Search Results for "tree"

A reactive runtime for building durable AI agents. Chidori is an open-source orchestrator, runtime, and IDE for building software in symbiosis with modern AI tools. When using Chidori, you author code with python or javascript, we provide a layer for interfacing with the complexities of AI models in long-running workflows. We have avoided the need for declaring a new language or SDK in order to provide these capabilities so that you can leverage software patterns that you are already...

...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/

MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values. This project is now hosted at: https://github.com/febo/myra

Java Behaviour Trees is a Java framework for easily building and running generic behaviour trees. Here, "behaviour tree" refers to the technique used to control the behaviour of characters in video games. IMPORTANT NOTE: our source code has been moved to GitHub. You will find it at: https://github.com/gaia-ucm/jbt Note however that all the binary files will still be distributed here at SourceForge.

Highly reusable and extensible Decision-Tree (Max-Gain) framework comprising of comprehensive input-processing and display functionality. Handles nominal, linear, continuous data. For preliminary description, refer - http://sushain.com/blog/archives/