The Intelligent Web

Many people are working on the Semantic Web with the main objective being to enhance web searches. Our proposal is a new research strategy based on the existence of a discrete set of semantic relations for the creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper (Álamo, Martínez, Jaén) the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective of developing automatic answering systems possible as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic production of web pages or automatic e-learning generation

Style Transfer and Extraction for the Handwritten Letters Using Deep Learning

How can we learn, transfer and extract handwriting styles using deep neural networks? This paper explores these questions using a deep conditioned autoencoder on the IRON-OFF handwriting data-set. We perform three experiments that systematically explore the quality of our style extraction procedure. First, We compare our model to handwriting benchmarks using multidimensional performance metrics. Second, we explore the quality of style transfer, i.e. how the model performs on new, unseen writers. In both experiments, we improve the metrics of state of the art methods by a large margin. Lastly, we analyze the latent space of our model, and we see that it separates consistently writing styles.Comment: Accepted in ICAART 201

Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019

Scaling Monte Carlo Tree Search on Intel Xeon Phi

Many algorithms have been parallelized successfully on the Intel Xeon Phi coprocessor, especially those with regular, balanced, and predictable data access patterns and instruction flows. Irregular and unbalanced algorithms are harder to parallelize efficiently. They are, for instance, present in artificial intelligence search algorithms such as Monte Carlo Tree Search (MCTS). In this paper we study the scaling behavior of MCTS, on a highly optimized real-world application, on real hardware. The Intel Xeon Phi allows shared memory scaling studies up to 61 cores and 244 hardware threads. We compare work-stealing (Cilk Plus and TBB) and work-sharing (FIFO scheduling) approaches. Interestingly, we find that a straightforward thread pool with a work-sharing FIFO queue shows the best performance. A crucial element for this high performance is the controlling of the grain size, an approach that we call Grain Size Controlled Parallel MCTS. Our subsequent comparing with the Xeon CPUs shows an even more comprehensible distinction in performance between different threading libraries. We achieve, to the best of our knowledge, the fastest implementation of a parallel MCTS on the 61 core Intel Xeon Phi using a real application (47 relative to a sequential run).Comment: 8 pages, 9 figure

Quantum Probability and Operant Conditioning: Behavioral Uncertainty in Reinforcement Learning

An implicit assumption in the study of operant conditioning and reinforcement learning is that behavior is stochastic, in that it depends on the probability that an outcome follows a response and on how the presence or absence of the output affects the frequency of the response. In this paper we argue that classical probability is not the right tool to represent uncertainty operant conditioning and propose an interpretation of behavioral states in terms of quantum probability instead