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Assesses the ability of AI models to solve real-world software engineering issues sourced from GitHub repositories. The benchmark involves: Providing agents with a code repository and issue description; Challenging them to generate a patch that resolves the described problem; Evaluating the generated patch against unit tests; ML-Agent-Bench
Yes Up to 3 agent properties can be visualized in real-time using 2D graphics and color July 20, 2020 (Version 1.6.0) [1] AnyLogic: Agent-based general purpose; also supports discrete event and system dynamics simulations. The AnyLogic Company; Oakbrook Terrace, Illinois, USA Proprietary; Free Personal Learning Edition available Java
One of the earliest agent-based models in concept was Thomas Schelling's segregation model, [6] which was discussed in his paper "Dynamic Models of Segregation" in 1971. . Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate ...
[4] [5] Compared to the GitHub Copilot tool, [3] [4] the software can code, debug, plan and problem solve via machine learning techniques. [5] Devin AI works through a user prompting the software with a task in natural language, with the software responding by showing its plan while implementing the code. [3]
Fluentd was one of the data collection tools recommended by Amazon Web Services in 2013, when it was said to be similar to Apache Flume or Scribe. [10] Google Cloud Platform's BigQuery recommends Fluentd as the default real-time data-ingestion tool, and uses Google's customized version of Fluentd, called google-fluentd, as a default logging agent.
Its source code is available on GitHub, mainly under the MIT License, with some components under Photon License , Apache License v2, GPLv2, and LGPLv2.1. [2] Building Azure Linux requires the Go programming language, QEMU utilities, and RPM. [5] Starting from the release 2.0.20240301, Azure Linux was renamed from CBL-Mariner. [8]
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Agent-oriented programming (AOP) is a programming paradigm where the construction of the software is centered on the concept of software agents.In contrast to object-oriented programming which has objects (providing methods with variable parameters) at its core, AOP has externally specified agents (with interfaces and messaging capabilities) at its core.