Machine Learning and Optimization

In this paper, we consider optimization as an approach for quickly and flexibly developing hybrid cognitive capabilities that are efficient, scalable, and can exploit task knowledge to improve solution speed and quality.

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a forest of trees whose nodes store previously seen examples.

This paper presents a new beamforming method for distant speech recognition (DSR).

Few direct comparisons have been made between the responsiveness of children with autism to computer-generated or animated characters and their responsiveness to humans. Twelve 4- to 8-year-old children with autism interacted with a human therapist; a human-controlled, interactive avatar in a theme park; a human actor speaking like the avatar; and cartoon characters who sought social responses.

In this paper we introduce a simplified architecture for gated recurrent neural networks that can be used in single-pass applications, where word-spotting needs to be done in real-time and phoneme-level information is not available for training. The network operates as a self-contained block in a strictly forward-pass configuration to directly generate keyword labels.

Our goal is to endow an interactive character with appropriate turn-taking behavior using visual, audio and contextual features.

We build turn-taking models for autonomous characters in language-based interactions with small groups of children.

We evaluated the behaviors by having children play a language-based game twice, once with a flexible host and once with an inflexible host that did not have access to the behaviors.

We examine the particular problem of identifying when each child playing an interactive game in a small group is speaking to an animated character.

We address the problem of identifying when a child playing an interactive game in a small group is speaking to an animated or robotic character versus conferring with his friend.