We present an implemented approach to transform natural language sentences into SPARQL, using background knowledge from ontologies and lexicons. Therefore, eligible technologies and data storage possibilities are analyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required. Resulting in an architectural approach based on automatic SPARQL query construction for effective natural language queries. Secondly, we analyze the performance of RDBMS, RDF and Triple Stores for the knowledge representation. The proposed approach will be evaluated on the basis of a query catalog by means of query efficiency, accuracy, and data storage performance. The results show, that natural language access to industry data using ontologies and lexicons, is a simple but effective approach to improve the diagnosis process and the data search for a broad range of users. Furthermore, virtual RDF graphs do support the DB-driven knowledge graph representation process, but do not perform efficient under industry conditions in terms of performance and scalability.

We describe a novel system for discovering and characterizing emerging events. We define event emergence to be a developing situation comprised of a series of sub-events. To detect sub-events from a very large, continuous textual input stream, we use two techniques: (1) frequency-based detection of sub-events that are potentially entailed by an emerging event; and (2) anomaly-based detection of other sub-events that are potentially indicative of an emerging event. Identifying emerging events from detected sub-events involves connecting sub-events to each other and to the relevant emerging events within the event models and estimating the likelihood of possible emerging events. Each sub-event can be part of a number of emerging events and supports various event models to varying degrees. We adopt a coherent and compact model that probabilistically identifies emerging events. The innovative aspect of our work is a well-defined framework where statistical Big Data techniques are informed by event semantics and inference techniques (and vice versa). Our work is strongly grounded in semantics and knowledge representation, which enables us to produce more reliable results than would otherwise be possible with a purely statistical approach.

In this paper, we briefly address a research regarding how to objectively evaluate machine-based object similarity measures by human-based estimation. Based on a novel approach for similarity measure of 3-D objects we create a ground truth of 3-D objects and their similarities estimated by humans. The automatic similarity results achieved are evaluated against this ground truth in terms of precision and recall in an object retrieval scenario. To further illustrate the reciprocity properties between machine and human perception, we compare the similarities achieved by both on testing data and show how it can be used to address other problems and formulations.

We all share the same world, but are free to formulate and argue for our own interpretations of this shared reality. For different agents will grant differing degrees of importance to the same facts and norms. We cannot experiment on human cultures the way scientists experiment on cell cultures, but we can construct thought experiments that imagine the consequences of otherwise impossible changes. Successful thought experiments do not change the world, but change the way we see the world. This paper describes Gedanken-style reasoning in an AI system that allows a computer to understand, or at least speculate on, the surprising causal interactions between apparently unrelated concepts. This system ponders alternate worlds in which the amount of a conceptual ingredient [X] is increased or decreased, to see what unexpected and apparently incongruous effects might arise from this change. Our goal is to construct a creative generator of novel what-if scenarios that can be used in the generation of perspective-shaping stories, poems and jokes.

In this paper we explain how IETAL agents learn their environment, and how they build their intrinsic, internal representation of it, which they then use to build their expectations when on quest to satisfy its active drives. As environments change (with or without other agents present in them), the agents learn to new and “forget” irrelevant, “old” associations made. We discuss the concept of emotional context of associations, and show a gallery of simulations of behaviors in small multiagent societies.

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.

We propose a learning agent that first learns concepts in an integrated, cross-modal manner, and then uses these as the semantics model to map language. We consider an abstract model for the action of throwing, modeling the entire trajectory. From a large set of throws, we take the trajectory images and and the throwing parameters. These are mapped jointly onto a low-dimensional non-linear manifold. Such models improve with practice, and can be used as the starting point for real-life tasks such as aiming darts or recognizing throws by others. How can such models can be used in learning language? We consider a set of videos involving throwing and rolling actions. These actions are analyzed into a set of contrastive semantic classes based on agent, action, and the thrown object (trajector). We obtain crowdsourced commentaries for these videos (raw text) from a number of adults. The learner attempts to associate labels using contrastive probabilities for the semantic class. Only a handful of high-confidence words are found, but the agent starts off with this partial knowledge. These are used to learn incrementally larger syntactic patterns, initially for the trajector, and eventually for full agent-trajector-action sentences. We demonstrate how this may work for two completely different languages - English and Hindi, and also show how rudiments of agreement, synonymy and polysemy are detected.

We present a system that produces emotionally rich poetry inspired by personalized and empathic interpretation of text, particularly Internet blogs. Our implemented system is based on the blackboard architecture, and generates poetry from a theme that it considers the most inspiring. It also incorporates a model of emotions with an individual optimism rate that defines an affective state. The poems produced by the system contain emotional expressions that describe these feelings. We explain how the system re-conceptualizes the text by the empathic interpretation of its content. We also present how the blackboard architecture may support divergent problem solving in the field of computational creativity.We describe the system architecture and the generation algorithm followed by some illustrative results. Finally, we mention possible continuation of this work by incorporating other language generating systems as well as human experts in the blackboard architecture.

We are interested in the manipulation skills required by future service robots performing everyday tasks such as preparing food and cleaning in a typical home environment. Such robots must have a robust set of skills that can be applied in the unpredictable and varying circumstances that arise in everyday life.To succeed in such a setting, a service robot must have a strong ability to transfer old skills to new varied settings. We are inspired by the strong transfer ability demonstrated by infants and toddlers on simple manipulation activities, and we are motivated to try and replicate these abilities in an artificial system.We treat this as a problem of making analogies, and describe a theoretical framework which could account for it. We sketch the ideas of a computational model for implementing the required analogical reasoning.

In this summary report I give an account of research conducted over the last two years, showing the suitability and the advantages of applying computational analogy-engines in the analysis and design of analogy-based methods and tools in teaching and education. This overview constitutes the conclusion of the first phase of a multi-stage effort trying to introduce computational models of analogy also to education and the learning sciences, thus opening up these fields to computational tools and methods not only on an instrumental level, but also in analytical, conceptual, and design-oriented studies. I locate the "analogy-engines in the classroom" research program within the bigger schemes of studying human creativity and computational creativity, provide an introduction to the theoretical underpinnings of the endeavor, and revisit three worked out case studies serving as proofs of the feasibility of the overall approach.