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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:...

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.

Ellipsis is a modular, agent-oriented, and extendable toolkit to execute stationary and mobile software agents written in the Java programming language. 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...

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/ .

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.