Search Results for "artificial" - Page 6
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Showing 254 open source projects for "artificial"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Brainlab is a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator (NCS).
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Extensible framework that enables productive design, analysis, and execution of arbitrary-sized neural-networks or system on a distributed, scalable, high-throughput runtime platform. Enables synapse-oriented-programming.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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QASE is a Java-based API designed to provide all the functionality needed to create game agents in Quake 2. Powerful enough to facilitate high-end research, it is also suitable for undergrad courses geared towards classic AI and agent-based systems.
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The ART testbed program allows researchers to compare their work from a practical point of view in the modelling of trust in agent societies.
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LEET (Large Experiment and Evaluation Tool) is a front-end software utility for WEKA that simplifies large-scale experiment and evaluation of algorithms and datasets in the classification context.
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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The RTSCup is a programming environment for RTS games which can be used as a benchmark for evaluating several AI techniques. It is designed to make it easier and more intuitive for researchers to produce their applications over this plataform.
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A platform for setting up autonomic services in a distributed environment. Provides service discovery, service provisioning / usage, autonomic adaptation to the context, mobility, support for supervision and service aggregation, and more.
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The Mobile Autonomous Robot Simulation Framework assists in building the applications to simulate an Autonomous Mobile Robot, its interaction with the environment, behaviors and also its sensors, actuators and locomotion mechanism.
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Java package to study a clustering model described in the paper \"Novel Clustering Algorithm Based Upon Games on Evolving Network\" by Q. Li, Z. Chen, Y. He and J-P. Jiang (in arxiv: http://arxiv.org/pdf/0812.5064v1), generalizations and similar issues.
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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.
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ERepSim presents an agent-based cloud simulation environment integrating electronic institutions from multiagent research to simulate Internet-of-Services systems.
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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.
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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.
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Animants are virtual ants. Their name comes from the combination of "ants" and "animats", an animat being "a sofware approximation of a living creature". Animants aim to simulate the behaviour of living animals.
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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..).
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A suite of machine learning benchmarks where each agent must solve a lot of different tasks without recompilation. This means that the programmers cannot manually specify topologies or adjust parameters to specific tasks.
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