognition that the dichotomy between consistent and inconsistent sets of formulae that comes from classical logics is not sufficient for describing inconsistent information. We review some existing proposals and make new proposals for measures of inconsistency and measures of information, and then prove that they are all pairwise incompatible. This shows that the notion of inconsistency is a multi-dimensional concept where different measures provide different insights. We then explore relationships between measures of inconsistency and measures of information in terms of the trade-offs they identify when using them to guide resolution of inconsistency.

AIJ Journal 2011 Journal Article

Weighted argument systems: Basic definitions, algorithms, and complexity results

• Paul E. Dunne
• Anthony Hunter
• Peter McBurney
• Simon Parsons
• Michael Wooldridge

We introduce and investigate a natural extension of Dung's well-known model of argument systems in which attacks are associated with a weight, indicating the relative strength of the attack. A key concept in our framework is the notion of an inconsistency budget, which characterises how much inconsistency we are prepared to tolerate: given an inconsistency budget β, we would be prepared to disregard attacks up to a total weight of β. The key advantage of this approach is that it permits a much finer grained level of analysis of argument systems than unweighted systems, and gives useful solutions when conventional (unweighted) argument systems have none. We begin by reviewing Dung's abstract argument systems, and motivating weights on attacks (as opposed to the alternative possibility, which is to attach weights to arguments). We then present the framework of weighted argument systems. We investigate solutions for weighted argument systems and the complexity of computing such solutions, focussing in particular on weighted variations of grounded extensions. Finally, we relate our work to the most relevant examples of argumentation frameworks that incorporate strengths.

KER Journal 2010 Journal Article

A survey of formalisms for representing and reasoning with scientific knowledge

• Anthony Hunter
• Weiru Liu

Abstract With the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

AAAI Conference 2010 Conference Paper

Inducing Probability Distributions from Knowledge Bases with (In)dependence Relations

• Jianbing Ma
• Weiru Liu
• Anthony Hunter

When merging belief sets from different agents, the result is normally a consistent belief set in which the inconsistency between the original sources is not represented. As probability theory is widely used to represent uncertainty, an interesting question therefore is whether it is possible to induce a probability distribution when merging belief sets. To this end, we first propose two approaches to inducing a probability distribution on a set of possible worlds, by extending the principle of indifference on possible worlds. We then study how the (in)dependence relations between atoms can influence the probability distribution. We also propose a set of properties to regulate the merging of belief sets when a probability distribution is output. Furthermore, our merging operators satisfy the well known Konieczny and Pino-Pérez postulates if we use the set of possible worlds which have the maximal induced probability values. Our study shows that taking an induced probability distribution as a merging result can better reflect uncertainty and inconsistency among the original knowledge bases.

AIJ Journal 2010 Journal Article

On the measure of conflicts: Shapley Inconsistency Values

• Anthony Hunter
• Sébastien Konieczny

There are relatively few proposals for inconsistency measures for propositional belief bases. However inconsistency measures are potentially as important as information measures for artificial intelligence, and more generally for computer science. In particular, they can be useful to define various operators for belief revision, belief merging, and negotiation. The measures that have been proposed so far can be split into two classes. The first class of measures takes into account the number of formulae required to produce an inconsistency: the more formulae required to produce an inconsistency, the less inconsistent the base. The second class takes into account the proportion of the language that is affected by the inconsistency: the more propositional variables affected, the more inconsistent the base. Both approaches are sensible, but there is no proposal for combining them. We address this need in this paper: our proposal takes into account both the number of variables affected by the inconsistency and the distribution of the inconsistency among the formulae of the base. Our idea is to use existing inconsistency measures in order to define a game in coalitional form, and then to use the Shapley value to obtain an inconsistency measure that indicates the responsibility/contribution of each formula to the overall inconsistency in the base. This allows us to provide a more reliable image of the belief base and of the inconsistency in it.

AIJ Journal 2009 Journal Article

Encoding deductive argumentation in quantified Boolean formulae

• Philippe Besnard
• Anthony Hunter
• Stefan Woltran

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair 〈 Φ, α 〉 where Φ is minimal subset of the knowledge-base such that Φ is consistent and Φ entails the claim α. Different logics provide different definitions for consistency and entailment and hence give us different options for argumentation. Classical propositional logic is an appealing option for argumentation but the computational viability of generating an argument is an issue. To better explore this issue, we use quantified Boolean formulae to characterise an approach to argumentation based on classical logic.

