A significant problem facing researchers in reinforcement learning, and particularly in multi-objective learning, is the dearth of good benchmarks. In this paper, we present a method and software tool enabling the creation of random problem instances, including multi-objective learning problems, with specific structural properties. This tool, called Merlin (for Multi-objective Environments for Reinforcement LearnINg), provides the ability to control these features in predictable ways, thus allowing researchers to begin to build a more detailed understanding about what features of a problem interact with a given learning algorithm to improve or degrade the algorithm's performance. We present this method and tool, and briefly discuss the controls provided by the generator, its supported options, and their implications on the generated benchmark instances.

Face-to-face interaction between people is generally effortless and effective. We exchange glances, take turns speaking and make facial and manual gestures to achieve the goals of the dialogue. This paper describes an action composition and selection architecture for synthetic characters capable of full-duplex, real-time face-to-face interaction with a human. This architecture is part of a computational model of psychosocial dialogue skills, called Y_m_i_r_, that bridges between multimodal perception and multimodal action generation. To test the architecture, a prototype humanoid has been implemented, named G_a_n_d_a_ l_f_, who commands a graphical model of the solar system and can engage in task-directed dialogue with people using speech, manual and facial gesture. Gandalf has been tested in interaction with users and has been shown capable of fluid turn-taking and multimodal dialogue. The primary focus in this paper will be on the action selection mechanisms and low-level composition of motor commands. An overview is also given of the Ymir model and Gandalf's graphical representation.

No abstract available.