Publications
426 results found
Kakas A, Michael L, Toni F, 2016, Argumentation: Reconciling Human and Automated Reasoning, Bridging the Gap between Human and Automated Reasoning 2016, Publisher: CEUR-WS, Pages: 43-60, ISSN: 1613-0073
We study how using argumentation as an alternative foundation for logic gives a framework in which we can reconcile human and automated reasoning. We analyse this reconciliation between human and automated reasoning at three levels: (1) at the level of classical, strict reasoning on which, till today, automated reasoning and computing are based, (2) at the level of natural or ordinary human level reasoning as studied in cognitive psychology and which artificial intelligence, albeit in its early stages, is endeavouring to automate, and (3) at the level of the recently emerged cognitive computing paradigm where systems are required to be cognitively compatible with human reasoning based on common sense or expert knowledge, machine-learned from unstructured data in corpora over the web or other sources.
Fan X, Toni F, 2016, On the interplay between games, argumentation and dialogues, 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016), Publisher: ACM, Pages: 260-268
Game theory, argumentation and dialogues all address problemsconcerning inter-agent interaction, but from different perspectives.In this paper, we contribute to the study of the interplay betweenthese fields. In particular, we show that by mapping games in normalform into structured argumentation, computing dominant solutionsand Nash equilibria is equivalent to computing admissiblesets of arguments. Moreover, when agents lack complete information,computing dominant solutions/Nash equilibria is equivalentto constructing successful (argumentation-based) dialogues.Finally, we study agents’ behaviour in these dialogues in reversegame-theoretic terms and show that, using specific notions of utility,agents engaged in (argumentation-based) dialogues are guaranteedto be truthful and disclose relevant information, and thuscan converge to dominant solutions/Nash equilibria of the originalgames even under incomplete information.
Gao Y, Toni F, Wang H, et al., 2016, Argumentation-based multi-agent decision making with privacy preserved, 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS 2016), Publisher: ACM, Pages: 1153-1161
We consider multi-agent decision making problems in whichagents need to communicate with other agents to make sociallyoptimal decisions but, at the same time, have someprivate information that they do not want to share. Abstractargumentation has been widely used in both single-agent andmulti-agent decision making problems, because of its abilityfor reasoning with incomplete and conflicting information.In this work, we propose an abstract argumentation-basedknowledge representation and communication protocol, suchthat agents can find socially optimal strategies by only disclosingthe ‘necessary’ and ‘disclosable’ information. Weprove that our protocol is sound, efficient, of perfect informationsecurity and guaranteed to terminate.
Rago A, Toni F, Aurisicchio M, et al., 2016, Discontinuity-free decision support with quantitative argumentation debates, Fifteenth International Conference, KR 2016, Publisher: AAAI
IBIS (Issue Based Information System) provides a widelyadopted approach for knowledge representation especiallysuitable for the challenging task of representing wicked decisionproblems. While many tools for visualisation and collaborativedevelopment of IBIS graphs are available, automateddecision support in this context is still underdeveloped, eventhough it would benefit several applications. QuAD (QuantitativeArgumentation Debate) frameworks are a recently proposedIBIS-based formalism encompassing automated decisionsupport by means of an algorithm for quantifying thestrength of alternative decision options, based on aggregationof the strength of their attacking and supporting arguments.The initially proposed aggregation method, however, maygive rise to discontinuities. In this paper we propose a novel,discontinuity-free algorithm for computing the strength of decisionoptions in QuAD frameworks. We prove that this algorithmfeatures several desirable properties and we comparethe two aggregation methods, showing that both may be appropriatein the context of different application scenarios.
Kontarinis D, Toni F, 2016, Identifying malicious behavior in multi-party bipolar argumentation debates, 13th European Conference, EUMAS 2015, and Third International Conference, AT 2015, Publisher: Springer, Pages: 267-278, ISSN: 0302-9743
Lately, several works have analyzed potential uses of argumentation in multi-party debates. Usually, the focus of such works is the computation of a collectively “correct” outcome, a challenging task even when the debate’s users truthfully express their beliefs. This work focuses on debates where some users may exhibit specific types of “malicious” behavior: they may lie (bymaking statements they do not believe to hold) and they may hide valuable information (by not making relevant statements they believe to hold). Our approach is the following: firstly, we define “user attributes” which capture different aspects of a user’s behavior in a debate (how active, how opinionated and how classifiable a user has been); then, we build and test experimentally hypotheses that, from the values of these attributes, can predict whether a user has lied and/or hidden valuable information.
