he workshop is focused on the problems of Stochastic Planning and Probabilistic Inference and the intimate connections between them. Both Planning and inference are core tasks in AI and the connections between them have been long recognized. However, much of the work in these subareas is disjoint. The last decade has seen many exciting developments with explicit constructions and reductions between planning and inference that aim for efficient algorithms for large scale problems and applications. The work in this area is distributed across many conferences, sub-communities, and sub-topics and varies from discrete to continuous problems, single versus multiagent problems, general versus spatial problems, propositional versus relational problems, model based planning versus reinforcement learning, and exact/optimal versus approximate versus heuristic solutions. Applications similarly vary for example from scheduling to sustainability and to robot control.The goal of this workshop is to bring together researchers from all these areas and facilitate synergy and exchange of ideas: to discuss core ideas, techniques and algorithms that take advantage of the connection between planning and inference, identify opportunities and challenges for future work, and explore applications and how they can inform the development of such work.The workshop will include invited talks by experts on planning and inference, contributed talks and a poster session, leaving room for discussion and interaction among participants. Current invited speakers include Rina Dechter, Marc Toussaint, and Pascal Van Hentenryck.The workshop topic is broad and the intention of this first workshop is to enable interaction among the various sub-areas while keeping the focus on the interaction between planning and inference. Some basic questions include the following:What are effective reductions from planning to inference?What are effective inference algorithms for such problems?What are the challenges in planning applications, and how does their structure help or interfere with the application of planning as inference?Can generic inference algorithms be used directly for planning? or are we better off tailoring algorithms directly to the planning problem?Can planning algorithms or ideas developed for them be used for general inference?How do structured solutions, for example, lifted inference, lifted planning, spatial MDPs, cooperative multiagent systems, and approximations in continuous problems, translate across the planning/inference spectrum, and help improve scalability.Success stories and challenges in using planning for inference or inference for planning.These questions cut across theoretical foundations and practical applications.