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Aggregating evidence about the positive and negative effects of treatments

ARTIFICIAL INTELLIGENCE IN MEDICINE 2012年第3期56卷 173-190页

作者： Hunter, Anthony Williams, Matthew UCL Dept Comp Sci London WC1E 6BT England Univ Coll Hosp Dept Clin Oncol London NW1 2PG England

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. (c) 2012 Elsevier B.V. All rights reserved.

关键词： computational models of argument argument systems Knowledge aggregation Evidence aggregation Evidence-based medicine Clinical recommendations

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A probabilistic approach to modelling uncertain logical arguments

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2013年第1期54卷 47-81页

作者： Hunter, Anthony UCL Dept Comp Sci London WC1E 6BT England

argumentation can be modelled at an abstract level using a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another. Since arguments are often uncertain, it can be useful to quantify the uncertainty associated with each argument. Recently, there have been proposals to extend abstract argumentation to take this' uncertainty into account. This assigns a probability value for each argument that represents the degree to which the argument is believed to hold, and this is then used to generate a probability distribution over the full subgraphs of the argument graph, which in turn can be used to determine the probability that a set of arguments is admissible or an extension. In order to more fully understand uncertainty in argumentation, in this paper, we extend this idea by considering logic-based argumentation with uncertain arguments. This is based on a probability distribution over models of the language, which can then be used to give a probability distribution over arguments that are constructed using classical logic. We show how this formalization of uncertainty of logical arguments relates to uncertainty of abstract arguments, and we consider a number of interesting classes of probability assignments. (C) 2012 Elsevier Inc. All rights reserved.

关键词： computational models of argument argument systems Logic-based argumentation Probabilistic argumentation Logical arguments Uncertain arguments

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Instantiating abstract argumentation with classical logic arguments: Postulates and properties

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ARTIFICIAL INTELLIGENCE 2011年第9-10期175卷 1479-1497页

作者： Gorogiannis, Nikos Hunter, Anthony UCL Dept Comp Sci London WC1E 6BT England Univ London Dept Comp Sci London E1 4NS England

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. (C) 2011 Elsevier B.V. All rights reserved.

关键词： computational models of argument Abstract argumentation Logical argumentation

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Probabilistic qualification of attack in abstract argumentation

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2014年第2期55卷 607-638页

作者： Hunter, Anthony UCL Dept Comp Sci London WC1E 6BT England

An argument graph is a graph where each node denotes an argument, and each arc denotes an attack by one argument on another. It offers a valuable starting point for theoretical analysis of argumentation following the proposals by Dung. However, the definition of an argument graph does not take into account the belief in the attacks. In particular, when constructing an argument graph from informal arguments, where each argument is described in free text, it is often evident that there is uncertainty about whether some of the attacks hold. This might be because there is some expressed doubt that an attack holds or because there is some imprecision in the language used in the arguments. In this paper, we use the set of spanning subgraphs of an argument graph as a sample space. A spanning subgraph contains all the arguments, and a subset of the attacks, of the argument graph. We assign a probability value to each spanning subgraph such that the sum of the assignments is 1. This means we can reflect the uncertainty over which is the actual subgraph using this probability distribution. Using the probability distribution over subgraphs, we can then determine the probability that a set of arguments is admissible or an extension. We can also obtain the probability of an attack relationship in the original argument graph as a marginal distribution (i.e. it is the sum of the probability assigned to each subgraph containing that attack relationship). We investigate some of the features of this proposal, and we consider the utility of our framework for capturing some practical argumentation scenarios. (C) 2013 Elsevier Inc. All rights reserved.

关键词： computational models of argument argument systems Abstract argumentation Probabilistic argumentation Uncertain argumentation Enthymemes

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Algorithms for generating arguments and counterarguments in propositional logic

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2011年第6期52卷 672-704页

作者： Efstathiou, Vasiliki Hunter, Anthony UCL Dept Comp Sci London WC1E 6BT England

A common assumption for logic-based argumentation is that an argument is a pair (Phi, alpha) where Phi is minimal subset of the knowledgebase such that Phi is consistent and Phi entails the claim alpha. Different logics provide different definitions for consistency and entailment and hence give us different options for formalising arguments and counterarguments. The expressivity of classical propositional logic allows for complicated knowledge to be represented but its computational cost is an issue. In previous work we have proposed addressing this problem using connection graphs and resolution in order to generate arguments for claims that are literals. Here we propose a development of this work to generate arguments for claims that are disjunctive clauses of more than one disjunct, and also to generate counteraguments in the form of canonical undercuts (i.e. arguments that with a claim that is the negation of the conjunction of the support of the argument being undercut). (C) 2011 Elsevier Inc. All rights reserved.

