Risk Assessment Algorithms Based On Recursive Neural Networks

The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety system

Banach-Mazur Games with Simple Winning Strategies

We discuss several notions of "simple" winning strategies for Banach-Mazur games on graphs, such as positional strategies, move-counting or length-counting strategies, and strategies with a memory based on finite appearance records (FAR). We investigate classes of Banach-Mazur games that are determined via these kinds of winning strategies. Banach-Mazur games admit stronger determinacy results than classical graph games. For instance, all Banach-Mazur games with omega-regular winning conditions are positionally determined. Beyond the omega-regular winning conditions, we focus here on Muller conditions with infinitely many colours. We investigate the infinitary Muller conditions that guarantee positional determinacy for Banach-Mazur games. Further, we determine classes of such conditions that require infinite memory but guarantee determinacy via move-counting strategies, length-counting strategies, and FAR-strategies. We also discuss the relationships between these different notions of determinacy

Viable Dependency Parsing as Sequence Labeling

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.Comment: Camera-ready version to appear at NAACL 2019 (final peer-reviewed manuscript). 8 pages (incl. appendix

Parity Games on Undirected Graphs

International audienceWe examine the complexity of solving parity games in the special case when the underlying game graph is undirected. For strictly alternating games, that is, when the game graph is bipartite between the nodes of the two players, we observe that the solution can be computed in linear time. In contrast, when the assumption of strict alternation is dropped, we show that the problem is as hard in the undirected case as it is in the general, directed, case