This paper extends existing methods for information searching and sharing in large-scale, dynamic networks of agents, to deal with networks of heterogeneous agents: Agents that do...
The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend ...
We report the implementation and evaluation of a Simulation Theory (ST) approach to the Theory of Mind in intelligent graphical agents driven by an affective agent architecture FA...
The paper formalizes a distributed approach to the problem of supervising the execution of a multi-agent plan where (possibly joint) actions are executed concurrently by a team of...
We study the concept of bribery in the situation where voters are willing to change their votes as we ask them, but where their prices depend on the nature of the change we reques...
Decentralized Markov decision processes are frequently used to model cooperative multi-agent systems. In this paper, we identify a subclass of general DEC-MDPs that features regul...
The Distributed Constraint Optimization Problem (DCOP) is a fundamental formalism for multi-agent cooperation. A dedicated framework called Resource Constrained DCOP (RCDCOP) has ...
Toshihiro Matsui, Marius Silaghi, Katsutoshi Hiray...
This paper addresses the problem of plan recognition for multiagent teams. Complex multi-agent tasks typically require dynamic teams where the team membership changes over time. T...
We present an algorithm that allows swarms of robots to navigate in environments containing unknown obstacles, moving towards and spreading along 2D shapes given by implicit funct...