Open Source Python Artificial Intelligence Software for BSD

The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! https://docs.opencv.org/master Books about the OpenCV are described here: https://opencv.org/books.html

FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA...

MSTParser is a non-projective dependency parser that searches for maximum spanning trees over directed graphs. Models of dependency structure are based on large-margin discriminative training methods. Projective parsing is also supported. mstparser 0.5.1 is now available via Maven Central. If you use Maven as your build tool, then you can add it as a dependency in your pom.xml file: <dependency> <groupId>net.sourceforge.mstparser</groupId> <artifactId>mstparser</artifactId> <version>0.5.1</version> </dependency>

VLE is a multi-modeling and simulation environment to study complex dynamic systems. VLE is based on the discrete event specification DEVS. and it implements the DSDE formalism (A merge of Dynamic Structure DEVS, DSDEVS, with Parallel DEVS, PDEVS). VLE provides a complete set of C++ libraries, called VFL (VLE Foundation Libraries), to develop DEVS models, to gets results of simulations, to launch simulation on cluster. The models can be developed with the DEVS formalism or with the classical mathematical formalism: Ordinary Differential Equation with Euler, Range-Kutta or QSS integrator, Finite state automaton (FDDEVS, UML State chart, Hybrid Petri net). The VLE environment provides an IDE to develop C++ models, DEVS coupled models. VLE have also three ports to use the VFL with Python, Java and R programming languages.

openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.

FramerD is a distributed semi-structured object database originally developed at MIT. It provides an internationalized Scheme-based scripting language, built-in text analysis tools, and special support for web scripting.

Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior. Researchers all over the world, both from the fields of artificial intelligence and cognitive science, are using Soar for a variety of tasks.

Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more.

InproTK is an Incremental Spoken Dialogue Processing Toolkit, that is, a toolkit to help you build dialogue systems that listen and talk incrementally, allowing for advanced interactional behaviour. Please see our Wiki for more information: http://sourceforge.net/p/inprotk/wiki/

Sakura is a Knowledge Navigator and User Interface for UNIX, which implements HyperMedia and its own windowing and packing system, both in the main program and in an extensive API for Tcl and other languages.

Comprehensive framework for delivering personalized travel services using agent infrastructure.

Voice Conference Manager uses VoiceXML and CCXML to control speech recognition, text to speech, and voice biometrics for a telephone conference service. Say the names or numbers of people and VCM places them into the call. Can be hosted on public servers

A.I. security app. Development ceased.

The goal of this project is to learn about and develop an AI. Current path is using a bot on AIM.

This project aims to build a full featured C++ (and possibly Python) library and associated tools which facilitate building Artificial Life simulations in a distributed environment.

DrPangloss is a python implementation of a three operator genetic algorithm, complete with a java swing GUI for running the GA and visualising performance, generation by generation

Ellogon is a multi-lingual, cross-platform, general-purpose language engineering environment.

Excors is a natural language interpretation system (chatterbot framework) that uses XML and Python for natural language modules.

HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.

JBoost is a simple, robust system for classification. JBoost contains implementations of several boosting algorithms in an alternating decision tree framework. In addition, JBoost provides extensible software for adding more learning algorithms.

JFIPA is intended to be a scalable, easy-to-deploy router and parser of messages between agents using the FIPA Agent Communication Language represented as XML

Jacomma is a framework for developing network-aware reactive information agents, using the ICM agent communication infrastructure. It offers transparent ICM bindings for java, and includes a lightweight agent execution environment.

jrgp is a strong-typed Genetic Programming system, which features a graphical interface (gool) to setup and run GP-problems and a tool (fs-d) that greatly simplifies the definition of a GP-problem.

Java based multi-agent platform built on an organizational model (agent, group, role). MadKit provides general agent facilities (lifecycle management, message passing, distribution, ...), and allows high heterogeneity in agents.

MutationFinder is a biomedical natural language processing (NLP) system for extracting mentions of point mutations from free text. MutationFinder achieves high performance (99% precision, 81% recall on blind test data) as an information extraction system