Open Source Java Artificial Intelligence Software - Page 35

Macsy is a framework for developing modular agents. Data is organised in blackboards. Computations are performed by modules that annotate the data in the blackboards. Modules communicate indirectly through the annotations that they leave in the blackboards. The framework enables the development of decentralised software agents for a plethora of applications.

Ontology MApping FRAmework Toolkit allows to create semantic relations between two (source and target) ontologies, and apply such relations in translating source ontology instances into target ontology instances.

MAPONTO is an interactive tool for discovering semantic mappings between database schemas and ontologies as well as between different database schemas. The tool analyzes semantics in the input schemas and ontologies for generating semantic mappings.

MARGO (Multiattribute ARGumentation framework for Opinion explanation), written in Prolog, is the engine developed in the ArguGRID project for decision making.

The FlashServer is a part of the MASSim project of the Computational Intelligence Group at the TU Clausthal, Germany and his main task is the visualization of the games as Flash videos. Currently the FS is not in use during the Multi-Agent Contest.

The Macro Agent Transport Event-based Simulator (MATES) is intended to efficiently investigate the behavior of distributed agent based systems over mobile ad hoc networks (MANETs). MATES provides an expandable environment for agent-based system testing.

MATSim is a framework for building multi-agent transport simulations. MATSim has moved to GitHub: https://github.com/matsim-org/matsim Source code and newer releases are now hosted at GitHub!

MEM Net - Mote EMulator Network. This project will focus on: 1) MEM - Wireless Sensor Node (mote) emulator 2) MEM Net - network of emulated motes So far, the only released package is “visual-sim-slides”. More comming next !

META Spider is a neural network powered search engine which aim is to determine if a given raw information is relevant, using the experience of the active user of the hosting computer as a reference.

This application allow user to predict microemulsion region based on algorithm consisted of artificial neural networks and random forests. Those techniques combined create Decision Support System. Application was created as a part of project K/DSC/000033 subsidy for young researchers from Polish Ministry of Higher Education.

MIC* comes with a new vision of multi-agent system engineering: The multi-agent system is devided explicitly into two dimensions: the deployment environment and the autonomous agents. MIC* is an algebraic model of a deployment environment (container of a

The aim of MIEX (Metadata and Information Extractor from small XML documents) is to create a wrapper for the Stanford Parser, to extract and store metadata (syntactic structures, relationships among words...) from simple XML documents.

This is a toolkit for medical natural language processing (NLP). The core engine is general enough to be used in a variety of text processing domains, though the toolkit includes specific support for medical reports and patient de-identification.

ML@IUL is a class-project of the Machine Learning (ML) course at DCTI (Department of Computer Science and Technology), IUL (ISCTE - Lisbon University Institute). The objective is to create an ML library from student assignments.

A Machine Learning and Data Retrieval Framework

MMI - Tethys is a framework to facilitate interoperability among environmental systems. It facilitates creation of metadata, data and Web Services following standards. It is currently based on JAVA, AXIS and ANT.

The MOEA Framework is a free and open source Java library for developing and experimenting with multiobjective evolutionary algorithms (MOEAs) and other general-purpose multiobjective optimization algorithms. The MOEA Framework supports genetic algorithms, differential evolution, particle swarm optimization, genetic programming, grammatical evolution, and more. A number of algorithms are provided out-of-the-box, including NSGA-II, NSGA-III, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms.

The MRCPv2 protocol is designed to allow client devices to control media processing resources, such as speech recognition engines. MRCP4J provides a Java API that encapsulates the MRCPv2 protocol and can be used to implement MRCP clients and/or servers.

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>

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

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.

An interface to an artificially intelligent agent for playing Magic: The Gathering by Wizards of the Coast. And one or more agents for static or adaptive playing of the game against other artificial or human players.

An Java artificial intelligence platform comprising common AI tools (neural networks, genetic algorithms, wavelets, etc...) and examples. The platform focuses on ease of use and ease of integration..

Mandarax is a pure Java implementation of a rule engine. It supports mutiples types of facts and rules based on reflection, databases, EJB etc, supporting XML standards (RuleML 0.8). It provides a J2EE compliant inference engine using backward chaining.