Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

Bikakis, Antonis; Caire, Patrice

doi:10.1007/978-3-319-17130-2_6

Cited by 5 publications

(6 citation statements)

References 23 publications

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“…As we also mention in the Introduction, this study is built on previous work on modeling multi-agent systems as MCS and computing coalitions using MCS computational methods and tools, which we first presented in [28]. Here we give more details about the proposed methodology.…”

Section: Related Workmentioning

confidence: 99%

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Contextual and Possibilistic Reasoning for Coalition Formation

Bikakis

Caire

2020

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In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

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Section: Related Workmentioning

confidence: 99%

“…This article is an extended version of [28], where we presented our methodology for formalizing multi-agent systems and computing coalitions among agents under the perfect world assumption, i.e., actions are always carried out with success by the agents that they have been assigned to.…”

Section: Introductionmentioning

confidence: 99%

Contextual and Possibilistic Reasoning for Coalition Formation

Bikakis

Caire

2020

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“…As tenant structures may change over time, as well as tenants' attitudes and policies towards each other, coalitions of cooperative agents of different tenants may emerge and disappear over time. The insights of [154,155] may help address this setting. However, agents need to be aware of their own and the other agents' association.…”

Section: Control Conflictsmentioning

confidence: 99%

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

109

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Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.

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“…There must be a tradeoff between how much privacy the system allows, and how cooperative and convivial the system can be [131]. On the one hand, the more private the AAL system is, the less opportunities it offers to its users to cooperate and exchange information with each other, and the less convivial it is for users [132,133]. On the other hand, AmI and AAL systems that share all their information with all other users offer better cooperation, but minimise their users' privacy.…”

Section: Tools and Framework For Privacy Protectionmentioning

confidence: 99%

Privacy challenges in Ambient Intelligence systems

Caire

Moawad

Efthymiou

et al. 2016

AIS

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Abstract. Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the "right to be left alone" to being a "commodity" that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The humancentred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

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Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

Cited by 5 publications

References 23 publications

Contextual and Possibilistic Reasoning for Coalition Formation

Contextual and Possibilistic Reasoning for Coalition Formation

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Privacy challenges in Ambient Intelligence systems

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