1,720,958 research outputs found
Enhancing Deep Sequence Generation with Logical Temporal Knowledge
Full text linkDespite significant advancements in deep learning for sequence forecasting, neural models are typically trained only on data, and the incorporation of high-level prior logical knowledge in their training is still an hard challenge. This limitation hinders the exploitation of background knowledge, such as common sense or domain-specific information, in predictive tasks performed by neural networks. In this work, we propose a principled approach to integrate prior knowledge in Linear Temporal Logic over finite traces (\ltlf) into deep autoregressive models for multistep symbolic sequence generation (i.e., suffix prediction) at training time. Our method involves representing logical knowledge through continuous probabilistic relaxations and employing a differentiable schedule for sampling the next symbol from the network. We test our approach on synthetic datasets based on background knowledge in Declare, inspired by Business Process Management (BPM) applications. The results demonstrate that our method consistently improves the performance of the neural predictor, achieving lower Damerau-Levenshtein (DL) distances from target sequences and higher satisfaction rates of the logical knowledge compared to models trained solely on data
Automated Database Indexing using Model-free Reinforcement Learning
Full text linkConfiguring databases for efficient querying is a complex task, often carried
out by a database administrator. Solving the problem of building indexes that
truly optimize database access requires a substantial amount of database and
domain knowledge, the lack of which often results in wasted space and memory
for irrelevant indexes, possibly jeopardizing database performance for querying
and certainly degrading performance for updating. We develop an architecture to
solve the problem of automatically indexing a database by using reinforcement
learning to optimize queries by indexing data throughout the lifetime of a
database. In our experimental evaluation, our architecture shows superior
performance compared to related work on reinforcement learning and genetic
algorithms, maintaining near-optimal index configurations and efficiently
scaling to large databases.Comment: 8 pages, 5 figures (some have subfigures), 1 tabl
Markov abstractions for PAC reinforcement learning in non-Markov decision processes
Full text linkOur work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of states while preserving the dynamics. We call it a Markov abstraction since it induces a Markov Decision Process over a set of states that encode the non-Markov dynamics. This phenomenon underlies the recently introduced Regular Decision Processes (as well as POMDPs where only a finite number of belief states is reachable). In all such kinds of decision process, an agent that uses a Markov abstraction can rely on the Markov property to achieve optimal behaviour. We show that Markov abstractions can be learned during reinforcement learning. Our approach combines automata learning and classic reinforcement learning. For these two tasks, standard algorithms can be employed. We show that our approach has PAC guarantees when the employed algorithms have PAC guarantees, and we also provide an experimental evaluation
Smart Makerspace: A Web Platform Implementation
Full text linkMakerspaces are creative and learning environments, home to activities such as fabrication processes and Do-It-Yourself (DIY) tasks. However, containing equipment that are not commonly seen or handled, these spaces can look rather challenging to novice users. This paper is based on the Smart Makerspace research from Autodesk, which uses a smart workbench for an immersive instructional space for DIY tasks. Having its functionalities in mind and trying to overcome some of its limitations, we approach the concept building an immersive instructional space as a web platform. The platform, introduced to users in a makerspace, had a feedback that reflects its potential between novice and intermediate users, for creating facilitators and encouraging these users.</jats:p
SmartIX: A database indexing agent based on reinforcement learning
Full text linkConfiguring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. In this paper, we develop the SMARTIX architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. We train and evaluate SMARTIX performance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows that SMARTIX converges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work
Using self-attention LSTMs to enhance observations in goal recognition
No full textGoal recognition is the task of identifying the goal an observed agent is pursuing. The quality of its results depends on the quality of the observed information. In most goal recognition approaches, the accuracy significantly decreases in settings with missing observations. To mitigate this issue, we develop a learning model based on LSTMs, leveraging attention mechanisms, to enhance observed traces by predicting missing observations in goal recognition problems. We experiment using a dataset of goal recognition problems and apply the model to enhance the observation traces where missing. We evaluate the technique using a state-of-the-art goal recognizer in four different domains to compare the accuracy between the standard and the enhanced observation traces. Experimental evaluation shows that recurrent neural networks with self-attention mechanisms improve the accuracy metrics of state-of-the-art goal recognition techniques by an average of 60%
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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