Towards Linguistically-Grounded Spatial Logics

We propose a method to analyze the amount of coverage and adequacy of spatial calculi by relating a calculus to a linguistic ontology for space by using similarities and linguistic corpus data. This allows evaluating whether and where a spatial calculus can be used for natural language interpretation. It can also lead to \u27more appropriate\u27 spatial logics for spatial language

Don’t fail me! The Level 5 Autonomous Driving Information Dilemma regarding Transparency and User Experience

Autonomous vehicles can behave unexpectedly, as automated systems that rely on data-driven machine learning have shown to infer false predictions or misclassifications, e.g., due to stickers on traffic signs, and thus fail in some situations. In critical situations, system designs must guarantee safety and reliability. However, in non-critical situations, the possibility of failures resulting in unexpected behaviour should be considered, as they negatively impact the passenger’s user experience and acceptance. We analyse if an interactive conversational user interface can mitigate negative experiences when interacting with imperfect artificial intelligence systems. In our quantitative interactive online survey (N=113) and comparative qualitative Wizard of Oz study (N=8), users were able to interact with an autonomous SAE level 5 driving simulation. Our findings demonstrate that increased transparency improves user experience and acceptance. Furthermore, we show that additional information in failure scenarios can lead to an information dilemma and should be implemented carefully

Natural Language meets Spatial Calculi

Abstract. We address the problem of relating natural language descriptions of spatial situations with spatial logical calculi, focusing on projective terms (orientations). We provide a formalism based on the theory of E-connections that connects natural language and spatial calculi. Semantics of linguistic expressions are specified in a linguistically motivated ontology, the Generalized Upper Model. Spatial information is specified as qualitative spatial relationships, namely orientations from the double-cross calculus. This linguistic-spatial connection cannot be adequately formulated without certain contextual, domain-specific aspects. We therefore extend the framework of E-connections twofold: (1) external descriptions narrow down the class of intended models, and (2) context-dependencies inherent in natural language descriptions feed back into the representation finite descriptions of necessary context information.

Machine learning for interpretation of spatial natural language in terms of QSR

Abstract. Computational approaches in spatial language understanding distinguish and use dierent aspects of spatial and contextual information. These aspects comprise linguistic grammatical features, qualitative formal representations, and situational context-aware data. We apply formal models and machine learning techniques to map spatial semantics in natural language to qualitative spatial representations. In particular, we investigate whether and how well linguistic features can be classied, automatically extracted, and mapped to region-based qualitative relations using corpus-based learning. We structure the problem of spatial language understanding into two parts: i) extracting parts of linguistic utterances carrying spatial information, and ii) mapping the results of the rst task to formal spatial calculi. In this paper we focus on the second step. The results show that region-based spatial relations can be learned to a high degree and are distinguishable on the basis of dierent linguistic features.status: publishe

Learning to interpret spatial natural language in terms of qualitative spatial relations

Computational approaches in spatial language understanding nowadays distinguish and use different aspects of spatial and contextual information. These aspects comprise linguistic grammatical features, qualitative formal representations, and situational context-aware data. In this chapter, we apply formal models and machine learning techniques to map spatial semantics in natural language to qualitative spatial representations. In particular, we investigate whether and how well linguistic features can be classified and automatically extracted and mapped to region-based qualitative relations using corpus-based learning. We separate the challenge of spatial language understanding into two tasks: (i) we identify and automatically extract those parts from linguistic utterances that provide specifically spatial information, and (ii) we map the extracted parts that result from the first task to qualitative spatial representations. In this chapter, we present both tasks and we particularly discuss experimental results of the second part of mapping linguistic features to qualitative spatial relations. Our results show that region-based spatial relations can indeed be learned to a high degree and that they are distinguishable on the basis of different linguistic features.status: publishe