We present an approach for acquiring common sense knowledge from social interaction. We argue that social common sense should be learned from daily interactions using implicit user's feedbacks and requires shared understanding of social situations. A service-oriented architecture, inspired from cognitive science, that foster mutual understanding between a smart environment and its inhabitants is presented. The method makes use of ConceptNet to work with common sense knowledge. We are able to successfully use and learn common sense knowledge.

In this paper, we propose a novel human-robot-interaction framework for the purpose of rapid visual scene understanding. The task of the robot is to correctly enumerate how many separate objects there are in the scene and to describe them in terms of their attributes. Our approach builds on top of a state-of-the-art 3D segmentation method segmenting stereo reconstructed point clouds into object hypotheses and combines it with a natural dialog system. By putting a `human in the loop', the robot gains knowledge about ambiguous situations beyond its own resolution. Specifically, we are introducing an entropy-based system to spot the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-to-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.

The aim of this paper is to revisit the fundamental requirements for bulding computational models for Interactive Narrative. We express the need for broader computational models of narrative and underline the fundamental difference between models for story generation and models for Interactive Narrative. Research directions are finally sketched to move towards dedicated computational models for Interactive Narrative.

This paper describes results from a large-scale, complex human study using non-facial and non-verbal affect for victim management in robot-assisted Urban Search and Rescue Applications. Statistically significant results are presented that indicate participants felt emotive robots were more calming, friendlier, and attentive.

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

The paper focuses on how a model could be learnt for determining at runtime how much of spoken input needs to be understood, and what configuration of processes can be expected to yield that result. Typically, a dialogue system applies a fixed configuration of shallow and deep forms of processing to its input. The configuration tries to balance robustness with depth of understanding, creating a system that always tries to understand as well as it can. The paper adopts a different view, assuming that what needs to be understood can vary per context. To facilitate this any-depth processing, the paper proposes an approach based on learnable controllers. The paper illustrates the main ideas of the approach on examples from a robot acquiring situated dialogue competence, and a robot working with users on a task.

Cochairs and Program Committee for the AAAI 2010 Symposium on Dialog with Robots.

A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.

This paper addresses the numerical estimation of the principal curvature of pattern transformation manifolds. When a visual pattern undergoes a geometric transformation, it forms a (sub)manifold in the ambient space, which is usually called the transformation manifold. The manifold curvature is an important property characterizing the manifold geometry, with several applications in manifold learning. We propose an efficient numerical algorithm for estimating the principal curvature at a certain point on the transformation manifold.