Computer Science

3D and AI

Standard AI test-beds have often been successfully used in the past to promote research, as they facilitate controlled empirical experimentation, comparative evaluations, and quantitative measurements. Problems like chess, and test environments like Phoenix and RoboCup, have resulted in significant improvements to the sciences of artificial intelligence and multiagent systems. Indeed, the usefulness of having a complex, dynamic multi-agent environment as a research infrastructure has been pointed out explicitly.

Some of the earlier work on MAS infrastructures lead to ModSAF, a system for military training based on distributed simulations using computer generated military forces. The software agents, in addition to human participants, made up these forces (fixed or rotary wing pilots, tank drivers, etc.) and had to act in a coordinated fashion that involved team play, mission planning, and reactive behavior.

However, most complex, dynamic, multi-agent research environments require considerable efforts to build and maintain, and therefore, there is a general scarcity of such infrastructures available for research use. Most existing infrastructures are designed to support specific tasks under a single environment, and rarely support human testing and comparison.
see My article

[see American Association for Artificial Intelligence]
[see Italian Association for Artificial Intelligence]
[see Catalan Association for Artificial Intelligence]
[see Artificial Intelligence Association of Ireland]
[see Society for Informatics; Section AI]

[see RoboCup]
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