Published in Autonomous Agents and Multi-Agent Systems
Reinforcement Learning (RL) agents are commonly thought of as adaptive decision procedures. They work on input/output data streams called “states”, “actions” and “rewards”. Most current research about RL adaptiveness to changes works under the assumption that the streams signatures (i.e. arity and types of inputs and outputs) remain the same throug...
Published in Autonomous Agents and Multi-Agent Systems
Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, real-world problems are inherently large-scale and of low observability. One key concept that fac...
Published in Autonomous Agents and Multi-Agent Systems
The class of assignment problems is a fundamental and well-studied class in the intersection of Social Choice, Computational Economics and Discrete Allocation. In a general assignment problem, a group of agents expresses preferences over a set of items, and the task is to allocate items to agents in an “optimal” way. A verification variant of this ...
Published in Autonomous Agents and Multi-Agent Systems
We conceptualize a decentralized software application as one constituted from autonomous agents that communicate via asynchronous messaging. Modern software paradigms such as microservices and settings such as the Internet of Things evidence a growing interest in decentralized applications. Constructing a decentralized application involves designin...
Published in Autonomous Agents and Multi-Agent Systems
The paper considers fair allocation of resources that are already allocated in an unfair way. This setting requires a careful balance between the fairness considerations and the rights of the present owners. The paper presents re-division algorithms that attain various trade-off points between fairness and ownership rights, in various settings diff...
Published in Autonomous Agents and Multi-Agent Systems
We study fair allocation of indivisible items, where the items are furnished with a set of conflicts, and agents are not permitted to receive conflicting items. This kind of constraint captures, for example, participating in events that overlap in time, or taking on roles in the presence of conflicting interests. We demonstrate, both theoretically ...
Published in Autonomous Agents and Multi-Agent Systems
In many real-life scenarios, a group of agents needs to agree on a common action, e.g., on a location for a public facility, while there is some consistency between their preferences, e.g., all preferences are derived from a common metric space. The facility location problem models such scenarios and it is a well-studied problem in social choice. W...
Published in Autonomous Agents and Multi-Agent Systems
Efficient and truthful mechanisms to price resources on servers/machines have been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with paramete...
Published in Autonomous Agents and Multi-Agent Systems
Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing conten...
Published in Autonomous Agents and Multi-Agent Systems
We introduce a voting model with multi-agent ranked delegations. This model generalises liquid democracy in two aspects: first, an agent’s delegation can use the votes of multiple other agents to determine their own—for instance, an agent’s vote may correspond to the majority outcome of the votes of a trusted group of agents; second, agents can sub...