AAMAS Conference 2009 Conference Paper

Inconsistency Tolerance in Weighted Argument Systems

• Paul E. Dunne
• Anthony Hunter
• Peter McBurney
• Simon Parsons
• Michael Wooldridge

We introduce and investigate a natural extension of Dung’s wellknown model of argument systems in which attacks are associated with a weight, indicating the relative strength of the attack. A key concept in our framework is the notion of an inconsistency budget, which characterises how much inconsistency we are prepared to tolerate: given an inconsistency budget β, we would be prepared to disregard attacks up to a total cost of β. The key advantage of this approach is that it permits a much finer grained level of analysis of argument systems than unweighted systems, and gives useful solutions when conventional (unweighted) argument systems have none. We begin by reviewing Dung’s abstract argument systems, and present the model of weighted argument systems. We then investigate solutions to weighted argument systems and the associated complexity of computing these solutions, focussing in particular on weighted variations of grounded extensions.

JAAMAS Journal 2008 Journal Article

An inquiry dialogue system

• Elizabeth Black
• Anthony Hunter

Abstract The majority of existing work on agent dialogues considers negotiation, persuasion or deliberation dialogues; we focus on inquiry dialogues, which allow agents to collaborate in order to find new knowledge. We present a general framework for representing dialogues and give the details necessary to generate two subtypes of inquiry dialogue that we define: argument inquiry dialogues allow two agents to share knowledge to jointly construct arguments; warrant inquiry dialogues allow two agents to share knowledge to jointly construct dialectical trees (essentially a tree with an argument at each node in which a child node is a counter argument to its parent). Existing inquiry dialogue systems only model dialogues, meaning they provide a protocol which dictates what the possible legal next moves are but not which of these moves to make. Our system not only includes a dialogue-game style protocol for each subtype of inquiry dialogue that we present, but also a strategy that selects exactly one of the legal moves to make. We propose a benchmark against which we compare our dialogues, being the arguments that can be constructed from the union of the agents’ beliefs, and use this to define soundness and completeness properties that we show hold for all inquiry dialogues generated by our system.

AIJ Journal 2008 Journal Article

Analysing inconsistent first-order knowledgebases

• John Grant
• Anthony Hunter

It is well-known that knowledgebases may contain inconsistencies. We provide a framework of measures, based on a first-order four-valued logic, to quantify the inconsistency of a knowledgebase. This allows for the comparison of the inconsistency of diverse knowledgebases that have been represented as sets of first-order logic formulae. We motivate the approach by considering some examples of knowledgebases for representing and reasoning with ontological knowledge and with temporal knowledge. Analysing ontological knowledge (including the statements about which concepts are subconcepts of other concepts, and which concepts are disjoint) can be problematical when there is a lack of knowledge about the instances that may populate the concepts, and analysing temporal knowledge (such as temporal integrity constraints) can be problematical when considering infinite linear time lines isomorphic to the natural numbers or the real numbers or more complex structures such as branching time lines. We address these difficulties by providing algebraic measures of inconsistency in first-order knowledgebases.

KR Conference 2008 Conference Paper

Measuring Inconsistency through Minimal Inconsistent Sets

• Anthony Hunter
• Sébastien Konieczny

In this paper, we explore the links between measures of inconsistency for a belief base and the minimal inconsistent subsets of that belief base. The minimal inconsistent subsets can be considered as the relevant part of the base to take into account to evaluate the amount of inconsistency. We define a very natural inconsistency value from these minimal inconsistent sets. Then we show that the inconsistency value we obtain is a particular Shapley Inconsistency Value, and we provide a complete axiomatization of this value in terms of five simple and intuitive axioms. Defining this Shapley Inconsistency Value using the notion of minimal inconsistent subsets allows us to look forward to a viable implementation of this value using SAT solvers.

