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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.
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OpenNERO is an open source software platform for implementing interactive educational games and demos of AI methods such as brute-force search, heuristic search, scripting, reinforcement learning, and evolutionary computation, and AI problems such as maze running, vacuuming, and robotic battle. It has been used in courses at MIT and Northwestern University.
Starlab is a free open source cross-platform educational computer programming language.
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There are several open source textbooks, listed below:
Artificial Intelligence: A Modern Approach (MIT Press)
MIT Introduction to AI Coursera course.
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Introduction to Robotics (also available on The Teaching Company)
Introduction to Programming With Python (also available on The Teaching Company)
The Art of Computer Programming, Volume 1 (also available on The Teaching Company)
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What’s New in the?
OpenNERO provides a number of research tools to illustrate how to apply AI techniques in real world problems. In the course of teaching Artificial Intelligence: A Modern Approach, Russell and Norvig focus on showing how AI methods can be applied to real world problems in the following areas:
Killer Robots – this is a sample of an AI programming game that illustrates a number of AI techniques:
Brute-force search to search through an array of possible actions for the best one;
Heuristic search for finding the shortest path in a maze;
Scripting to interpret a set of rules and act accordingly;
Reinforcement Learning to give an agent a reward and learn what actions to take to maximize the reward;
Evolutionary Computation to find a set of actions that yields the best solution in a sequence of iterations
Vacuuming – this is a sample of an AI programming game that illustrates an AI problem of finding a path that eliminates as much debris as possible.
Robotic Battle – this is a sample of an AI programming game that illustrates an AI problem of defeating another robot in battle.
The OpenNERO programming environment was initially developed at UC San Diego as a research tool for teaching AI to undergraduate and graduate students at UCSD. OpenNERO was first published in the spring of 2006 as a set of demos and exercises from AI: A Modern Approach.
In 2014, OpenNERO was upgraded with new features and capabilities to accommodate teaching and research in AI.
New Features and Changes:
Updated to Python 3.x
Updated to OpenCV 2.3.2
OpenCV is a library of computer vision algorithms
New Educational Functions
OpenNERO Demo Pages
OpenNERO Exercise Pages
OpenNERO Educational Tools
OpenNERO Educational Examples:
These pages feature interactive tools that illustrate techniques and problems from AI: A Modern Approach.
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