Maxime Morge

AAMAS Conference 2019 Conference Paper

Adaptive Multi-agent System for Situated Task Allocation

• Quentin Baert
• Anne-Cécile Caron
• Maxime Morge
• Jean-Christophe Routier
• Kostas Stathis

EUMAS Conference 2017 Conference Paper

Negotiation Strategy of Divisible Tasks for Large Dataset Processing

• Quentin Baert
• Anne-Cécile Caron
• Maxime Morge
• Jean-Christophe Routier

Abstract MapReduce is a design pattern for processing large datasets on a cluster. Its performances depend on some data skews and on the runtime environment. In order to tackle these problems, we propose an adaptive multiagent system. The agents interact during the data processing and the dynamic task allocation is the outcome of negotiations. These negotiations aim at improving the workload partition among the nodes within a cluster and so decrease the runtime of the whole process. Moreover, since the negotiations are iterative the system is responsive in case of node performance variations. In this paper, we show how, when a task is divisible, an agent may split it in order to negotiate its subtasks.

AAMAS Conference 2013 Conference Paper

Minimal Concession Strategy for Reaching Fair, Optimal and Stable Marriages

• Patricia Everaere
• Maxime Morge
• Gauthier Picard

AAMAS Conference 2010 Conference Paper

Combining statistics and arguments to compute trust

• Paul-Amaury Matt
• Maxime Morge
• Francesca Toni

We propose a method for constructing Dempster-Shafer belief functions modeling the trust of a given agent (the evaluator) in another (the target) by combining statistical information concerning the past behaviour of the target and arguments concerning the target's expected behaviour. Thesearguments are built from current and past contracts betweenevaluator and target. We prove that our method extends astandard computational method for trust that relies uponstatistical information only. We observe experimentally thatthe two methods have identical predictive performance whenthe evaluator is highly "cautious", but our method gives asignificant increase when the evaluator is not or is only moderately "cautious". Finally, we observe experimentally thattarget agents are more motivated to honour contracts whenevaluated using our model of trust than when trust is computed on a purely statistical basis.

ICAART Conference 2009 Conference Paper

Arguing over Motivations within the V3A-Architecture for Self-Adaptation

• Maxime Morge
• Kostas Stathis
• Laurent Vercouter

The Vowel Agent Argumentation Architecture (V3A) is an abstract model by means of which an autonomous agent argues with itself to manage its motivations and arbitrate its possible internal conflicts. We propose an argumentation technique which specifies the internal dialectical process and a dialogue-game amongst internal components which can dynamically join/leave the game, thus having the potential to support the development of self-adaptive agents. We exemplify this dialectical representation of the V3A model with a scenario, whereby components of the agent's mind called facets can be automatically downloaded to argue an agent's motivation.

AILAW Journal 2005 Journal Article

Collective Decision-Making Process to Compose Divergent Interests and Perspectives

• Maxime Morge

Abstract We propose in this paper DIAL, a framework for inter-agents dialogue, which formalize a collective decision-making process to compose divergent interests and perspectives. This framework bounds a dialectics system in which argumentative agents play and arbitrate to reach an agreement. For this purpose, we propose an argumentation-based reasoning to manage the conflicts between arguments having different strengths for different agents. Moreover, we propose a model of argumentative agents which justify the hypothesis to which they commit and take into account the commitments of their interlocutors according to their reputations. In the scope of our dialectics system, a third agent is responsible of the final decision outcome which is taken by resolving the conflict between two players according to their competences and the advanced arguments.