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IJCAI Conference 2011 Conference Paper

Aggregating Dependency Graphs into Voting Agendas in Multi-Issue Elections

• St
• eacute; phane Airiau
• Ulle Endriss
• Umberto Grandi
• Daniele Porello
• Joel Uckelman

Many collective decision making problems have a combinatorial structure: the agents involved must decide on multiple issues and their preferences over one issue may depend on the choices adopted for some of the others. Voting is an attractive method for making collective decisions, but conducting a multi-issue election is challenging. On the one hand, requiring agents to vote by expressing their preferences over all combinations of issues is computationally infeasible; on the other, decomposing the problem into several elections on smaller sets of issues can lead to paradoxical outcomes. Any pragmatic method for running a multi-issue election will have to balance these two concerns. We identify and analyse the problem of generating an agenda for a given election, specifying which issues to vote on together in local elections and in which order to schedule those local elections.

IJCAI Conference 2007 Conference Paper

• Nicolas Gaud
• Franck Gechter
• St
• eacute; phane Galland
• Abderrafi
• acirc; a Koukam

Holonic Multi-Agent Systems (HMAS) are a convenient and relevant way to analyze, model and simulate complex and open systems. Accurately simulate in real-time complex systems, where a great number of entities interact, requires extensive computational resources and often distribution of the simulation over various computers. A possible solution to these issues is multilevel simulation. This kind of simulation aims at dynamically adapting the level of entities' behaviors (microscopic, macroscopic) while being as faithful as possible to the simulated model. We propose a holonic organizational multilevel model for real-time simulation of complex systems by exploiting the hierarchical and distributed properties of the holarchies. To fully exploit this model, we estimate the deviation of simulation accuracy between two adjacent levels through physics-based indicators. These indicators will then allow us to dynamically determine the most suitable level for each entity in the application to maintain the best compromise between simulation accuracy and available resources. Finally a 3D real-time multilevel simulation of pedestrians is presented as well as a discussion of experimental results.

IJCAI Conference 2007 Conference Paper

• Sandip Sen
• St
• eacute; phane Airiau

Behavioral norms are key ingredients that allow agent coordination where societal laws do not sufficiently constrain agent behaviors. Whereas social laws need to be enforced in a top-down manner, norms evolve in a bottom-up manner and are typically more self-enforcing. While effective norms can significantly enhance performance of individual agents and agent societies, there has been little work in multiagent systems on the formation of social norms. We propose a model that supports the emergence of social norms via learning from interaction experiences. In our model, individual agents repeatedly interact with other agents in the society over instances of a given scenario. Each interaction is framed as a stage game. An agent learns its policy to play the game over repeated interactions with multiple agents. We term this mode of learning social learning, which is distinct from an agent learning from repeated interactions against the same player. We are particularly interested in situations where multiple action combinations yield the same optimal payoff. The key research question is to find out if the entire population learns to converge to a consistent norm. In addition to studying such emergence of social norms among homogeneous learners via social learning, we study the effects of heterogeneous learners, population size, multiple social groups, etc.

IJCAI Conference 2007 Conference Paper

• St
• eacute; phane Ross
• Brahim Chaib-draa

Solving large Partially Observable Markov Decision Processes (POMDPs) is a complex task which is often intractable. A lot of effort has been made to develop approximate offline algorithms to solve ever larger POMDPs. However, even state-of-the-art approaches fail to solve large POMDPs in reasonable time. Recent developments in online POMDP search suggest that combining offline computations with online computations is often more efficient and can also considerably reduce the error made by approximate policies computed offline. In the same vein, we propose a new anytime online search algorithm which seeks to minimize, as efficiently as possible, the error made by an approximate value function computed offline. In addition, we show how previous online computations can be reused in following time steps in order to prevent redundant computations. Our preliminary results indicate that our approach is able to tackle large state space and observation space efficiently and under real-time constraints.