1,760 research outputs found
Towards Intelligent Databases
This article is a presentation of the objectives and techniques
of deductive databases. The deductive approach to databases aims at extending
with intensional definitions other database paradigms that describe
applications extensionaUy. We first show how constructive specifications can
be expressed with deduction rules, and how normative conditions can be defined
using integrity constraints. We outline the principles of bottom-up and
top-down query answering procedures and present the techniques used for
integrity checking. We then argue that it is often desirable to manage with
a database system not only database applications, but also specifications of
system components. We present such meta-level specifications and discuss
their advantages over conventional approaches
Relational Representations in Reinforcement Learning: Review and Open Problems
This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u
Graph Edit Distance Reward: Learning to Edit Scene Graph
Scene Graph, as a vital tool to bridge the gap between language domain and
image domain, has been widely adopted in the cross-modality task like VQA. In
this paper, we propose a new method to edit the scene graph according to the
user instructions, which has never been explored. To be specific, in order to
learn editing scene graphs as the semantics given by texts, we propose a Graph
Edit Distance Reward, which is based on the Policy Gradient and Graph Matching
algorithm, to optimize neural symbolic model. In the context of text-editing
image retrieval, we validate the effectiveness of our method in CSS and CRIR
dataset. Besides, CRIR is a new synthetic dataset generated by us, which we
will publish it soon for future use.Comment: 14 pages, 6 figures, ECCV camera ready versio
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
Data mining in soft computing framework: a survey
The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included
Thoughts about using Constraint Solvers in Action
SMT solvers power many automated security analysis tools today. Nevertheless, a smooth integration of SMT solvers into programs is still a challenge that lead to different approaches for doing it the right way. In this paper, we review the state of the art for interacting with constraint solvers. Based on the different ideas found in literature we deduce requirements for a constraint solving service simplifying the integration challenge. We identify that for some of those ideas, it is required to run large scale experiments for evaluating some of the ideas behind the requirements empirically. We show that the platform is capable of running such an experiment for the case of measuring the impacts of seeds on the solver runtime
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