AAAI Conference 2008 Conference Paper

Reasoning about the Appropriateness of Proponents for Arguments

• Anthony Hunter

Formal approaches to modelling argumentation provide ways to present arguments and counterarguments, and to evaluate which arguments are, in a formal sense, warranted. While these proposals allow for evaluating object-level arguments and counterarguments, they do not give sufficient consideration to evaluating the proponents of the arguments. Yet in everyday life we consider both the contents of an argument and its proponent. So if we do not trust a proponent, we may choose to not trust their arguments. Or if we are faced with an argument that we do not have the expertise to assess (for example when deciding whether to agree to having a particular surgical operation), we tend to agree to an argument by someone who is an expert. In general, we see that for each argument, we need to determine the appropriateness of the proponent for it. So for an argument about our health, our doctor is normally an appropriate proponent, but for an argument about our investments, our doctor is normally not an appropriate proponent. In this way, a celebrity is rarely an appropriate proponent for an argument, and a liar is not necessarily an inappropriate proponent for an argument. In this paper, we provide a logic-based framework for evaluating arguments in terms of the appropriateness of the proponents.

AAMAS Conference 2008 Conference Paper

Using Enthymemes in an Inquiry Dialogue System

• Elizabeth Black
• Anthony Hunter

A common assumption for logic-based argumentation is that an argument is a pair hΦ, αi where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i. e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim (i. e. they are enthymemes). This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into the intended argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and this raises interesting issues for dialogue systems in which the recipient has to cope with the disparities between the different views on what constitutes common knowledge. Here we investigate the use of enthymemes in inquiry dialogues. For this, we propose a generative inquiry dialogue system and show how, in this dialogue system, enthymemes can be managed by the agents involved, and how common knowledge can evolve through dialogue.

AAMAS Conference 2007 Conference Paper

A Generative Inquiry Dialogue System

• Elizabeth Black
• Anthony Hunter

The majority of existing work on agent dialogues considers negotiation, persuasion or deliberation dialogues. We focus on inquiry dialogues that allow two agents to share knowledge in order to construct an argument for a specific claim. Inquiry dialogues are particularly useful in cooperative domains such as healthcare, and can be embedded within other dialogue types. Existing inquiry dialogue systems only model dialogues, meaning they provide a protocol which dictates what the possible legal next moves are but not which of these moves to make. Our system not only includes a general dialogue-game style inquiry protocol but also a strategy, for an agent to use with this protocol, that selects exactly one of the legal moves to make. We propose a benchmark against which we compare our dialogues, being the arguments that can be constructed from the union of the agents' beliefs, and use this to define soundness and completeness properties for inquiry dialogues. We show that these properties hold for all well-formed inquiry dialogues in our system.

AAAI Conference 2007 Conference Paper

Real Arguments Are Approximate Arguments

• Anthony Hunter

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair Φ, α where Φ is minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. However, real arguments (i. e. arguments presented by humans) usually do not have enough explicitly presented premises for the entailment of the claim. This is because there is some common knowledge that can be assumed by a proponent of an argument and the recipient of it. This allows the proponent of an argument to encode an argument into a real argument by ignoring the common knowledge, and it allows a recipient of a real argument to decode it into an argument by drawing on the common knowledge. If both the proponent and recipient use the same common knowledge, then this process is straightforward. Unfortunately, this is not always the case, and raises the need for an approximation of the notion of an argument for the recipient to cope with the disparities between the different views on what constitutes common knowledge.

ECAI Conference 2006 Conference Paper

Contouring of Knowledge for Intelligent Searching for Arguments

• Anthony Hunter

A common assumption for logic-based argumentation is that an argument is a pair 〈Φ, α〉 where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. Different logics are based on different definitions for entailment and consistency, and these give us different options for argumentation. For a variety of logics, in particular for classical logic, there is a need to develop intelligent techniques for generating arguments. Since building a constellation of arguments and counterarguments involves repeatedly querying a knowledgebase, we propose a framework based on what we call “contours” for storing information about a knowledgebase that provides boundaries on what is provable in the knowledgebase. Using contours allows for more intelligent searching of a knowledgebase for arguments and counterarguments.

KR Conference 2006 Conference Paper

Knowledgebase compilation for efficient logical argumentation

• Philippe Besnard
• Anthony Hunter

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair (X, p) where X is minimal subset of the knowledgebase such that X is consistent and X entails the claim p. Different logics are based on different definitions for entailment and consistency, and give us different options for argumentation. For a variety of logics, in particular for classical logic, the computational viability of generating arguments is an issue. Here, we present a solution that involves compiling a knowledgebase K based on the set of minimal inconsistent subsets of K, and then generating arguments from the compilation. Whilst generating a compilation is expensive, generating arguments from a compilation is relatively inexpensive.

