18th International Conference of the Italian Association for Artificial Intelligence
Giuseppe De Giacomo
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. He is Program Chair of ECAI 2020. He has got an ERC Advanced Grant for the project WhiteMech: White-box Self Programming Mechanisms (2019-2024).
Machines who Imagine: Envisioning the Future of AI beyond Data Science
Abstract. AI is currently dominated by data science, a six decades old field that began with the first attempt to build a learning machine by Arthur Samuel to play checkers on an IBM 701 computer in 1959. AI has become commercially highly successful, with economic impact estimated to be in the trillions of dollars. As AI scales, however, to address global problems, the weaknesses of relying extensively on data science are becoming more apparent. The computational costs of deep learning have grown by a factor of 500,000 in the past 5 years. The resulting models are opaque and hard to interpret. The requirements of collecting and labeling huge datasets are difficult to satisfy in many areas.
In this talk, I will outline a new vision for AI, one that is based on imagination. Data science concerns itself with statistical summarization of the past history. Imagination science is concerned with understanding the future, of evaluating the impact of interventions, of causal and counterfactual reasoning, and analogical reasoning and creativity. Imagination has been viewed as a uniquely human capability, one that allows us to compose symphonies, create abstract art, imagine alien life in distant galaxies, invent new technologies and model the large-scale structure of the universe. I will sketch out a few lines of research that are providing the first tangible steps on building imagination machines.
Short Bio. Sridhar Mahadevan is Director of the Data Science Laboratory at Adobe Research. From 2001 to 2018, he was a tenured professor at the University of Massachusetts, Amherst, co-directing the Autonomous Learning Laboratory out of which many of the groundbreaking discoveries in reinforcement learning emerged. He was elected Fellow of AAAI in 2014 for his significant contributions to machine learning. He has published over 150 papers in leading conferences and journals and lectured on various topics in AI in over three dozen countries. In 2018, his paper on imagination machines at AAAI received the Blue Sky Best Paper Award.
Empirical Model Learning: merging knowledge-based and data-driven decision models through machine learning
Abstract. Designing good models is one of the main challenges for obtaining realistic and useful decision support and optimization systems. Traditionally combinatorial models are crafted by interacting with domain experts with limited accuracy guarantees. Nowadays we have access to data sets of unprecedented scale and accuracy about the systems we are deciding on.
In this talk we propose a methodology called Empirical Model Learning that uses machine learning to extract data-driven decision model components and integrates them into an expert-designed decision model. We outline the main domains where EML could be useful and we show how to ground Empirical Model Learning on a problem of thermal-aware workload allocation and scheduling on a multi-core platform.
In addition, we discuss how to use EML with different optimization and machine learning techniques, and we provide some hints about recent work on EML for hierarchical optimization and on-line/off-line optimization.
Short Bio. Michela Milano is full professor at the Department Computer Science and Engineering of the University of Bologna. She is Deputy President of EurAI (the European Association of Artificial Intelligence) and Executive Councillor of AAAI (the Association for the Advancements of Artificial Intelligence). She is Editor in Chief of the Constraint Journal, past Area Editor of INFORMS Journal on Computing and member of the Editorial Board of ACM Computing Surveys. Her research interests cover decision support and optimization systems merging techniques of constraint programming, operations research and machine learning. She is author of more than 150 papers on international conferences and journals. She has been the recipient of the Google Faculty Research Award on DeepOpt: Embedding deep networks in Combinatorial Optimization, and coordinated and participated to many EU projects and industrial collaborations.
Understanding the World with AI: Training and Validating AI Systems Using Synthetic Data
Abstract. The world around us is highly complex but Autonomous Systems must be able to reliably make accurate decisions that in many cases may even affect human lives. With Digital Reality we propose an approach that instead of only relying on real data, learns models of the real world and uses synthetic sensor data generated via simulations, for the training and -- even more importantly -- the validation of Autonomous Systems. This is extended by a continuous process of validating the models against the real world for improving and adapting them to a changing environment.
A highly relevant application of this approach is in intelligent sensor systems. Using a model about the object to be measured and the measuring process these systems are aware of what and how they are measuring and can adapt the measuring strategy and parameters accordingly, e.g. to obtain accurate measurements or target high throughput.
Short Bio. Philipp Slusallek is Scientific Director at the German Research Center for Artificial Intelligence (DFKI), where he heads the research area on Agents and Simulated Reality. At Saarland University he has been a professor for Computer Graphics since 1999, a principle investigator at the German Excellence‐Cluster on “Multimodal Computing and Interaction” since 2007, and was Director for Research at the Intel Visual Computing Institute 2009‐2017. Before coming to Saarland University, he was a Visiting Assistant Professor at Stanford University. He is associate editor of Computer Graphics Forum, a fellow of Eurographics, a member of acatech (German National Academy of Science and Engineering), and a member of the European High‐Level Expert Group on Artificial Intelligence. In addition, Prof. Slusallek co‐founded the European initiative CLAIRE (Confederation of Laboratories for Artificial Intelligence Research in Europe) in 2018.