Publications
435 results found
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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- Citations: 4
, 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
Fan X, Toni F, 2014, A general framework for sound assumption-based argumentation dialogues, ARTIFICIAL INTELLIGENCE, Vol: 216, Pages: 20-54, ISSN: 0004-3702
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- Citations: 20
Evripidou V, Toni F, 2014, Quaestio-it.com: a social intelligent debating platform, Journal of Decision Systems, Vol: 23, Pages: 333-349, ISSN: 1246-0125
Information sharing between online users has altered the way we seek and find information. Users have at their disposal a wide range of tools for exchanging opinions and engaging in discussions. This creates a vast amount of information and the need for a more strict structure and semantics in order to take full advantage of the information and automatically extract useful conclusions to support decision-making. In this paper we describe a framework, based on computational argumentation, for modeling and analysing social discussions, and demonstrate a question-and-answer web application, based on this framework, offering debating infrastructure for opinion exchanges between users and providing support for extracting intelligent answers to user-posed questions.
Besnard P, Garcia A, Hunter A, et al., 2014, Introduction to structured argumentation, Argument and Computation, Vol: 5, Pages: 1-4, ISSN: 1946-2166
In abstract argumentation, each argument is regarded as atomic. There is no internal structure to an argument. Also, there is no specification of what is an argument or an attack. They are assumed to be given. This abstract perspective provides many advantages for studying the nature of argumentation, but it does not cover all our needs for understanding argumentation or for building tools for supporting or undertaking argumentation. If we want a more detailed formalisation of arguments than is available with abstract argumentation, we can turn to structured argumentation, which is the topic of this special issue of Argument and Computation. In structured argumentation, we assume a formal language for representing knowledge, and specifying how arguments and counterarguments can be constructed from that knowledge. An argument is then said to be structured in the sense that normally the premises and claim of the argument are made explicit, and the relationship between the premises and claim is formally defined (for instance using logical entailment). In this introduction, we provide a brief overview of the approaches covered in this special issue on structured argumentation. © 2014 Taylor and Francis.
Toni F, 2014, A tutorial on assumption-based argumentation, Argument and Computation, Vol: 5, Pages: 89-117, ISSN: 1946-2166
We give an introductory tutorial to assumption-based argumentation (referred to as ABA)-a form of argumentation where arguments and attacks are notions derived from primitive notions of rules in a deductive system, assumptions and contraries thereof. ABA is equipped with different semantics for determining winning sets of assumptions and-interchangeably and equivalently-winning sets of arguments. It is also equipped with a catalogue of computational techniques to determine whether given conclusions can be supported by a winning set of arguments. These are in the form of disputes between (fictional) proponent and opponent players, provably correct w.r.t. the semantics. Albeit simple, ABA is powerful in that it can be used to represent and reason with a number of problems in AI and beyond: non-monotonic reasoning, preferences, decisions. While doing so, it encompasses the expressive and computational needs of these problems while affording the transparency and explanatory power of argumentation. © 2014 Taylor and Francis.
Fan X, Toni F, 2014, Decision making with assumption-based argumentation, Publisher: Springer, Pages: 127-142, ISSN: 0302-9743
In this paper, we present two different formal frameworks for representing decision making. In both frameworks, decisions have multiple attributes and meet different goals. In the second framework, decisions take into account preferences over goals. We also study a family of decision functions representing making decisions with different criteria, including decisions meeting all goals, most goals, goals no other decisions meet, and most preferred achievable goals. For each decision function, we define an argumentation-based computational mechanism for computing and explaining the selected decisions. We make connections between decision making and argumentation semantics, i.e., selected decisions in a decision making framework are admissible arguments in the corresponding argumentation framework. The main advantage of our approach is that it not only selects decisions but also gives an argumentation-based justification of selected decisions. © 2014 Springer-Verlag Berlin Heidelberg.
Gao Y, Toni F, 2014, Argumentation accelerated reinforcement learning for RoboCup Keepaway-Takeaway, Pages: 79-94, ISSN: 0302-9743
Multi-Agent Learning (MAL) is a complex problem, especially in real-time systems where both cooperative and competitive learning are involved. We study this problem in the RoboCup Soccer Keepaway-Takeaway game and propose Argumentation Accelerated Reinforcement Learning (AARL) for this game. AARL incorporates heuristics, represented by arguments in Value-Based Argumentation, into Reinforcement Learning (RL) by using Heuristically Accelerated RL techniques. We empirically study for a specific setting of the Keepaway-Takeaway game the suitability of AARL, in comparison with standard RL and hand-coded strategies, to meet the challenges of MAL. © 2014 Springer-Verlag Berlin Heidelberg.
Kakas A, Toni F, Mancarella P, 2014, Argumentation for propositional logic and nonmonotonic reasoning, Pages: 272-286, ISSN: 1613-0073
Argumentation has played a significant role in understanding and unifying under a common framework different forms of defeasible reasoning in AI. Argumentation is also close to the original inception of logic as a framework for formalizing human argumentation and debate. In this context, the purpose of this paper is twofold: To draw a formal connection between argumentation and classical reasoning (in the form of Propositional Logic) and link this to support defeasible, Non-Monotonic Reasoning in AI. To this effect, we propose Argumentation Logic and show properties and extensions thereof.