KR Conference 2006 Conference Paper

Shapley Inconsistency Values

• Anthony Hunter
• S�bastien Konieczny

There are relatively few proposals for inconsistency measures for propositional belief bases. However inconsistency measures are potentially as important as information measures for artificial intelligence, and more generally for computer science. In particular, they can be useful to define various operators for belief revision, belief merging, and negotiation. The measures that have been proposed so far can be split into two classes. The first class of measures takes into account the number of formulae required to produce an inconsistency: the more formulae required to produce an inconsistency, the less inconsistent the base. The second class takes into account the proportion of the language that is affected by the inconsistency: the more propositional variables affected, the more inconsistent the base. Both approaches are sensible, but there is no proposal for combining them. We address this need in this paper: our proposal takes into account both the number of variables affected by the inconsistency and the distribution of the inconsistency among the formulae of the base. Our idea is to use existing inconsistency measures (ones that takes into account the proportion of the language affected by the inconsistency, and so allow us to look inside the formulae) in order to define a game in coalitional form, and then to use the Shapley value to obtain an inconsistency measure that indicates the responsibility/contribution of each formula to the overall inconsistency in the base. This allows us to provide a more reliable image of the belief base and of the inconsistency in it.

AAAI Conference 2004 Conference Paper

Making Argumentation More Believable

• Anthony Hunter

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A problem with these proposals is that they do not consider the believability of the arguments from the perspective of the intended audience. In this paper, we start by reviewing a logic-based framework for argumentation based on argument trees which provide a way of exhaustively collating arguments and counter-arguments. We then extend this framework to a model-theoretic evaluation of the believability of arguments. This extension assumes that the beliefs of a typical member of the audience for argumentation can be represented by a set of classical formulae (a beliefbase). We compare a beliefbase with each argument to evaluate the empathy (or similarly the antipathy) that an agent has for the argument. We show how we can use empathy and antipathy to define a pre-ordering relation over argument trees that captures how one argument tree is “more believable” than another. We also use these to define criteria for deciding whether an argument at the root of an argument tree is defeated or undefeated given the other arguments in the tree.

AAAI Conference 2004 Conference Paper

Towards Higher Impact Argumentation

• Anthony Hunter

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. An example is the framework by Besnard and Hunter that is based on classical logic and in which an argument (obtained from a knowledgebase) is a pair where the first item is a minimal consistent set of formulae that proves the second item (which is a formula). In the framework, the only counter-arguments (defeaters) that need to be taken into account are canonical arguments (a form of minimal undercut). Argument trees then provide a way of exhaustively collating arguments and counter-arguments. A problem with this set up is that some argument trees may be “too big” to have sufficient impact. In this paper, we address the need to increase the impact of argumentation by using pruned argument trees. We formalize this in terms of how arguments resonate with the intended audience of the arguments. For example, if a politician wants to make a case for raising taxes, the arguments used would depend on what is important to the audience: Arguments based on increased taxes are needed to pay for improved healthcare would resonate better with an audience of pensioners, whereas arguments based on increased taxes are needed to pay for improved transport infrastructure would resonate better with an audience of business executives. By analysing the resonance of arguments, we can prune argument trees to raise their impact.

NMR Workshop 2004 Conference Paper

Towards higher impact argumentation

• Anthony Hunter

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. An example is the framework by Besnard and Hunter that is based on classical logic and in which an argument (obtained from a knowledgebase) is a pair where the first item is a minimal consistent set of formulae that proves the second item (which is a formula). In the framework, the only counter-arguments (defeaters) that need to be taken into account are canonical arguments (a form of minimal undercut). Argument trees then provide a way of exhaustively collating arguments and counter-arguments. A problem with this set up is that some argument trees may be “too big” to have sufficient impact. In this paper, we address the need to increase the impact of argumentation by using pruned argument trees. We formalize this in terms of how arguments resonate with the intended audience of the arguments. For example, if a politician wants to make a case for raising taxes, the arguments used would depend on what is important to the audience: Arguments based on increased taxes are needed to pay for improved healthcare would resonate better with an audience of pensioners, whereas arguments based on increased taxes are needed to pay for improved transport infrastructure would resonate better with an audience of business executives. By analysing the resonance of arguments, we can prune argument trees to raise their impact.

