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Build Agents and Models on One Platform
Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.
Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
A C++ library for machine learning within dynamic systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.
ERepSim presents an agent-based cloud simulation environment integrating electronic institutions from multiagent research to simulate Internet-of-Services systems.
ECSKernel is a multiagent coordination algorithm testbed, built on the RoboCupRescue disaster simulation platform. It is easily configurable and can be used with user-generated scenarios.
LabLOVE (Life On a Virtual Environment) is an evolutionary multi-agent simulation environment. It is fast, modular and extensible. Contains the reference implementation for the gridbrain algorithm.
RegMAS (Regional Multi Agent Simulator) is a spatially explicit multi-agent model framework, developed in C++ language and designed for long-term simulations of effects of government policies over agricultural systems (farm sizes, incomes, land use..).
TOAST (Trust Organisational Agent System Testbed) is a simulation framework used to evaluate and compare different trust models for agents embedded in organisational systems.
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Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples. It is also useful as way of exploring surprise theory. For more information see also: http://ilab.usc.edu/
The program system JCASim is a general-purpose system for simulating cellular automata in Java. It includes a stand-alone application and an applet for web presentations.
bioCity is a continuous space, discrete time simulation written in C++. It is meant to be of use to population biologists. Rules of behaviour are given to individuals that move, live, die, and procreate.
An extended version of RoboCup Rescue Simulation official viewer intended to make it more informative. It utilizes charts, statistics and some missing little useful features.
SynapseNN is a simulator for artificial neuronal networks. It can be used to solve concrete problems. Use it as a standalone-application or as a library from your own applications.
ARTIcomm (ARTIFICIAL communities) – project for developing communities of intelligent embodied agents with artificial neural networks using evolutionary algorithms.
An easy to extend, highly graphical, easy to use 2D robot simulator specialized for path planning algorithms. Can be used in testing various robotic algorithms, and already used for comparison of path planning algorithms like RRT, RRTConnect, PRM, RboT..
"Java Artificial Intelligence Markup Language PAD" is a tool that manages ProgramD AI (on local or remote machines) and AIML files with real-time previews and it provides a network support to test AI capabilities over many network protocols.
Little b is a Lisp-based language which allows scientists to build shareable, reusable mathematical models of complex systems based on shared parts. The initial focus is molecular and multicellular networks.
Project web page: http://www.littleb.org
This project about providing run-time support for developing Decentralized Autonomic Computing systems (Eclipse Innovation Grand). Currently the project contains a automatic guided vehicle (AGV) simulator and an editor and visualisation as eclipse plugin
Mito-MAS-m is a simulator of the mitochondrial inner membrane and the enzymatic complexes embedded in it, implementing a coarse-grained (CG) model of the molecules using rigid structures and Dissipative Particules Dynamics (DPD) as motion equation.
OpenDiscreteDynamicProgrammingTemplate : founds optimal constrainted parameters of a discrete controls with second order optimization template replacing Hessian with directional derivatives and backpropagation for digital filter(as neural network)
The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.