Invited Talks
openEASE --- an open knowledge service for knowledge representation and reasoning research for robotic agents
Abstract
In this talk I will present openEASE, a web-based knowledge service
providing robot and human activity data. openEASE contains
semantically annotated data of manipulation actions, including the
environment the agent is acting in, the objects it manipulates, the
task it performs, and the behavior it generates. The episode
representations can include images captured by the robot, other sensor
datastreams as well as full-body poses. A powerful query language and
inference tools, allow reasoning about the data and retrieving
requested information based on semantic queries. Based on the data and
using the inference tools robots can answer queries regarding to what
they did, why, how, what happened, and what they saw.
openEASE can be used by KR&R researchers using a browser-based query
and visualization interface, but also remotely by robotic via a
WebSocket API.
Michael Beetz
Michael Beetz is a professor for Computer Science at the Faculty for Mathematics & Informatics of the University Bremen and head of the Institute for Artificial Intelligence (IAI). He received his diploma degree in Computer Science with distinction from the University of Kaiserslautern. His MSc, MPhil, and PhD degrees were awarded by Yale University in 1993, 1994, and 1996, and his Venia Legendi from the University of Bonn in 2000. He was vice-coordinator of the German cluster of excellence CoTeSys (Cognition for Technical Systems, 2006-2011), coordinator of the European FP7 integrating project RoboHow (web-enabled and experience-based cognitive robots that learn complex everyday manipulation tasks, 2012-2016), and is the coordinator of the German collaborative research centre EASE (Everyday Activity Science and Engineering, since 2017). His research interests include plan-based control of robotic agents, knowledge processing and representation for robots, integrated robot learning, and cognition-enabled perception.
Probabilistic Planning and Control by Probabilistic Programming: Semantics, Inference and Learning
Abtract
Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is defined over a set of actions which change that state in one way or another. Planning in many real-world settings, however, is much more involved: an agent's knowledge is almost never simply a set of facts that are true, and actions that the agent intends to execute never operate the way they are supposed to. Thus, probabilistic planning attempts to incorporate stochastic models directly into the planning process. In this article, we briefly report on probabilistic planning through the lens of probabilistic (logic) programming: a programming paradigm that aims to ease the specification of structured probability distributions. In particular, we provide an overview of the features of two systems, HYPE and ALLEGRO, which emphasise different strengths of probabilistic programming that are particularly useful for complex modelling issues raised in probabilistic planning. Among other things, with these systems, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting. In the last part of the talk, we discuss some ongoing work on how to learn the parameter and structure of probabilistic (logic) programs.
Vaishak Belle
Vaishak Belle is a Chancellor’s Fellow/Lecturer at the School of Informatics, University of Edinburgh, an Alan Turing Institute Faculty Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. Vaishak’s research is in artificial intelligence, and is motivated by the need to augment learning and perception with high-level structured, commonsensical knowledge, to enable AI systems to learn faster and more accurate models of the world. He is interested in computational frameworks that are able to explain their decisions, modular, re-usable, and robust to variations in problem description. He has co-authored over 40 scientific articles on AI, and along with his co-authors, he has won the Microsoft best paper award at UAI, and the Machine learning journal award at ECML-PKDD. In 2014, he received a silver medal by the Kurt Goedel Society.
