A Multi-agent System for Group Decision Support Based on Conflict Resolution Styles

Rossi, Silvia; Napoli, Claudia Di; Barile, Francesco; Liguori, Luca

doi:10.1007/978-3-319-57285-7_9

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Dierently from works based on aggregation strategies, we propose a MAS that uses negotiation techniques to make group recommendations. Similar to our approach is the one presented in [13] that uses a MAS for generating group recommendations in the POI domain, but with some key dierences: it requires live user intervention as candidate proposals are generated using a pool of items rated by all the users; the mediator relies on aggregation techniques to compute the group rating of the items; the mediator executes the protocol and generates the proposals; the agents can generate counter-oers and model users' behavior in conicingt situations. We could not compare with it because it requires live user intervention.…”

Section: Discussionmentioning

confidence: 99%

A MAS-Based Approach for POI Group Recommendation in LBSN

Schiaffino

Godoy

Pace

et al. 2020

Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Discussionmentioning

confidence: 99%

A MAS-Based Approach for POI Group Recommendation in LBSN

Schiaffino

Godoy

Pace

et al. 2020

Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection

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“…However, the model does not consider the interaction beyond two participants (the agent and the user) and neither deal with the context of the dialog that may affect the user's comprehension and motivation. In another work, multiple agents autonomously interact and negotiate with each other to come up with a global recommendation to satisfy their users (Rossi et al, 2017). In this extreme case, the interaction between agents and humans can be considered minimal although every agent operates on-behalf of its corresponding user.…”

Section: Related Workmentioning

confidence: 99%

A coordination framework for multi-agent persuasion and adviser systems

Subagdja

Tan

Kang

2019

Expert Systems with Applications

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Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the right context still remains a great challenge. In this paper, a coordination framework for advice giving and persuasive agents is presented. Apart from preventing overwhelming messages, the adaptation enables cooperation among the agents to make their advices more impactful. In contrast to conventional models that rely on natural language contents or direct multi-modal cues to align the dialogs, the proposed framework is built to be more practical allowing the agents to actively share their observation, goals, and plans to each other. This allows them to adapt the schedules, strategies, and contents of their scheduled advices or reminders at runtime with respect to each other's objectives. Challenges and issues in multi-agent adviser systems are identified and defined in this paper supported by a survey study about perceived usefulness and user comprehensibility of advices delivered by multiple agents. The coordination among the advice giving agents are investigated and exemplified with a simulation of activity of daily living in the context of aging in place.

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“…Secondly, as recognized in the more recent works [3,15,16], another task is to support the group in their decision-making process, hence not just suggesting an item or a rigid ranked list of items, but rather to truly help the group to reach a joint decision. To increase the effectiveness of these functionalities, the system can make use of various individual, group, interrelationship or even decisionprocess features, such as, individual personalities [17,18,20,21] group type (for example family, close friends, or colleagues), close, highly emotional or loosely coupled relationships within the group [6,9,29], having a well defined, structured decision making-process, or a casual open conversation [7,14].…”

Section: Introductionmentioning

confidence: 99%

GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

Vinella

Delić

Barile

et al. 2022

Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

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Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user-and group-centric focus.

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A Multi-agent System for Group Decision Support Based on Conflict Resolution Styles

Cited by 8 publications

References 11 publications

A MAS-Based Approach for POI Group Recommendation in LBSN

A MAS-Based Approach for POI Group Recommendation in LBSN

A coordination framework for multi-agent persuasion and adviser systems

GMAP 2022: Workshop on Group Modeling, Adaptation and Personalization

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