Search Results for "xray-core"

LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. ...

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/

The project has moved to github https://github.com/EnFlexIT/AgentWorkbench Agent.GUI is a simulation framework and toolkit based on the JADE framework. It provides functionalities for time aspects, agent-environment interaction, visualization and load balancing, Furthermore, the included application focuses the usability for end users.

...The world has an amount of energy that the user may use to create Actors and components while Actors may also create other Actors from their own energy. The code is structured in several layers: - april-core simply defines the library and the objects - april-gui builds on the concepts in april-core, giving a visual interpretation to the concepts - AprilDream is a GUI program that uses april-gui objects to display a World in evolution. The code base also contains a test suite based on [Google test](http://code.google.com/p/googletest/) and a set of ever-growing commented examples.

...Ellipsis uses the standard application server JBoss (version 4.2.3 GA) without any modifications. In contrast to existing mobile agent toolkits, Ellipsis has an agent- and message-oriented architecture. Ellipsis is built on top of the JMX architecture. Core Ellipsis components are implemented as MBeans which provides for basic ser-vices to execute agents and control their life-cycle. One of the core components is a special MBean (AgentModul) that makes it possible to load an agent powered by agent engines. Agent engines can execute one type of agent. Based on this mechanism, agents can be added dynamically to a running system, can be stopped (for example in case of an error) and restarted later. ...

nu-BDI is an extension of the Jason agent interpreter aimed at processing norms using constraints on the agent's instantiated plans. The core agent reasoning cycle now includes a component for processing norms and enforcing norm constraints.

Java JNI API for morphological analysis of Russian. Note: C++ JNI code and the core code is at the project http://sourceforge.net/projects/seman/ .

Torch5 provides a Matlab-like environment for state-of-the-art machine learning algorithms. This project aims at gathering all contributed packages for Torch5, which are not part of the Torch5 core.

PIQLE is a Platform Implementing Q-LEarning (and other Reinforcement Learning) algorithms in JAVA. Version 2 is a major refactoring. The core data structures and algorithms are in piqle-coreVersion2. Examples are in piqle-examplesVersion2. A complete doc

This project intends to create an indexing search engine, for knowledge management. The primary object is to apply an information retrieval core. And implement a knowledge data discovery theory such as data mining algorithm, text mining.

SB-MASE is aimed at students and researchers in AI, for simulation and control of agents and robots. It uses task based decomposition and the subsumption architecture as its core control mechanisms and includes numerous possibilities for adding plug-ins.