Title: Queryable Self-Deliberating Dynamic Systems
Abstract: Dynamic systems that operate autonomously in nondeterministic (uncertain) environments are becoming a reality. These include intelligent robots, self-driving cars, but also manufacturing systems (Industry 4.0), smart objects and spaces (IoT), advanced business process management systems (BPM), and many others. These systems are currently being revolutionized by advancements in sensing (vision, language understanding) and actuation components (autonomous mobile manipulators, automated storage and retrieval systems). However, in spite of these advances, their core logic is still mainly based on hard-wired rules either designed or possibly obtained through a learning process.
On the other hand, we can envision systems that are able to deliberate by themselves about their course of action when un-anticipated circumstances arise, new goals are submitted, new safety conditions are required, and new regulations and conventions are imposed. Crucially, empowering dynamic systems with deliberating capabilities carries significant risks and therefore we must be able to balance such power with trust. For this reason it is of interest to make these systems queryable, analyzable and explainable in human terms, so as to be guarded by human oversight. In this talk we discuss how recent scientific discoveries in Knowledge Representation and Planning combined with insights from Verification and Synthesis in Formal Methods, Data-Aware Processes in Databases, as well as other areas of AI, chart a novel path for realizing what we may call Queryable Self-Deliberating Dynamic Systems. That is, systems with a multifaceted model of the world that can be exploited to deliberate on their course of action and answer queries about their behavior.
Short Bio: Giuseppe De Giacomo is full professor in Computer Science and Engineering at Univ. Roma “La Sapienza". His research activity has concerned theoretical, methodological and practical aspects in different areas of AI and CS, most prominently Knowledge Representation, Reasoning about Actions, Generalized Planning, Autonomous Agents, Service Composition, Business Process Modeling, Data Management and Integration. He is AAAI Fellow, ACM Fellow, and EurAI Fellow.
Title: Doing for our robots what evolution did for us
Abstract: We, as robot engineers, have to think hard about our role in the design of robots and how it interacts with learning, both in "the factory" (that is, at engineering time) and in "the wild" (that is, when the robot is delivered to a customer). I will share some general thoughts about the strategies for robot design and then talk in detail about some work I have been involved in, both in the design of an overall architecture for an intelligent robot and in strategies for learning to integrate new skills into the repertoire of an already competent robot.
Short Bio: Leslie Kaelbling is the Panasonic Professor of Computer Science and Engineering at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founder of the Journal of Machine Learning Research. Her research agenda is to make intelligent robots by integrating perception, estimation, learning, planning, and reasoning.
Title: Deep Learning: Why deep and is it only doable for neural networks?
Abstract: The term “deep learning” is generally regarded as a synonym of "deep neural networks (DNNs)". Though deep learning techniques have achieved great success in many applications, it remains unclear why the models must be deep and shallow models could not be that powerful. In this talk we will share some thoughts about the essentials of deep learning, and claim that deep learning can be realized with other models, even based on non-differentiable ones, not necessarily to be limited by neural networks and gradient-based backpropagation.
Short Bio: Zhi-Hua Zhou is a Professor of Computer Science and Artificial Intelligence at Nanjing University. His research interests are mainly in machine learning and data mining, involving ensemble methods, weakly supervised learning, multi-label learning, etc. He authored the books "Ensemble Methods: Foundations and Algorithms" and "Machine Learning (in Chinese)". He is a Fellow of the AAAI, ACM, AAAS, IEEE and IAPR.