Toni F, Craven R, 2016, Argument graphs and assumption-based argumentation
Arguments in structured argumentation are usually defined as trees, and extensions as sets of such tree-based arguments with various properties depending on the particular argumentation semantics. However, these arguments and extensions may have redundancies as well as circularities, which are conceptually and computationally undesirable. Focusing on the specific case of Assumption-Based Argumentation (ABA), we propose novel notions of arguments and admissible/grounded extensions, both defined in terms of graphs. We show that this avoids the redundancies and circularities of standard accounts, and set out the relationship to standard tree-based arguments and admissible/grounded extensions (as sets of arguments). We also define new notions of graph-based admissible/grounded dispute derivations for ABA, for determining whether specific sentences hold under the admissible/grounded semantics. We show that these new derivations are superior with respect to standard dispute derivations in that they are complete in general, rather than solely for restricted classes of ABA frameworks. Finally, we present several experiments comparing the implementation of graph-based admissible/grounded dispute derivations with implementations of standard dispute derivations, suggesting that the graph-based approach is computationally advantageous.
Čyras K, Toni F, 2016, ABA<sup>+</sup>: Assumption-based argumentation with preferences, Principles of Knowledge Representation and Reasoning: Proceedings of the 15th International Conference, KR 2016, Publisher: AAAI Press, Pages: 553-556
Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We present a novel approach to account for preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA). The new formalism, called ABA + , incorporates object-level preferences (over assumptions) directly into the attack relation to reverse attacks. We give several basic desirable properties of ABA + .
Cyras K, Toni F, 2016, Properties of ABA+ for Non-Monotonic Reasoning, 16th International Workshop on Non-Monotonic Reasoning (NMR'16)
We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and non-monotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.
Cyras K, Satoh K, Toni F, 2016, Abstract Argumentation for Case-Based Reasoning., Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning (KR 2016), Publisher: AAAI Press, Pages: 549-552
We investigate case-based reasoning (CBR) problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. To this end, we employ abstract argumentation (AA) and propose a novel methodology for CBR, called AA-CBR. The argumentative formulation naturally allows to characterise the computation of an outcome as a dialogical process between a proponent and an opponent, and can also be used to extract explanations for why an outcome for a new case is (not) computed.
Makriyiannis M, Lung T, Craven R, et al., 2016, Smarter electricity and argumentation theory, 4th International Workshop, CIMA 2014, Publisher: Springer, Pages: 79-95, ISSN: 2190-3018
The current, widespread introduction of smart electricity meters is resulting in large datasets’ becoming available, but there is as yet little in the way of advanced data analytics and visualization tools, or recommendation software for changes in contracts or user behaviour, which use this data. In this paper we present an integrated tool which combines the use of abstract argumentation theory with linear optimization algorithms, to achieve some of these ends.
Mocanu A, Fan X, Toni F, et al., 2016, Online argumentation-based platform for recommending medical literature, 4th International Workshop, CIMA 2014, Publisher: Springer, Pages: 97-115, ISSN: 2190-3018
In medical practice, choosing the correct treatment is a key problem [1]. In this work, we present an online medical recommendation system, RecoMedic, that selects most relevant medical literature for patients. RecoMedic maintains a medical literature repository in which users can add new articles, query existing articles, compare articles and search articles guided by patient information. RecoMedic uses argumentation to accomplish the article selection. Thus, upon identifying relevant articles, RecoMedic also explains its selection. RecoMedic can be deployed using single-agent as well as multi-agent implementations. The developed system has been experimented with by senior medical Ph.D students from SouthernMedical University in China.
Čyras K, Toni F, 2016, Non-monotonic inference properties for assumption-based argumentation, Third International Workshop, TAFA 2015, Publisher: Springer, Pages: 92-111, ISSN: 0302-9743
Cumulative Transitivity and Cautious Monotonicity are widely considered as important properties of non-monotonic inference and equally as regards to information change. We propose three novel formulations of each of these properties for Assumption-Based Argumentation (ABA)-an established structured argumentation formalism, and investigate these properties under a variety of ABA semantics.
