Learning and joint deliberation through argumentation in multiagent systems

Ontañón, Santi; Plaza, Enric

doi:10.1145/1329125.1329318

Cited by 35 publications

(13 citation statements)

References 13 publications

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“…While such analysis can be done in terms of general foundational principles, we believe that such debate can be carried out in more pragmatic terms, so the reasonableness of one approach or the other will depend on the specific collective argumentation context under consideration (e.g. the framework-wise approach could be the Other miscellaneous [8,28,39,59,60,64] most reasonable one in the context of deliberative democracy, while the argument-wise approach could be the most reasonable one in the context of a debate among experts). The interaction of collective argumentation with SCT, JAT and GT stands out, in our opinion, as the most promising approach in the field.…”

Section: Discussionmentioning

confidence: 99%

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Collective argumentation: A survey of aggregation issues around argumentation frameworks

Bodanza

Tohmé

Auday

2017

AAC

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Abstract. Dung's argumentation frameworks have been applied for over twenty years to the analysis of argument justification. This representation focuses on arguments and the attacks among them, abstracting away from other features like the internal structure of arguments, the nature of utterers, the specifics of the attack relation, etc. The model is highly attractive because it reduces most of the complexities involved in argumentation processes. It can be applied to different settings, like the argument evaluation of an individual agent or the case of dialectic disputes between two agents (pro and con), or even in multi-agent collective argumentation. The latter case involves agents with possibly different arguments and/or opinions on how to evaluate them, leading to the possibility of considering multiple sets of arguments and attack relations. Two basic questions can be asked here, namely 'what to aggregate' and 'how to aggregate'. The former concerns what kinds of entities do the agents intend to choose (arguments, attacks, assessments, etc.), while the second one focuses on which aggregation mechanisms yield rational choices (voting on arguments, merging procedures to obtain a common argumentation framework, deliberation processes, etc.). In particular, the question about the rationality of a collective argument choice relates this topic to Social Choice and Judgment Aggregation theories, while its associated strategic issues relate it to Game Theory. The research efforts on the disparate problems elicited by collective argumentation have generated a considerable corpus of literature that deserves an orderly evaluation. This survey is intended as a contribution to that end.

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Section: Discussionmentioning

confidence: 99%

“…Ontañón and Plaza [59] aim to model a deliberation process among learning agents that decide upon some joint decision. They define a specific protocol for the aggregation of attack criteria.…”

Section: Other Workmentioning

confidence: 99%

Collective argumentation: A survey of aggregation issues around argumentation frameworks

Bodanza

Tohmé

Auday

2017

AAC

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“…GILA, however, is closely related to work on distributed learning (Davies and Edwards 1995), where groups of agents collaborate to learn and solve a common problem. Work in this area focuses on both the integration of inductive inferences during learning (Davies 2001) (closely related to the POIROT project), and on the integration of solutions during problem solving (Ontañón and Plaza 2007) (which is closely related to the GILA project).…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Zhang

Shrestha

Yoon

et al. 2012

ACM Trans. Intell. Syst. Technol.

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We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During the performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

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“…The following table shows a summary of the CBR systems [31], [8]. [11], PATDEX [24], CASELINE [28], CREEK [1], CASEY [13], PROTOS [4], RADIX [6] FPL [22], CCBR [17], AMAL [20] ProCLAIM [26] Dynamic Static REBECAS [14], AuRA [16], SAPED [3], SINS [23], CASEP2 [2,22], BROADWAY [12], SBR [5] CICLMAN [25], RoBoCats [19], POMAESS [29], S-MAS [7] MCBR [15], CBR-TEAM [21] Dynamic Dynamic IDCBR-MAS (our approach)…”

Section: Introductionmentioning

confidence: 99%

Incremental dynamic case-based reasoning-MAS: From static to dynamic cycle

Zouhair

En-Naimi

2015

2015 5th International Conference on Information &Amp; Communication Technology and Accessibility (ICTA)

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In this paper we present our approach in the field of Case-Based Reasoning (CBR). This approach is based on the reuse of previous traces that are similar to the current situation in a dynamic way. Several approaches have been used in this area, but they suffer from some limitations in automated realtime management dynamic parameters (the designer should be defined beforehand the different situations possible).We propose a multi-agent multi-layer architecture based on Incremental Dynamic Case-Based Reasoning (IDCBR) able to study dynamic situations (recognition, prediction, and learning situations). We propose a generic approach able to learn automatically from their experiences in order to acquire the knowledge automatically. Based on the Case-Based Reasoning and multi-agent paradigm, we propose a modification of the static CBR cycle in order to introduce a dynamic process of Case-Based Reasoning based on a dynamic similarity measure able to evaluate in real time the similarity between a dynamic situation (target case) and past experiences stored in the memory (sources case) in order to predict the target case in the future.

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Learning and joint deliberation through argumentation in multiagent systems

Cited by 35 publications

References 13 publications

Collective argumentation: A survey of aggregation issues around argumentation frameworks

Collective argumentation: A survey of aggregation issues around argumentation frameworks

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Incremental dynamic case-based reasoning-MAS: From static to dynamic cycle

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