StarAI 2018
Eighth International Workshop on Statistical Relational AI
Eighth International Workshop on Statistical Relational AI
The purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from two fields: logical (or relational) AI and probabilistic (or statistical) AI. These fields share many key features and often solve similar problems and tasks. Until recently, however, research in them has progressed independently with little or no interaction. The fields often use different terminology for the same concepts and, as a result, keeping-up and understanding the results in the other field is cumbersome, thus slowing down research. Our long term goal is to change this by achieving a synergy between logical and statistical AI. As a stepping stone towards realizing this big picture view on AI, we are organizing the Eighth International Workshop on Statistical Relational AI at the 27th International Joint Conference on Artificial Intelligence (IJCAI) in Stockholm, on July 14th 2018.
StarAI will be a one day workshop with short paper presentations, a poster session, and three invited speakers.
Authors should submit either a full paper reporting on novel technical contributions or work in progress (AAAI style, up to 7 pages excluding references), a short position paper (AAAI style, up to 2 pages excluding references), or an already published work (verbatim, no page limit, citing original work) in PDF format via EasyChair. All submitted papers will be carefully peer-reviewed by multiple reviewers and low-quality or off-topic papers will be rejected. Accepted papers will be presented as a short talk and poster.
Abstract:Deep reinforcement learning has many exciting successes but people typically require much less experience to learn to make good decisions and obtain a high reward policy. We have been focusing on sample efficient methods for leveraging old data and strategic exploration methods to enable agents to quickly learn to make good decisions. In this talk I will describe some of our recent efforts in this space, including new work on perhaps the hardest Atari game, Pitfall.
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Abstract: StaRAI models combine the powerful formalisms of probability theory and first-order logic to handle uncertainty in large, complex problems. While they provide a very effective representation paradigm due to their succinctness and parameter sharing, efficient learning is a significant problem in these models. First, I will discuss state-of-the-art learning method based on boosting that is representation independent. Our results demonstrate that learning multiple weak models can lead to a dramatic improvement in accuracy and efficiency.
One of the key attractive properties of StaRAI models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. However, in current StaRAI research, the human is restricted to either being a mere labeler or being an oracle who provides the entire model. I will present our recent work that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. Finally, I will discuss our work on soliciting advice from humans as needed that allows for seamless interactions with the human expert.
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Abstract: Computational sustainability is a new interdisciplinary research field with the overarching goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from wildlife conservation and biodiversity, to poverty mitigation, to materials discovery for renewable energy materials. I will also highlight cross-cutting computational themes and challenges for AI at the intersection of constraint reasoning, optimization, machine learning, citizen science, and crowd-sourcing.
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StarAI is currently provoking a lot of new research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful StarAI tools will enable new applications in several large, complex real-world domains including those involving big data, social networks, natural language processing, bioinformatics, the web, robotics and computer vision. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to effectively handle the former while probability helps her effectively manage the latter. We seek to invite researchers in all subfields of AI to attend the workshop and to explore together how to reach the goals imagined by the early AI pioneers.
The focus of the workshop will be on general-purpose representation, reasoning and learning tools for StarAI as well as practical applications. Specifically, the workshop will encourage active participation from researchers in the following communities: satisfiability (SAT), knowledge representation (KR), constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP), graphical models and probabilistic reasoning (UAI), statistical learning (NIPS, ICML, and AISTATS), graph mining (KDD and ECML PKDD) and probabilistic databases (VLDB and SIGMOD). It will also actively involve researchers from more applied communities, such as natural language processing (ACL and EMNLP), information retrieval (SIGIR, WWW and WSDM), vision (CVPR and ICCV), semantic web (ISWC and ESWC) and robotics (RSS and ICRA).
Previous StarAI workshops were held in conjunction with AAAI 2010, UAI 2012, AAAI 2013, AAAI 2014, UAI 2015, and IJCAI 2016, UAI 2017 and were among the most popular workshops at the conferences.