Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The
devices cooperate to monitor one or more physical phenomena within an area of interest …

For an autonomous system to be helpful to humans and to pose no unwarranted risks, it
needs to align its values with those of the humans in its environment in such a way that its …

In the past decades, many optimization methods have been devised and applied to job shop
scheduling problem (JSSP) to find the optimal solution. Many methods assumed that the …

The flexible job shop scheduling (FJSS) is important in real-world factories due to the wide
applicability. FJSS schedules the operations of jobs to be executed by specific machines at …

Intelligent transport systems, efficient electric grids, and sensor networks for data collection
and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve …

In the framework of fully cooperative multi-agent systems, independent (non-communicative)
agents that learn by reinforcement must overcome several difficulties to manage to …

We extend Q-learning to a noncooperative multiagent context, using the framework of
generalsum stochastic games. A learning agent maintains Q-functions over joint actions …

We develop an exact dynamic programming algorithm for partially observable stochastic
games (POSGs). The algorithm is a synthesis of dynamic programming for partially …

Over the past few years, several methods for segmenting a scene containing multiple rigidly
moving objects have been proposed. However, most existing methods have been tested on …