In this article, an optimization method is proposed for the dimensional synthesis of robotic manipulators with limited mobility, i.e. less than 6 degrees-of-freedom (“DoF”), a prescribed set tasks in constrained environment. Since these cannot achieve full 6-DoF they are able to follow only certain paths position and orientation space. While most common approach problem employs pure path-planni...

Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge embedding methods focus on structural relationships in fixed triples while ignoring temporal information. Currently, existing time-aware only factual plausibility, smoothness, models interactions betwee...

Recently, two approaches, fine-tuning large pre-trained language models and variational training, have attracted significant interests, separately, for semi-supervised end-to-end task-oriented dialog (TOD) systems. In this paper, we propose Variational Latent-State GPT model (VLS-GPT), which is the first to combine strengths of approaches. Among many options models, generative inference learnin...

End-to-end neural networks have achieved promising performances in natural language generation (NLG). However, they are treated as black boxes and lack interpretability. To address this problem, we propose a novel framework, heterogeneous rendering machines (HRM), that interprets how generators render an input dialogue act (DA) into utterance. HRM consists of renderer set mode switcher. The con...

This work presents a new dialog dataset, CookDial, that facilitates research on task-oriented systems with procedural knowledge understanding. The corpus contains 260 human-to-human dialogs in which an agent, given recipe document, guides the user to cook dish. Dialogs CookDial exhibit two unique features: (i) alignment between flow and supporting document; (ii) complex agent decision-making in...