Gao Y, Toni F, 2014, Argumentation accelerated reinforcement learning for RoboCup Keepaway-Takeaway, Pages: 79-94, ISSN: 0302-9743
Multi-Agent Learning (MAL) is a complex problem, especially in real-time systems where both cooperative and competitive learning are involved. We study this problem in the RoboCup Soccer Keepaway-Takeaway game and propose Argumentation Accelerated Reinforcement Learning (AARL) for this game. AARL incorporates heuristics, represented by arguments in Value-Based Argumentation, into Reinforcement Learning (RL) by using Heuristically Accelerated RL techniques. We empirically study for a specific setting of the Keepaway-Takeaway game the suitability of AARL, in comparison with standard RL and hand-coded strategies, to meet the challenges of MAL. © 2014 Springer-Verlag Berlin Heidelberg.
Craven R, Toni F, Williams M, 2014, Graph-based dispute derivations in assumption-based argumentation, Second International Workshop, TAFA 2013, Publisher: Springer, Pages: 46-62, ISSN: 0302-9743
Arguments in structured argumentation are usually defined as trees. This introduces both conceptual redundancy and inefficiency in standard methods of implementation. We introduce rule-minimal arguments and argument graphs to solve these problems, studying their use in assumption-based argumentation (ABA), a well-known form of structured argumentation. In particular, we define a new notion of graph-based dispute derivations for determining acceptability of claims under the grounded semantics in ABA, study formal properties and present an experimental evaluation thereof. © 2014 Springer-Verlag Berlin Heidelberg.
Zhong Q, Fan X, Toni F, et al., 2014, Explaining best decisions via argumentation, Pages: 224-237, ISSN: 1613-0073
This paper presents an argumentation-based multi-attribute decision making model, where decisions made can be explained in natural language. More specifically, an explanation for a decision is obtained from a mapping between the given decision framework and an argumentation framework, such that best decisions correspond to admissible sets of arguments, and the explanation is generated automatically from dispute trees sanctioning the admissibility of arguments. We deploy a notion of rationality where best decisions meet most goals and exhibit fewest redundant attributes. We illustrate our method by a legal example, where decisions amount to past cases most similar to a given new, open case.
Bonatti P, Oliveira E, Sabater-Mir J, et al., 2014, On the integration of trust with negotiation, argumentation and semantics, KNOWLEDGE ENGINEERING REVIEW, Vol: 29, Pages: 31-50, ISSN: 0269-8889
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- Citations: 3
Toni F, 2014, From Logic Programming to Argumentation and Back., Publisher: CEUR-WS.org, Pages: 11-11
Evripidou V, Carstens L, Toni F, et al., 2014, Argumentation-based collaborative decisions for design, 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Publisher: IEEE COMPUTER SOC, Pages: 805-809, ISSN: 1082-3409
Carstens L, Toni F, Evripidou V, 2014, Argument Mining and Social Debates, 5th Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 451-452, ISSN: 0922-6389
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- Citations: 1
Schulz C, Toni F, 2014, Complete Assumption Labellings, 5th Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 405-412, ISSN: 0922-6389
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- Citations: 6
Kakas A, Toni F, Mancarella P, 2014, Argumentation Logic, 5th Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 345-356, ISSN: 0922-6389
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- Citations: 2
Gao Y, Toni F, 2014, Argumentation Accelerated Reinforcement Learning for Cooperative Multi-Agent Systems, 21st European Conference on Artificial Intelligence (ECAI), Publisher: IOS PRESS, Pages: 333-338, ISSN: 0922-6389
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- Citations: 7
Fan X, Toni F, 2014, On Computing Explanations in Abstract Argumentation, 21st European Conference on Artificial Intelligence (ECAI), Publisher: IOS PRESS, Pages: 1005-1006, ISSN: 0922-6389
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- Citations: 9
Fan X, Toni F, Mocanu A, et al., 2014, Dialogical Two-Agent Decision Making with Assumption-based Argumentation, International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Publisher: ASSOC COMPUTING MACHINERY, Pages: 533-540
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- Citations: 21
Kakas AC, Mancarella P, Sadri F, et al., 2014, Computational Logic Foundations of KGP Agents., CoRR, Vol: abs/1401.3443
Evripidou V, Toni F, 2014, Quaestio-it.com: a social intelligent debating platform, Journal of Decision Systems, ISSN: 1246-0125
Information sharing between online users has altered the way we seek and find information. Users have at their disposal a wide range of tools for exchanging opinions and engaging in discussions. This creates a vast amount of information and the need for a more strict structure and semantics in order to take full advantage of the information and automatically extract useful conclusions to support decision-making. In this paper we describe a framework, based on computational argumentation, for modeling and analysing social discussions, and demonstrate a question-and-answer web application, based on this framework, offering debating infrastructure for opinion exchanges between users and providing support for extracting intelligent answers to user-posed questions. © 2014 © 2014 Taylor & Francis.
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