Schulz C, Toni F, 2016, Justifying answer sets using argumentation., Theory Pract. Log. Program., Vol: 16, Pages: 59-110
Craven R, Toni F, 2015, Argument Graphs and Assumption-Based Argumentation, Artificial Intelligence, Vol: 233, Pages: 1-59, ISSN: 1872-7921
Arguments in structured argumentation are usually defined as trees, and extensions as sets of such tree-based arguments with various properties depending on the particular argumentation semantics. However, these arguments and extensions may have redundancies as well as circularities, which are conceptually and computationally undesirable. Focusing on the specific case of Assumption-Based Argumentation (ABA), we propose novel notions of arguments and admissible/grounded extensions, both defined in terms of graphs. We show that this avoids the redundancies and circularities of standard accounts, and set out the relationship to standard tree-based arguments and admissible/grounded extensions (as sets of arguments). We also define new notions of graph-based admissible/grounded dispute derivations for ABA, for determining whether specific sentences hold under the admissible/grounded semantics. We show that these new derivations are superior with respect to standard dispute derivations in that they are complete in general, rather than solely for restricted classes of ABA frameworks. Finally, we present several experiments comparing the implementation of graph-based admissible/grounded dispute derivations with implementations of standard dispute derivations, suggesting that the graph-based approach is computationally advantageous.
Carstens L, Toni F, 2015, Improving out-of-domain sentiment polarity classification using argumentation, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Publisher: IEEE, Pages: 1294-1301
Domain dependence is an issue that most researchers in corpus-based computational linguistics have faced at one time or another. With this paper we describe a method to perform sentiment polarity classification across domains that utilises Argumentation. We train standard supervised classifiers on a corpus and then attempt to classify instances from a separate corpus, whose contents are concerned with different domains (e.g. sentences from film reviews vs. Tweets). As expected the classifiers perform poorly and we improve upon the use of a simple classifier for out-of-domain classification by taking class labels suggested by classifiers and arguing about their validity. Whenever we can find enough arguments suggesting a mistake has been made by the classifier we change the class label according to what the arguments tell us. By arguing about class labels we are able to improve F1 measures by as much as 14 points, with an average improvement of F1 = 7.33 across all experiments.
Eiter T, Toni F, 2015, Introduction to the 31st International Conference on Logic Programming special issue, the 31st International Conference on Logic Programming special issue, Publisher: CAMBRIDGE UNIV PRESS, Pages: 413-418, ISSN: 1471-0684
Toni F, Gao Y, 2015, Potential based reward shaping for hierarchical reinforcement learning, 24th International Joint Conference on Artificial Intelligence, Publisher: AAAI Press/ International Joint Conference on Artificial Intelligence
Hierarchical Reinforcement Learning (HRL) outperformsmany ‘flat’ Reinforcement Learning (RL)algorithms in some application domains. However,HRL may need longer time to obtain the optimalpolicy because of its large action space. PotentialBased Reward Shaping (PBRS) has been widelyused to incorporate heuristics into flat RL algorithmsso as to reduce their exploration. In thispaper, we investigate the integration of PBRS andHRL, and propose a new algorithm: PBRS-MAXQ-0. We prove that under certain conditions, PBRSMAXQ-0is guaranteed to converge. Empirical resultsshow that PBRS-MAXQ-0 significantly outperformsMAXQ-0 given good heuristics, and canconverge even when given misleading heuristics.
Toni F, Schulz C, 2015, Logic programming in assumption-based argumentation revisited — semantics and graphical representation, Twenty-Ninth AAAI Conference on Artificial Intelligence, Publisher: AAAI Press, Pages: 1569-1575, ISSN: 2159-5399
Logic Programming and Argumentation Theory have beenexisting side by side as two separate, yet related, techniquesin the field of Knowledge Representation and Reasoning formany years. When Assumption-Based Argumentation (ABA)was first introduced in the nineties, the authors showed howa logic program can be encoded in an ABA framework andproved that the stable semantics of a logic program correspondsto the stable extension semantics of the ABA frameworkencoding this logic program. We revisit this initial workby proving that the 3-valued stable semantics of a logic programcoincides with the complete semantics of the encodingABA framework, and that the L-stable semantics of thislogic program coincides with the semi-stable semantics ofthe encoding ABA framework. Furthermore, we show howto graphically represent the structure of a logic program encodedin an ABA framework and that not only logic programmingand ABA semantics but also Abstract Argumentationsemantics can be easily applied to a logic program using thesegraphical representations.
Schulz C, Toni F, 2015, Justifying answer sets using argumentation, Theory and Practice of Logic Programming, Vol: 16, Pages: 59-110, ISSN: 1475-3081
An answer set is a plain set of literals which has no further structure that would explain why certain literals are part of it and why others are not. We show how argumentation theory can help to explain why a literal is or is not contained in a given answer set by defining two justification methods, both of which make use of the correspondence between answer sets of a logic program and stable extensions of the assumption-based argumentation (ABA) framework constructed from the same logic program. Attack Trees justify a literal in argumentation-theoretic terms, i.e. using arguments and attacks between them, whereas ABA-Based Answer Set Justifications express the same justification structure in logic programming terms, that is using literals and their relationships. Interestingly, an ABA-Based Answer Set Justification corresponds to an admissible fragment of the answer set in question, and an Attack Tree corresponds to an admissible fragment of the stable extension corresponding to this answer set.
