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Stephane Airiau

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8 papers
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8

IJCAI Conference 2017 Conference Paper

Rationalisation of Profiles of Abstract Argumentation Frameworks: Extended Abstract

  • Stephane Airiau
  • Elise Bonzon
  • Ulle Endriss
  • Nicolas Maudet
  • Julien Rossit

We review a recently introduced model in which each of a number of agents is endowed with an abstract argumentation framework reflecting her individual views regarding a given set of arguments. A question arising in this context is whether the diversity of views observed in such a situation is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual information and, second, the personal preferences of the agent concerned. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. This is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

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AAMAS Conference 2016 Conference Paper

Rationalisation of Profiles of Abstract Argumentation Frameworks

  • Stephane Airiau
  • Elise Bonzon
  • Ulle Endriss
  • Nicolas Maudet
  • Julien Rossit

Different agents may have different points of view. This can be modelled using different abstract argumentation frameworks, each consisting of a set of arguments and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. This is useful for understanding what types of profiles can reasonably be expected to come up in a multiagent system.

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AAMAS Conference 2010 Conference Paper

Learning Context Conditions for BDI Plan Selection

  • Dhirendra Singh
  • Sebastian Sardina
  • Lin Padgham
  • Stephane Airiau

An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element oflearning from experience. In particular, the so-called context conditions of plans, on which the whole model relies for plan selection, are restricted to be boolean formulas that are to be specified atdesign/implementation time. To address these limitations, we propose a novel BDI programming framework that, by suitably modeling context conditions as decision trees, allows agents to learn theprobability of success for plans based on previous execution experiences. By using a probabilistic plan selection function, the agentscan balance exploration and exploitation of their plans. We developand empirically investigate two extreme approaches to learning thenew context conditions and show that both can be advantageousin certain situations. Finally, we propose a generalization of theprobabilistic plan selection function that yields a middle-groundbetween the two extreme approaches, and which we thus argue isthe most flexible and simple approach.

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AAMAS Conference 2010 Conference Paper

Multiagent Resource Allocation with Sharable Items: Simple Protocols and Nash Equilibria

  • Stephane Airiau
  • Ulle Endriss

We study a particular multiagent resource allocation problem with indivisible, but sharable resources. In our model, the utility of an agent for using a bundle of resources is the difference between a valuation of that bundle and a congestion cost (or delay), a figure formed by adding up the individual congestion costs of each resource in the bundle. The valuation and the delay can be agent-dependent. When the agents that share a resource also share the resource's control, the current users of a resource will require some compensation when a new agent wants to use the resource. We study the existence of distributed protocols that lead to a social optimum. Depending on constraints on the valuation functions (mainly modularity), on the delay functions (e. g. , convexity), and the structural complexity of the deals between agents, we prove either the existence of some sequences of deals or the convergence of all sequences of deals to a social optimum. When the agents do not have joint control over the resources (i. e. , they can use any resource they want), we study the existence of pure Nash equilibria. We provide results for modular valuation functions and relate them to results from the literature on congestion games.

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AAMAS Conference 2008 Conference Paper

Norm Emergence Under Constrained Interactions in Diverse Societies

  • Partha Mukherjee
  • Stephane Airiau
  • Sandip Sen

Effective norms, emerging from sustained individual interactions over time, can complement societal rules and significantly enhance performance of individual agents and agent societies. Researchers have used a model that supports the emergence of social norms via learning from interaction experiences where each interaction is viewed as a stage game. In this social learning model, which is distinct from an agent learning from repeated interactions against the same player, an agent learns a policy to play the game from repeated interactions with multiple learning agents. The key research question is to characterize when and how the entire population of homogeneous learners converge to a consistent norm when multiple action combinations yield the same optimal payoff. In this paper we study two extensions to the social learning model that significantly enhances its applicability. We first explore the effects of heterogeneous populations where different agents may be using different learning algorithms. We also investigate norm emergence when agent interactions are physically constrained. We consider agents located on a grid where an agent is more likely to interact with other agents situated closer to it than those that are situated afar. The key new results include the surprising acceleration in learning with limited interaction ranges. We also study the effects of pure-strategy players, i. e. , nonlearners in the environment.

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