Large-scale concurrent computing in artificial intelligence research
C3P Proceedings of the third conference on Hypercube concurrent computers and applications - Volume 2
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evaluating intelligent tutoring with gaming-simulations
WSC '95 Proceedings of the 27th conference on Winter simulation
The user interface as an agent environment
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Agent technology in computer science and engineering curriculum
Proceedings of the 5th annual SIGCSE/SIGCUE ITiCSEconference on Innovation and technology in computer science education
An intelligent distributed environment for active learning
Journal on Educational Resources in Computing (JERIC)
An agent architecture for long-term robustness
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Give students a clue: a course-project for undergraduate artificial intelligence
Proceedings of the 38th SIGCSE technical symposium on Computer science education
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It is not uncommon to teach Artificial Intelligence (AI) by asking students to implement agents that embody intelligent behavior. This helps students gain a fuller understanding of the many concepts taught in the course. There are two issues with this approach that deserve attention. First, students come into an AI course knowing how to program in different languages and having different levels of programming ability. Second, it's useful for the students to have a single task environment for all of the agents they program. A solution to both issues lies in a distributed system where the agents are clients communicating with a server that handles a configurable task environment. This allows the students to program their agents in any language and on any platform they desire, so long as they can communicate with the task environment server. If the task environment can be configured to provide additional levels of complexity and difficulty, this allows students to program at a level they are comfortable with. They can then challenge themselves by incorporating more advanced capabilities into their agents. This paper presents just such a distributed and configurable task environment that was developed for an undergraduate AI course.