OpenNERO Download X64 NERO (Norvig, R. (2003). Artificial Intelligence: a Modern Approach, New York: Thomson Learning) is a knowledge-based system for computer-aided software engineering. NERO consists of two main components: a knowledge base containing information about the domain-specific problem, and an inference engine that uses the knowledge base to guide problem solving. The knowledge base is built up using the author’s own project specifications (task-specific instances), ontology mappings, API code snippets, and provided solutions. The knowledge base consists of basic data structures used to represent the domain-specific problem, such as problems, solutions, and states. NERO was designed to be independent of a particular problem domain, and most of the time, it uses generic problem representations. However, when a problem domain is known to be non-standard, it is possible to create a knowledge base specialized for the problem. The inference engine is based on the Prolog program-switching engine SWI-Prolog. NERO uses a special version of Prolog called Shelly, which is integrated with the SWI-Prolog inference engine and allows the usage of logical data structures. In addition, NERO uses a set of generic programming techniques to combine Prolog and generic programming in a modular way. NERO can be considered as a generic data-flow program development environment. The NERO knowledge base consists of Prolog modules, which represent the domain-specific problem in generic terms. Each module contains rules that relate a problem statement with a corresponding solution, and a number of fact, exception, and transformation rules. The NERO inference engine uses the rule system to generate data flow, perform backtracking, and solve task-specific problems. The NERO inference engine uses a tree representation of the task for efficient problem solving. In order to solve a problem, NERO first generates the data flow tree using the generic rules of the problem domain, and then explores the tree from its root. The algorithm terminates when the goal is satisfied. A solution is considered to be correct when the data flow of the solution is consistent with the goal and the core domain knowledge, and there are no exceptions. NERO's main purpose is to provide a convenient and efficient framework for implementing an inference engine that can solve all classes of problems. NERO does not specify or limit the inference algorithms used. NERO's rule system allows for integration of any inference algorithm that is based on directed graphs. NERO installation: OpenNERO X64 1a423ce670 OpenNERO The Keymapper is a tool for managing and playing MIDI files on Linux. It is available on GitHub. See for more details. OpenUPID Description: OpenUPID is a simple Python module designed to help people keeping track of their passwords on Linux. It is available on GitHub. See for more details. OpenBUGS Description: OpenBUGS is a software system for Bayesian analysis of scientific data. See for more details. OSRM Description: OpenStreetMap is a global, free, community-maintained geographical database of maps and related location data. See for more details. OPUS Description: OPUS is a software library for real-time, streaming speech synthesis on Linux. See for more details. Otb Description: Otb is a set of tools for playing computer and video game music on Linux. See for more details. Overpass Description: The OpenStreetMap (OSM) project is a free collaborative geographic information system designed to allow everyone to contribute to, and benefit from, a freely editable map of the world. See for more details. OXNET Description: The Open and Free Audio Network Community (OXNET) is an open source, peer-to-peer network of professional sound engineers to provide free audio material, knowledge, and advice. See for more details. Papa John's Recipe Builder Description: Papa John's Recipe Builder is a simple web-based application for building personalized pizza recipes. It is available on GitHub. See for more details. PDV InfoDescription: PDV Info is a simple tool for processing and modifying physical document metadata (PIM and PMS) of OpenOffice documents. See for more details. PDFpen Description: PDFpen is a simple tool for batch converting documents to PDF format on Linux. It is available on GitHub. See What's New in the OpenNERO? System Requirements: Windows Vista Windows XP 512 MB RAM 256 MB RAM For Mac: Mac OS X 10.7 or later Minimum 512 MB RAM Screen resolution of 1024x768 We recommend a wireless network, such as WiFi Audio Specs: 1.0 Mbits/sec If your internet connection is not quite what we are using (of course it is)
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