IJCAI Conference 2003 Conference Paper

Evaluating Significance of Inconsistencies

• Anthony Hunter

Inconsistencies frequently occur in knowledge about the real-world. Some of these inconsistencies may be more significant than others, and some knowledgebases (sets of formulae) may contain more inconsistencies than others. This creates problems of deciding whether to act on these inconsistencies, and if so how. To address this, we provide a general characterization of inconsistency, based on quasi-classical logic (a form of paraconsistent logic with a more expressive semantics than Belnap's four-valued logic, and unlike other paraconsistent logics, allows the connectives to appear to behave as classical connectives). We analyse inconsistent knowledge by considering the conflicts arising in the minimal quasi-classical models for that knowledge. This is used for a measure of coherence for each knowledgebase, and for a measure of significance of inconsistencies in each knowledgebase. In this paper, we formalize this framework, and consider applications in managing heterogeneous sources of knowledge.

AAAI Conference 2002 Conference Paper

Measuring Inconsistency in Knowledge via Quasi-Classical Models

• Anthony Hunter

The language for describing inconsistency is underdeveloped. If a knowledgebase (a set of formulae) is inconsistent, we need more illuminating ways to say how inconsistent it is, or to say whether one knowledgebase is “more inconsistent” than another. To address this, we provide a general characterization of inconsistency, based on quasi-classical logic (a form of paraconsistent logic with a more expressive semantics than Belnap’s four-valued logic, and unlike other paraconsistent logics, allows the connectives to appear to behave as classical connectives). We analyse inconsistent knowledge by considering the conflicts arising in the minimal quasi-classical models for that knowledge. This is used for a measure of coherence for each knowledgebase, and for a preference ordering, called the compromise relation, over knowledgebases. In this paper, we formalize this framework, and consider applications in managing heterogeneous sources of knowledge.

KER Journal 2001 Journal Article

Hybrid argumentation systems for structured news reports

• Anthony Hunter

Numerous argumentation systems have been proposed in the literature. Yet there often appears to be a shortfall between proposed systems and possible applications. In other words, there seems to be a need for further development of proposals for argumentation systems before they can be used widely in decision-support or knowledge management. I believe that this shortfall can be bridged by taking a hybrid approach. Whilst formal foundations are vital, systems that incorporate some of the practical ideas found in some of the informal approaches may make the resulting hybrid systems more useful. In informal approaches, there is often an emphasis on using graphical notation with symbols that relate more closely to the real-world concepts to be modelled. There may also be the incorporation of an argument ontology oriented to the user domain. Furthermore, in informal approaches there can be greater consideration of how users interact with the models, such as allowing users to edit arguments and to weight influences on graphs representing arguments. In this paper, I discuss some of the features of argumentation, review some key formal argumentation systems, identify some of the strengths and weaknesses of these formal proposals and finally consider some ways to develop formal proposals to give hybrid argumentation systems. To focus my discussions, I will consider some applications, in particular an application in analysing structured news reports.

KER Journal 2000 Journal Article

Reasoning with inconsistency in structured text

• Anthony Hunter

Reasoning with inconsistency involves some compromise on classical logic. There is a range of proposals for logics (called paraconsistent logics) for reasoning with inconsistency each with pros and cons. Selecting an appropriate paraconsistent logic for an application depends upon the requirements of the application. Here we review paraconsistent logics for the potentially significant application area of technology for structured text. Structured text is a general concept that is implicit in a variety of approaches to handling information. Syntactically, an item of structured text is a number of grammatically simple phrases together with a semantic label for each phrase. Items of structured text may be nested within larger items of structured text. The semantic labels in a structured text are meant to parameterize a stereotypical situation, and so a particular item of structured text is an instance of that stereotypical situation. Much information is potentially available as structured text, including tagged text in XML, text in relational and object-oriented databases, and the output from information extraction systems in the form of instantiated templates. In this review paper, we formalize the concept of structured text, and then focus on how we can identify inconsistency in items of structured text, and reason with these inconsistencies. Then we review key approaches to paraconsistent reasoning, and discuss the application of them to reasoning with inconsistency in structured text.

KER Journal 1998 Journal Article

Non-monotonic reasoning by Grigoris Antoniou, MIT Press, 1997, £29.50, ISBN 0-262-01157-3.

• Anthony Hunter