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The majority of existing linear temporal logic (LTL) planning methods rely on the construction of a discrete product automaton, which combines a discrete abstraction of robot mobility and a Büchi automaton that captures the LTL specification. Representing this product automaton as a graph and using graph search techniques, optimal plans that satisfy the LTL task can be synthesized. However, constructing expressive discrete abstractions makes the synthesis problem computationally intractable. In this article, we propose a new sampling-based LTL planning algorithm that does not require any discrete abstraction of robot mobility. Instead, it incrementally builds trees that explore the product state-space, until a maximum number of iterations is reached or a feasible plan is found. The use of trees makes data storage and graph search tractable, which significantly increases the scalability of our algorithm. To accelerate the construction of feasible plans, we introduce bias in the sampling process, which is guided by transitions in the Büchi automaton that belong to the shortest path to the accepting states. We show that our planning algorithm, with and without bias, is probabilistically complete and asymptotically optimal. Finally, we present numerical experiments showing that our method outperforms relevant temporal logic planning methods. [ABSTRACT FROM AUTHOR]

The target of product attributes prediction is to complete the characteristics set for defining a particular product. Most of the existing methods treat the product attributes prediction as a Named-Entity Recognition (NER) problem from the products' affiliated data, such as product title and introduction. However, in a large number of industrial applications of Alibaba, we found that the existing methods are good at concrete attributes extraction (e.g., color, size) but short of abstract attributes extraction (e.g., applicable event). Moreover, these abstract attributes are usually not easy to extract from the products' affiliated data. In this paper, we propose a novel heterogeneous s tar graph a ttention n etwork called "SAN", which incorporates the advantages of multiple information in the e-commerce scene to predict the abstract attributes of products. Specifically, we model the customer interactive behaviors, product title and concrete attributes of a product as a star graph. Then, we extract the node features, node types and graph structure information from the heterogeneous star graph network which consists of n star graphs. By leveraging the parallel multiple attention mechanism, SAN can aggregate features and learn weights of nodes for product representation and abstract attribute prediction. Extensive experimental results of a real-world e-commerce dataset have demonstrated that SAN outperforms state-of-the-art methods significantly for product attributes prediction. These series of solutions are already planned for use in the applications of Alibaba. [ABSTRACT FROM AUTHOR]