Baroni P, Romano M, Toni F, et al., 2015, Automatic evaluation of design alternatives with quantitative argumentation, Argument and Computation, Vol: 6, Pages: 24-49, ISSN: 1946-2166
This paper presents a novel argumentation framework to support Issue-Based Information System style debates on design alternatives, by providing an automatic quantitative evaluation of the positions put forward. It also identifies several formal properties of the proposed quantitative argumentation framework and compares it with existing non-numerical abstract argumentation formalisms. Finally, the paper describes the integration of the proposed approach within the design Visual Understanding Environment software tool along with three case studies in engineering design. The case studies show the potential for a competitive advantage of the proposed approach with respect to state-of-the-art engineering design methods.
Schulz C, Satoh K, Toni F, 2015, Characterising and explaining inconsistency in logic programs, Pages: 467-479, ISSN: 0302-9743
A logic program under the answer set semantics can be inconsistent because its only answer set is the set of all literals, or because it does not have any answer sets. In both cases, the reason for the inconsistency may be (1) only explicit negation, (2) only negation as failure, or (3) the interplay between these two kinds of negation. Overall, we identify four different inconsistency cases, and show how the respective reason can be further characterised by a set of culprits using semantics which are weaker than the answer set semantics. We also provide a technique for explaining the set of culprits in terms of trees whose nodes are derivations. This can be seen as an important first step towards debugging inconsistent logic programs.
De Vos M, Eiter T, Lierler Y, et al., 2015, Technical communications of the 31st international conference on logic programming - Editorial, ISSN: 1613-0073
Carstens L, Toni F, 2015, Towards relation based Argumentation Mining, Pages: 29-34
We advocate a relation based approach to Argumentation Mining. Our focus lies on the extraction of argumentative relations instead of the identification of arguments, themselves. By classifying pairs of sentences according to the relation that holds between them we are able to identify sentences that may be factual when considered in isolation, but carry argumentative meaning when read in context. We describe scenarios in which this is useful, as well as a corpus of annotated sentence pairs we are developing to provide a testbed for this approach.
Aurisicchio M, Baroni P, Pellegrini D, et al., 2015, Comparing and integrating argumentation-based with matrix-based decision support in Arg&Dec, Pages: 1-20, ISSN: 0302-9743
The need of making decisions pervades every field of human activity. Several decision support methods and software tools are available in the literature, relying upon different modelling assumptions and often producing different results. In this paper we investigate the relationships between two such approaches: the recently introduced QuAD frameworks, based on the IBIS model and quantitative argumentation, and the decision matrix method, widely adopted in engineering. In addition, we describe Arg&Dec (standing for Argue & Decide), a prototype web application for collaborative decision-making, encompassing the two methodologies and assisting their comparison through automated transformation.
Fan X, Toni F, 2015, On explanations for non-acceptable arguments, Pages: 112-127, ISSN: 0302-9743
Argumentation has the unique advantage of giving explanations to reasoning processes and results. Recent work studied how to give explanations for arguments that are acceptable, in terms of arguments defending it. This paper studies the counterpart of this problem by formalising explanations for arguments that are not acceptable. We give two different views (an argument-view and an attack-view) in explaining the non-acceptability of an argument and show the computation of explanations with debate trees.
Carstens L, Fan X, Gao Y, et al., 2015, An overview of argumentation frameworks for decision support, Pages: 32-49, ISSN: 0302-9743
Several forms of argumentation frameworks have been used to support decision-making: these frameworks allow at the same time a graphical representation of decision problems as well as an automatic evaluation of the goodness of decisions. We overview several such uses of argumentation frameworks and discuss future directions of research, including cross-fertilisations amongst them.
Toni F, Fan X, 2015, On Computing Explanations in Argumentation, Twenty-Ninth AAAI Conference on Artificial Intelligence Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence, Publisher: Association for the Advancement of Artificial Intelligence, Pages: 1496-1502, ISSN: 1535-6698
Sierra C, Toni F, 2015, AI Communications track on agreement technologies, AI COMMUNICATIONS, Vol: 28, Pages: 385-385, ISSN: 0921-7126
Fan X, Toni F, 2015, Mechanism Design for Argumentation-Based Information-Seeking and Inquiry, 18th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), Publisher: SPRINGER-VERLAG BERLIN, Pages: 519-527, ISSN: 0302-9743
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, 2015, Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015), Cork, Ireland, August 31 - September 4, 2015., Publisher: CEUR-WS.org
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