关键词： Logic-based argumentation computational models of argument

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Towards a framework for computational persuasion with applications in behaviour change

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argument & COMPUTATION 2018年第1期9卷 15-40页

作者： Hunter, Anthony UCL Dept Comp Sci Gower St London WC1E 6BT England

Persuasion is an activity that involves one party trying to induce another party to believe something or to do something. It is an important and multifaceted human facility. Obviously, sales and marketing is heavily dependent on persuasion. But many other activities involve persuasion such as a doctor persuading a patient to drink less alcohol, a road safety expert persuading drivers to not text while driving, or an online safety expert persuading users of social media sites to not reveal too much personal information online. As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. An automated persuasion system (APS) is a system that can engage in a dialogue with a user (the persuadee) in order to persuade the persuadee to do (or not do) some action or to believe (or not believe) something. To do this, an APS aims to use convincing arguments in order to persuade the persuadee. computational persuasion is the study of formal models of dialogues involving arguments and counterarguments, of user models, and strategies, for APSs. A promising application area for computational persuasion is in behaviour change. Within healthcare organizations, government agencies, and non-governmental agencies, there is much interest in changing behaviour of particular groups of people away from actions that are harmful to themselves and/or to others around them.

关键词： computational persuasion persuasion dialogues persuasive arguments dialogical argumentation computational models of argument probabilistic argumentation argumentation strategies

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Semi-Supervised Methods for Explainable Legal Prediction 19

Semi-Supervised Methods for Explainable Legal Prediction

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17th International Conference on Artificial Intelligence and Law (ICAIL)

作者： Branting, K. Weiss, B. Brown, B. Pfeifer, C. Chakraborty, A. Ferro, L. Pfaff, M. Yeh, A. MITRE Corp Mclean VA 22102 USA MITRE Corp Ann Arbor MI USA MITRE Corp Burlington Rd Bedford MA 01730 USA

ISBN: (纸本)9781450367547

Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support-explainable outcome prediction-that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an Attention Network for prediction and attention weights to highlight salient case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed SCALE (Semi-supervised Case Annotation for Legal Explanations), exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value.

关键词： Artificial Intelligence & Law Legal Reasoning computational models of argument Machine Learning Human Language Technology

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Answer Set Programming for Legal Decision Support and Explanation 36

Answer Set Programming for Legal Decision Support and Explan...

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36th Annual International Conference on Legal Knowledge and Information Systems (JURIX)

作者： Dupre, Daniele Theseider Univ Piemonte Orientale DISIT Novara Italy

ISBN: (纸本)9781643684727;9781643684734

The ANGELIC methodology was successfully used to predict decisions of the European Court of Human Rights based on a set of logical rules, with significantly better accuracy than the one achieved by machine learning approaches, as well as to explain the results of reasoning, quite valuable in order to make them trustworthy. This work demonstrates a different logic-based approach, based on Answer Set Programming for solving and generating explanations for solutions. The use of a general knowledge representation and reasoning system, where representation and inference are not tightly coupled, allows for using the same representation for inference tasks different from prediction, thus getting more value out of the domain model, and opens for integrating further forms of knowledge.

关键词： Answer Set Programming Reasoning with Legal Cases computational models of argument Explanation

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computational Persuasion with Applications in Behaviour Change 6

Computational Persuasion with Applications in Behaviour Chan...

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6th International Conference on computational models of argument (COMMA)

作者： Hunter, Anthony UCL Dept Comp Sci Gower St London WC1E 6BT England

ISBN: (纸本)9781614996866

关键词： computational persuasion Persuasion dialogues Persuasive arguments Dialogical argumentation computational models of argument Probabilistic argumentation argumentation strategies

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An argumentative Characterization of Disjunctive Logic Programming 19th

An Argumentative Characterization of Disjunctive Logic Progr...

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19th EPIA Conference on Artificial Intelligence (EPIA)

作者： Heyninck, Jesse Arieli, Ofer Ruhr Univ Bochum Inst Philosophy 2 Bochum Germany Acad Coll Tel Aviv Sch Comp Sci Tel Aviv Israel

ISBN: (纸本)9783030302443;9783030302436

This paper extends the result of Caminada and Schulz [6,7] by showing that assumption-based argumentation can represent not only normal logic programs, but also disjunctive logic programs. For this, we incorporate a previous work of ours (see [19,20]), in which reasoning with assumption-based argumentation frameworks is based on certain core logics and the strict/defeasible assumptions may be arbitrary formulas in those logics. In our case, the core logic respects some inference rules for disjunction, which allows disjunctions in the heads of the programs' rules to be handled properly.

关键词： Knowledge representation and reasoning Non-monotonic reasoning computational models of argument Disjunctive logic programming Structured argumentation

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