Combining multiple classifiers via combining schemes or meta-learners has led to substantial improvements in many classification problems. One of the challenging tasks is to choos...
In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
Despite the significant progress to extend Markov Decision Processes (MDP) to cooperative multi-agent systems, developing approaches that can deal with realistic problems remains ...
This paper proposes a novel method to characterize the performance of autonomous agents in the Trading Agent Competition for Supply Chain Management (TAC-SCM). We create benchmark...
Constraint programming is a commonly used technology for solving complex combinatorial problems. However, users of this technology need significant expertise in order to model the...
The ability to interpret demonstrations from the perspective of the teacher plays a critical role in human learning. Robotic systems that aim to learn effectively from human teach...
Matt Berlin, Jesse Gray, Andrea Lockerd Thomaz, Cy...
The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sources containing such opinions, e.g., product reviews, forums, discussion grou...
One important aspect in directing cognitive robots or agents is to formally specify what is expected of them. This is often referred to as goal specification. Temporal logics such...
Significant work has been done on computational aspects of solving games under various solution concepts, such as Nash equilibrium, subgame perfect Nash equilibrium, correlated eq...