Learning to converge to an efficient, i.e., Pareto-optimal Nash equilibrium of the repeated game is an open problem in multiagent learning. Our goal is to facilitate the learning ...
The search for frequent subgraphs is becoming increasingly important in many application areas including Web mining and bioinformatics. Any use of graph structures in mining, howev...
The online learning problem requires a player to iteratively choose an action in an unknown and changing environment. In the standard setting of this problem, the player has to ch...
There are two major approaches to activity coordination in multiagent systems. First, by endowing the agents with the capability to jointly plan, that is, to jointly generate hypot...
This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We s...