The emergence of Large Language Models (LLMs) has revolutionized many fields, not only traditional natural language processing (NLP) tasks. Recently, research on applying LLMs to the database field has been booming, and as a typical non-relational database, the use of LLMs in graph database research has naturally gained significant attention. Recent efforts have increasingly focused on leveraging LLMs to translate natural language into graph query language (NL2GQL). Although some progress has been made, these methods have clear limitations, such as their reliance on streamlined processes that often overlook the potential of LLMs to autonomously plan and collaborate with other LLMs in tackling complex NL2GQL challenges. To address this gap, we propose NAT-NL2GQL, a novel multi-agent framework for translating natural language to graph query language. Specifically, our framework consists of three synergistic agents: the Preprocessor agent, the Generator agent, and the Refiner agent. The Preprocessor agent manages data processing as context, including tasks such as name entity recognition, query rewriting, path linking, and the extraction of query-related schemas. The Generator agent is a fine-tuned LLM trained on NL-GQL data, responsible for generating corresponding GQL statements based on queries and their related schemas. The Refiner agent is tasked with refining the GQL or context using error information obtained from the GQL execution results. Given the scarcity of high-quality open-source NL2GQL datasets based on nGQL syntax, we developed StockGQL, a dataset constructed from a financial market graph database. It is available at: https://github.com/leonyuancode/StockGQL. Experimental results on the StockGQL and SpCQL datasets reveal that our method significantly outperforms baseline approaches, highlighting its potential for advancing NL2GQL research.

This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks. To set the stage for this sangam, we explore two different flavors of model space problems that have been studied in the AI planning literature and explore the effect of an LLM on those tasks. We empirically demonstrate how the performance of an LLM contrasts with combinatorial search (CS) - an approach that has been traditionally used to solve model space tasks in planning, both with the LLM in the role of a standalone model space reasoner as well as in the role of a statistical signal in concert with the CS approach as part of a two-stage process. Our experiments show promising results suggesting further forays of LLMs into the exciting world of model space reasoning for planning tasks in the future.

Gluon and Apache MXNet have been great for deep learning, especially for newbies like me. They added a Deep Learning Toolkit that is easy to use and has a number of great pre-trained models that you can easily use to do some general use cases around computer vision. So, I have used a simple well-documented example that I tweaked to save the final image and send some JSON details via MQTT to Apache NiFi. GluonCV makes this even easier! Again, let's take a simple Python example, tweak it, run it via a shell script, and send the results over MQTT.

As we all know Google has open-sourced a library called TensorFlow that can be used in Android for implementing Machine Learning. TensorFlow is an open-source software library for Machine Intelligence provided by Google. I searched the internet a lot but did not find a simple way or a simple example to build TensorFlow for Android. After going through many resources, I was able to build it. Then, I decided to write on it so that it would not take time for others.

As a system scientist doing modeling and simulation, I have been interested for some time in ways that modeling and simulation and AI could be of value to each other. After all, both areas have their roots in putting knowledge into useful representations. I have speculated (AI Magazine, summer 1989, pp. With respect to breadth of coverage and potential readership, Artificial Intelligence, Simulation, and Modeling does provide a broad survey of current research, but it is written from an AI perspective and will find a greater readership among AI researchers than simulationists. Mark E. Lacy is manager of computational The cover to Expert Systems in Business: A Practical Approach by Michael L. Barrett and Annabel C. Beerel (Ellis Horwood Limited, Chichester, England, 1988, 259 pages, $36.95, ISBN O-7458-0269-9) contains an abstract design in colors of violet, brilliant green, and dark magenta.

As a system scientist doing modeling and simulation, I have been interested for some time in ways that modeling and simulation and AI could be of value to each other. After all, both areas have their roots in putting knowledge into useful representations. I have speculated (AI Magazine, summer 1989, pp. With respect to breadth of coverage and potential readership, Artificial Intelligence, Simulation, and Modeling does provide a broad survey of current research, but it is written from an AI perspective and will find a greater readership among AI researchers than simulationists. Mark E. Lacy is manager of computational The cover to Expert Systems in Business: A Practical Approach by Michael L. Barrett and Annabel C. Beerel (Ellis Horwood Limited, Chichester, England, 1988, 259 pages, $36.95, ISBN O-7458-0269-9) contains an abstract design in colors of violet, brilliant green, and dark magenta.

As a system scientist doing modeling and simulation, I have been interested for some time in ways that modeling and simulation and AI could be of value to each other. After all, both areas have their roots in putting knowledge into useful representations. I have speculated (AI Magazine, summer 1989, pp. With respect to breadth of coverage and potential readership, Artificial Intelligence, Simulation, and Modeling does provide a broad survey of current research, but it is written from an AI perspective and will find a greater readership among AI researchers than simulationists. Mark E. Lacy is manager of computational The cover to Expert Systems in Business: A Practical Approach by Michael L. Barrett and Annabel C. Beerel (Ellis Horwood Limited, Chichester, England, 1988, 259 pages, $36.95, ISBN O-7458-0269-9) contains an abstract design in colors of violet, brilliant green, and dark magenta.

"Common sense is a wild thing, savage, and beyond rules." The "Naive Physics Manifesto" of Pat Hayes (1978) proposes a large-scale project to develop a formal theory encompassing the entire knowledge of physics of naive reasoners, expressed in a declarative symbolic form. The theory is organized in clusters of closely interconnected concepts and axioms. More recent work on the representation of commonsense physical knowledge has followed a somewhat different methodology. The goal has been to develop a competence theory powerful enough to justify commonsense physical inferences, and the research is organized in microworlds, each microworld covering a small range of physical phenomena.

Recent advances in enterprise web-based software have created a need for sophisticated yet user-friendly data-quality solutions. A new category of data-quality solutions that fill this need using intelligent matching and retrieval algorithms is discussed. Solutions are focused on customer and sales data and include realtime inexact search, batch processing, and data migration. Users are empowered to maintain higher quality data resulting in more efficient sales and marketing operations. Sales managers spend more time with customers and less time managing data.

We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5-6 years. We show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. We also present AI technologies, implemented in table and algorithm search, that are special search modes in CiteSeerX. While it is challenging to rebuild a system like Cite-SeerX from scratch, many of these AI technologies are transferable to other digital libraries and search engines.